Measuring "engagement" seems to be an even longer quest for Marketers and Analysts. There was so much we could measure and so little. As Marketers we have been frustrated with the near constant 2% conversion rates for our websites. We would like to have another metric that justifies our existence, and of course that of our website.

And that's just when it comes to e-commerce websites.

The fervor for measuring engagement is even higher for non-ecommerce websites because there is little in terms of Outcomes to measure there.

So there has been a lot of proverbial ink used up in defining "engagement". Pundits have pontificated. Bloggers have blogged. Guru's have spoken from their perches. Industry Analysts have given their brains to the cause. Vendors have…. well tried. Hard.

Yet not much to show for all this collective effort.

Engagement, that phrase / name, is not a metric that anyone understands and even when used it rarely drives the action / improvement on the website.

Why?

Because it is not really a metric, it is an excuse.

Even as creating engaging experiences on the web is mandatory, the metric called Engagement is simply an excuse for an unwillingness to sit down and identify why a site exists. An excuse for a unwillingness to identify real metrics that measure if your web presence is productive. An excuse for taking a short cut with clickstream data rather than apply a true Web Analytics 2.0 approach to measure success.

Does that sound a tad bit tough-lovish?

The desire to measure "engagement" with customers is a good one. But let's try to understand why in the context of web analytics so many efforts at measuring "engagement" have yielded almost no results:

1. Each business is unique and each website is trying to accomplish something unique. Think of all the reasons a website exists, now imagine what engagement could be for each.
   

   
   Result: It is really hard to generalize, and often turns out to be a comparison of apples to monkeys to whales. That translates into a poor understand of what is being measured.

2. It is nearly impossible to define engagement in a standard way that can be applied across the board. Definitions that exist are either too broad (to cover every nuance) or too narrow (hence very unique).
   

   
   Result: Few people understand what you mean when you say "engagement", and even fewer can then translate it to apply to their sites. Unlike clicks, visits, conversions, recency, ip addresses etc when you tell your management "engagement" it is hard to know what it is/means.

3. At the heart of it engagement tries to measure something deeply qualitative.
   Yet most efforts to measure it in our world tend to be hard core quantitative (translate that as: "we have clickstream, let's take our interpretations of what could possibly be happening, now find clicks that can carry the burden of our personal impositions, voila! here's engagement").
   

   
   Result: That mismatch is ok for a couple months, but as you measure it over time you'll discover that it does not indicate true customer intent and hence is doomed to have sub optimal impact.

4. One of my personal golden rules is that a metric should be instantly useful. This one is not. Say you measure engagement. It could be a % or a absolute number or a ratio or whatever (in fact it can be any or all of those at the same time). You fire off a graph or a excel spreadsheet with trends. You repeatedly get asked: What are we measuring?
   

   
   Result: Little action. It is not most important but we should always try to have metrics that are instantly useful, you look at 'em and you know what it is and if going up is good or bad. It is rare to find a measure of true customer engagement for a website that does not required a partial PhD to understand what is being measured.

5. Most of all engagement is a proxy for measuring an outcome from a website. Conversion is not enough, as mentioned above, so we try something else. The problem that we'll define engagement as a measure of some kind of outcome but we won't give it the sexy name of engagement.
   

   
   Result: Confusion and delay (tip of the hat to Thomas The Tank Engine). If we are measuring page views divided by unique visitors as a proxy of engagement (more pages per visitor means more "engagement") they why not call that metric page view per visitor? Atleast that will make it clear what you are measuring and then some smart person will question that it is not a very good definition!

In Summary: The reason engagement has not caught on like wild fire (except in white papers and analyst reports and pundit posts) is that it is a "heart" metric we are trying to measure with "head" data, and engagement is such a utterly unique feeling for each website that it will almost always have a unique definition for each and every website.

"So what you are saying is that we should not measure engagement."

I am saying you should very very carefully consider the above points, then not take a short cut (or as the American's say, a cop out) and actually define the metric as a Outcome metric (see element three of the trinity ).

Here is a process you can follow:

Simple easy to follow process that should help identify the critical metrics for your business and force your business leaders / stakeholders to help identify the real success metric that otherwise might have been hidden behind "engagement". And now it will be actionable across your organization becuase people will understand exactly what it is.

To stimulate your thought process here are some metrics you can use to measure "customer engagement" (that visitors are engaging with your website):

• "Are you engaged with us?"
  (exact phrasing of a site level survey question – let your customers interpret it as they will, after all why is your interpretation better then theirs)
  

• Likelihood to recommend website
  (another site level survey question – would you recommend our website to your friends / family members / lovers :))
  

• Use primary market research
  (similar to the first one, but in this case use good old market research to get a feel for how engaging your website is – and measure it every three months to compute the trend)
  

• Customer retention over time
  (on a ecommerce or non-ecommerce website, do people come back and how often – here's a helpful post on how to measure it)
  

• # of Visits per Unique Visits, Recency of Unique Visitors
  (recommended as a last resort – I am really not in favor of using quantitative metrics to measure qualitative outcomes – but you can use these to see if your website is "engaging" enough to pull people back and more frequently)

I am sure you'll have other metrics that you can think of in the spirit of the ones above. The above list is to share with you how I think about it.

We all want to engage with our customers. But as analytics practitioner our goal is to use the right metric by working hard to get to the root cause (rather than making a excuse) and sharing that with clarity with our decision makers. Then and only then will it be actionable.

In Summary :

• When most people measure "engagement" they have not done due diligence to identify what success means for their online presence. In absence of that hard work they fall into measuring engagement, and then measure something that is hard to action or something that will rarely improve the bottomline. Avoid this at all costs.
  

• Think very carefully about what you are measuring if you do measure engagement. If engagement to you is repeat visitors by visitors then call it Visit Frequency, don't call it engagement. Don't sexify, simplify! :)

• If you want to measure "engagement" then think of new and more interesting ways to measure that (see list above). Engagement at its core a qualitative feeling. It really hard to measure via pure clickstream (web analytics data). Think different.

Ok now its your turn. Please share your perspectives, critique, additions and subtractions via comments. Thank you in advance.

[Like this post? For more posts like this please click here, if it might be of interest please check out my book: Web Analytics: An Hour A Day.]

"Engagement" Is Not A Metric, It's An Excuse is a post from: Occam's Razor by Avinash Kaushik

Often though, atleast online, our hopes are dashed, efforts expended rarely have adequate ROI, the hype is followed with a bucket of cold water.

It is not that measuring if "Visitors" / "Customers" itself is a ignoble goal. It is more that our execution efforts in measuring engagement are fatally flawed. So much so that recently I was compelled to pen a warning post: “Engagement” Is Not A Metric, It’s An Excuse.

That post outlined the core issues faced in the quest of measuring Online Engagement. It also outlined a four step process you should follow when it comes to trying to measure engagement.

One of my central thoughts was that it is nearly difficult, if not near impossible, to measure Engagement, as many others have passionately recommended, based only on your quantitative data (in our case Web Analytics clickstream data).

So it was with great delight that I read Theo's email that helped me understand exactly why I felt that way! :)

When Theo proposed posting it here on Occam's Razor I quickly agreed.

Theo Papadakis is a Marketing Executive at cScape in London. Our paths first crossed when he invited me to contribute a thought piece to accompany the 2nd Annual Customer Engagement Survey Report (and to his credit he did so after having read my post above!). You can download the report, there are nice graphs and some interesting data and page twenty is quite interesting. :)

Here's Theo. . . . . .

__________________________________________________

Before we look at what aspects of a customer's online engagement Web Analytics can capture we need to clarify the meaning of the concept of customer engagement.

Definition of Customer Engagement (Engagement Index)

'Engagement' is a word with many meanings (vow, betrothal, involvement etc). For marketing purposes they can be boiled down to a single concept: one-way relation. If x is engaged with y, x is related to y.

The concept of customer engagement only deals with a particular kind of one-way relationship:

• Subject of engagement: The subject of engagement should not be limited to customers.

  
  Although 'visitor engagement' is better in that it takes into account non-customer visitors to your website/store, its focus on measuring people's engagement with your brand on your own premises is too restrictive.

  It is important to measure the engagement of customers, prospective customers and detractors with our brand, in every space they engage with it in.

• Object of engagement:

  The subject's relationship with a brand / company / product / consumption topic.

Now that we have defined what kind of relationships customer engagement deals with let's look at the criteria with which we can refine and classify the ways in which customers engage:

• Kind: Customers can be positively or negatively engaged with a company/product.

  A more in-depth examination of kind would reveal its content, usually a mixture of emotional states and rational beliefs, such as, in the case of positive engagement, sympathy, trust, pride, etc

• Degree: The degree of positive or negative engagement lies on a continuum that ranges from low involvement, namely, the psychological state of apathy, to high.

  An engaged person is someone with an above average involvement with his or her object of relatedness.

With the context setting out of the way. . . . .

What aspect of customer engagement can web analytics capture?

Having defined customer engagement we are better able to delimit what web analytics can and cannot tell us about the engagement of our website's visitors.

Let's look at some of the widely used web analytics metrics and understand what aspect of engagement they capture.

Unique Visits: Shows how many people decided to engage with you for the first time by visiting your website.

Frequency of Visit: Frequency must be contextualised within a specific time frame.

A customer who has engaged 10 times with the company in the past 10 years has a lower degree of engagement for example in relation to a customer who has also engaged 10 times in the company in the last 2 months.

Contextualised 'frequency' can therefore help us to identify the relative degree of our customers' engagement.

Recency of Visit: This metric speaks of the recency of our customers' last engagement.

Jim Novo has proven that it correlates well with degree of engagement. A customer whose last engagement with a brand is more recent than that of another is also likely to be more engaged.

Like frequency it therefore frames our customers' degree of engagement only relatively.

Depth of Visit: This tells us how many pages long our visitors' journeys through the site were.

Although a deep journey signifies a high degree of engagement this metric again does not distinguish between the kind of engagement.

Do your visitors passionately disagree with what you are writing about? Are they simply unable to find what they are looking for?

In both of these cases a high degree of engagement may be of a negative kind.

Time Spent on Site: Same story as with depth. Time spent correlates with degree of engagement but as it does not discriminate between kind it may simply be negatively spent desperately trying to find the content your visitor is after.

Similarly, most online metrics are only able to capture degree not kind of engagement:

• Subscribing (feed, email, newsletter)

• Registering

• Feedback (comments, complaints, inquiries etc)

• Rating\tagging\filtering\bookmarking its content

• User submissions (UGC)

• Printing or downloading a piece of content

• Brand index

Degree of Engagement

What comes out of the above discussion is that

This is not to say that we cannot make inferences and state hypotheses about the kind/content of engagement, based on what we can measure (degree of engagement), nor that these hypotheses are unlikely to be correct.

It is only to say that using web analytics it is impossible to make or support such inferences.

Such inferences, about the kind of engagement, must necessarily be informed by considerations that lie entirely outside the field of web analytics.

Before we begin making inferences on the basis of degree of engagement however let's discuss this metric a bit more.

Following a number of leading Web Analysts I also believe that a customer's degree of engagement is better calculated as a synthetic metric composed of several basic metrics, rather than as a one-metric solution e.g. measuring customer engagement by means of 'duration of visit' only. This requires an argument unto itself that will not be pursued here.

The score each of these component metrics takes however only makes sense if contextualised. Example: a frequent and recent visitor is 'more engaged than' someone who is not, but is he engaged? If yes how engaged is he? There is little we can do with relative statements such as this.

In order to make such statements meaningful and operational we need to contextualize the component metrics that constitute a customer's degree of engagement on a high/low continuum, beginning with apathy and proceeding with progressively higher degrees of engagement.

This means that both the lowest (apathy) and the highest degree of engagement need to be defined. The easiest way to do this is to define the average degree of engagement (the average score for several metrics of your choice across your site or based on a competitor-specific or industry-wide benchmark), considering everything that falls short of it as (increasing degrees of) apathy and everything beyond it as (increasing degrees of) engagement. In this way a customer's degree of engagement assumes a non-relative meaning (it remains of course relative to your website's, competitor's or industry's historical performance).

By inserting relative statements such as 'x is more engaged than y if and only if x does z and y does >z' into a continuum that is based on website \ competitor \ industry benchmarks, it is possible to provide a reference point which although relative in itself (historical performance) is sufficiently stable and pertinent to business performance, to provide with useful insights into visitor behaviour and business\campaign success. (Substitute z with any or an aggregate of the visit metrics score(s)).

Conclusions

• No web metric, or combination of metrics, can discriminate between kind of engagement i.e. positive engagement. This requires primary research.

• All web metrics can do is discriminate between relative degrees of engagement.

• Basic metrics can only discriminate between low degrees of engagement.

• A customer with a high score in his visit metrics may nevertheless feel apathetic towards the brand.

__________________________________________________

That was interesting, was it not? You can now see why I found Theo's article to be so enlightening.

I found the nuance of the kind of engagement and the degree of engagement to be particularly insightful.

The next time I take quantitative data (even if I mash it into a formula of five different metrics and call it website engagement index!) to my C-level executives I will first state what my engagement index measures is Degree of Engagement.

My hope is this will result in a clear understanding of the limits of what the data is saying.

That then I hope will lead to questions about measuring the Kind on Engagement (and to exploring qualitative measures).

No false promises made from the data. Progress made by understanding its limits and exposing them. That's awesomeness. IMHO.

My heartfelt thanks to Theo for sharing this article with me, and now with all of us.

It's your turn now.

Agree? Disagree? What's your experience? Please share your perspectives, critique, bouquets and brickbats via comments.

PS:
Couple other related posts:

• “Engagement” Is Not A Metric, It’s An Excuse
• Overview & Importance of Qualitative Metrics
• Multiplicity: Succeed Awesomely At Web Analytics 2.0!

Measuring Online Engagement: What Role Does Web Analytics Play? is a post from: Occam's Razor by Avinash Kaushik

Visiting a website, you leave behind a significant amount of data, whether you buy something or not. The website knows every "aisle" you walked down, everything you touched and everything you put in your cart and then discarded. If you buy, the site manager knows where you live, where you came to the website from, which promotion you are responding to, how many times you have bought before and so on. If you simply visited and left the website, it still knows everything you did and in the exact order you did it in.

Add to this the fact that now there is a massive proliferation of tools that will instantly create reports presenting data in every conceivable slice, graph, table, pivot or dump that you can imagine the challenge.

But, no matter what tool you use, the best that all this data will help you understand is What happened. It cannot, no matter how much you torture the data, tell you Why something happened. This is the reason qualitative data is so hyper important. It is the difference between 99% of the website analysis that happens that yields very little insights and the 1% that provides a window into the mind of a customer.

Combining the What (quantitative) with the Why (qualitative) will provide a company with a long term strategic competitive advantage.

There are many types of qualitative data at your disposal including brand buzz, customer satisfaction, net promoter indices, visitor engagement, stickiness, blog-pulse, etc.

While there are many options for qualitative analysis, perhaps the most important qualitative data point is how Customers/Visitors interact with your “web presence.� Visitor interaction can lead to actionable insights faster while having a richer impact on your decision making. There is a lot of "buzz" around "buzzy" metrics such as brand value / brand impact, blog-pulse, to name a couple. IMHO these "buzzy" metrics might be a sub optimal use of time/resources if we don't first have a hard core understanding of customer satisfaction and task completion on our websites.

There are many different methodologies to collect Customer qualitative data, including:

• Lab Usability Testing (inviting participants to complete tasks, guided or unguided)
• Follow Me Homes (observing in a customer's "native" environment)
• Experimentation/Testing (the latest new and cool thing to do, a/b or multivariate)
• Surveying (the grand daddy of them all)

If you are new to this world the last one is a great way to step into this new world and unlike what you might have heard it is both easy to implement, can be a continuous methodology, highly quantitative and is most often chock full of insights that will lend themselves to be very action oriented.

In future posts I hope to dive deeper into options, best practices and examples.

Agree? Disagree? Please share your feedback via comments.

Overview & Importance of Qualitative Metrics is a post from: Occam's Razor by Avinash Kaushik

It was a real Doh (!) moment.

Turns out we have not rfid'ed every visitor and they don't rub their head against their monitor before starting their session on my website (and of course another head rub when they decide they have had enough and exit). This would allow us to capture the time stamps accurately and have exact measures.

What a disappointment! Joking, just joking!! :)

I find that few people understand correctly how the Average Time on Site calculation is made.

That's regardless of source: weather they use the religious truth from a Competitive intelligence tool or from their website web analytics solution. For the latter it does not matter what data capture methodology you use on your site, WebLogs or JavaScript Tags.

This post is my humble attempt at explaining how Time on Page and Time on Site are computed.

In order to make life easy I am going to assume the following session happening on a website:

Someone requests your home page, your web analytics tool starts a session for the visitor, two more pages are requested before the visitor decides to leave your website (close browser, type in a url of a different site, click on a link on your site to go to different site….).

What we want to compute is…..

Tp = Time spent on a page.
Ts = Time spend on the website.

Someone visits your website at 10:00……

There is a entry in your log file (weblog or javascript tag, does not matter) that says, in English, "someone has requested the website homepage file at 10:00".

[
It actually looks more like this....

111.111.111.111 - - [08/Oct/2007:11:17:55 -0400] "GET / index.html HTTP/1.1" 200 10801 "http://www.google.com/search?q=avinash+kaushik&ie=utf-8&oe=utf-8 &aq=t&rls=org.mozilla:en-US:official&client=firefox-a" "Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.8.1.7) Gecko/20070914 Firefox/2.0.0.7"

Notice the time stamp there?

Why spoil the fun with technical stuff! But if you want more the tech stuff is explained very nicely here: Log file sample explained.
]

So far all your analytics program knows is when a page was requested, hence:

Tp = N/A [Not Available]
Ts = N/A

Next more fun happens on your site: someone clicks on a link to Page 2 from your home page. Hurray no bounce! :)

Now there is a new entry in your log file that says: "The same Visitor requested page two at 10:01"

Finally your web analytics program can compute some time metrics!

It knows how long the Visitor spent on the home page. It subtracts 10:01 from 10:00 and gets one minute. Hence:

Tp (Home Page) = 1 minute.

Notice something important: the only way your program knows how long someone spent on one page is by looking at the two time stamps. One from the request for the first page and one from request of second page.

Next the blinking "get $200 rebate on a $210 product" link on Page 2 entices the person to click on to Page 3 to buy the product. More sweet success! "Engagement!!"

The magical math outlined above happens (10:05 minus 10:01) and for Page 2:

Tp (Page 2) = 4 minutes.

The Visitor reaches Page 3 and notices that the rebate offer only applies to people who live in Antarctica who can show they currently own refrigerators (!!). As you can reasonably imagine this happens on the site on Page 3…..

Exit!

How long did it take to find and read the rebate fine print? You could reasonably guess if you knew how long the Visitor spent on Page 3.

The problem is that your logfile is missing one time stamp to do the magic math.

Tp (Page 3) = Time of page request (10:05) minus Time of next page request (N/A).

Hence:

Tp (Page 3) = 0 minutes (Since N/A!)

The program has no idea how long the Visitor spent on the last page on your site.

This is true for pretty much all web analytics programs in terms of default behavior.

Let's wrap this puppy up….

Tp (Home Page) = 1 minute.
Tp (Page 2) = 4 minutes.
Tp (Page 3) = 0 minutes.

Ts = 5 minutes. (Time on Site, also known as Session Length)

Makes sense?

I mean it probably does not make sense that you don't know how long Visitors spend on the last page in the session, but the explanation of how it works makes sense?

The Case of Bounce / Single Page Sessions:

Now I am sure your are asking yourself: "I wonder what happens in cases of sessions where there is only one page?"

Good question. This is what you are wondering about….

Bounce!

This is what is computed….

Tp = 0 minutes.
Ts = 0 minutes.

For bounced sessions (single page view sessions) your web metrics program can't compute how long people have spent on the page or on your website. It certainly records the page request (10:00) and it records start of the session but it does not know how long someone was there.

In case the Visitor came at 10:00 and leaves the browser open and rushes as the Visitors Wife / Husband / Boss yells at them to do the dishes and cleaning the dishes takes and hour…. the session is terminated at the end of 29 mins (default setting in most session based web analytics tools). It is important to note that the web metrics program will still say:

Tp = 0 minutes.
Ts = 0 minutes.

The Case of Tabbed Browsing:

Firefox gets credit for popularizing tabbed browsing – I love it and I have no idea how we survived for years without it! The latest versions of IE also support tab browsing, so the masses are also now using this delightful feature.

But what happens to the Time on Page and Time on Site computations when people open a link on a site in another tab and browse the site via two tabs at the same time? I do this all the time! :)

It messes up the time computations.

Here is a common scenario that we'll use to understand the impact…..

A Visitor comes to your home page. From there opens the first link in a new tab, but continues to scan the home page. The clicks onto a link to Page 2 from the home page, then onto Page 3 and then closes the tab (or moves away and forgets about it).

The Visitor goes to the tab opened from the home page to Page 4 of your site, spends time there, goes on to Page 5 in that tab. Then exits.

How is time on site computed? There are two ways in which I have seen web analytics tools report on this customer behavior.

Dealing with tab browsing # 1:

The web analytics tool takes the above picture literally and this happens…..

Outcomes: Two Sessions, one for each tab in the browser.

Session One (top): [referrer -> Google]

      Tp (Home Page) = 2 minutes
      Tp (Page 2) = 3 minutes
      Tp (Page 3) = 0 minutes

      Ts (session duration) = 5 minutes

Session Two (bottom): [referrer -> your site/homepage]

      Tp (Page 4) = 6 minutes
      Tp (Page 5) = 0 minutes

      Ts (session duration) = 6 minutes

Net net: 2 Visits. 1 Unique Visitor. Also notice the impact on referrers (for the second one you'll see your site referring to itself).

Really interesting outcome!

Dealing with tab browsing # 2:

Some web analytics tools "collect" all the "hits" (entries in the log files) and they will "linearize" the hits and construct one session from all the tabbed browsing Visitor behavior.

So keeping our use case exactly as above, visually this is what happens when data is processed……

[Pretty high resolution image: Click: Time on site impact tabbed browsing -linearized]

Outcomes: One Sessions, visit “reorganized” by time stamps.

Session One: [referrer -> Google]

      Tp (Home Page) = 1 minute
      Tp (Page 4) = 1 minute
      Tp (Page 2) = 3 minutes
      Tp (Page 3) = 2 minutes
      Tp (Page 5) = 0 minutes

      Ts (session duration) = 7 minutes

Net net: 1 Visit. 1 Unique Visitor.

Neither one perfectly captures exactly what the Visitor is actually doing on your site.

Which one do you prefer?

Be sure to ask your web analytics vendor which of the above two (or a different method) do they use to deal with tabbed browsing when it comes to computing time one site.

Given the increasing popularity of tabbed browsing the impact on your numbers could be big.

Google Analytics uses the second method ("linearization").

Take a deep breath now!

Extra Credit Section:

As always there are hacks that might allow you to capture the time spent on the last page (or even on the last event if you are using event logging).

One of the most common (and when I say most common I mean used by the 0.001%) is to add extra script / code that would capture the fact that the page was "unloaded" in the browser. Technically it is often called "onbeforeunload event".

In this case for each page you have a page request time stamp as well as a page unload time stamp. Hence you can do clean magic math required.

You will have to script this yourself or ask your vendor to help you create such code. You would then also have to ask your Vendor to modify how time on page (and time on site) are computed in the web analytics tool to take advantage of the additional time stamp.

If you are doing your own logfile parsing then you can more easily modify the log file to parse and data and then compute the metric for you.

There are other hacks as well.

Some people have also ventured that if your analytics tool has outbound link tracking (also often called exit tracking) then the web analytics tool has the time stamp of that click and can use it to compute time spent on the last page.

I am not a big fan of this because most people on your site will not exit the site by clicking on a link on your site (because most links on your site don't link to other sites, how inconvenient!). So if you use the outbound/exit link time stamps you'll end up computing Time on Site / Page for some Visitors to your site one way and everyone else another way.

It muddies waters, it becomes mixing apples with watermelons.

I am a fan of consistency, even if you consistently measure something imprecisely.

I hope that this post has helped you understand how time on website and time on a page is computed. Next time you see Average Time on Site or Average Time on Page you'll know what data is being included and what is not.

Also next time you compare numbers between a external tool, one of the competitive intelligence tools, and those reported by your internal web analytics tool, you'll know the questions to ask before you jump to conclusions!

Closing Thought : The purpose of this post is not to imply, overtly or covertly, that Time on Site is not a good metric. Far from it. For many types of businesses it can be a critical metric. My hope here is to educate you about how it is computed so that you can make more informed decisions. Computation of no web analytics metric is without flaws (look no further than Unique Visitors!!), time on site is perhaps one of the less flawed ones. :)

Ok now its your turn.

Please share your perspectives, critique, bouquets and brickbats via comments. For the Analytics Gods out there, did I get it right? Please comment.

[Like this post? For more posts like this please click here, if it might be of interest please check out my book: Web Analytics: An Hour A Day.]

PS:
Igor was upset that I was not doing a good job of cross posting. Since customer feedback is important… here are earlier posts in this series:

• Standard Metrics Revisited: #1: Visitors
• Standard Metrics Revisited: #2: Top Exit Pages
• Standard Metrics Revisited: #3: Bounce Rate

And here is a related post that outlines four attributes each great metric should possess:

• Web Metrics Demystified

Standard Metrics Revisited: #4 : Time on Page & Time on Site is a post from: Occam's Razor by Avinash Kaushik

Sadly execution and analysis of these new social media channels has been hobbled by old world thinking. When it comes to marketing because of the old world thinking from the worlds of sTelevision and Magazines, and when it comes to measurement because of the world of traditional web analytics.

These new channels, Twitter and Facebook and YouTube and Tumblr and, yes, even blogs, are very distinct customer / participant experiences. Stale marketing or measurement thinking applied to them results in terribly sub optimal results for all involved.

So in this post my hope is to share with you what is unique about measuring one such channel, Twitter. The blog post is also sprinkled with my own words of folksy wisdom as to how you should use the channel for maximum impact.

My new book Web Analytics 2.0 covers social media measurement, but I am going to cover something very different in this post.

First: An Ode to New Thinking:

One common thing between the all tools in this post is that they were built by "outsiders".

One of the things I love and adore about Twitter (besides all that connection and conversation) is how its open API has lit a fierce fire of innovation when it comes to analytics. Anyone and their brother and ma-in-law can develop a tool, and they have! Much to the benefit of the rest of us.

Perhaps the most beneficial thing to me is how much out of the box innovation this has brought.

For example just look at traditional web analytics tools, there is absolutely no fresh thinking when it comes to Social Media Measurement. Their constant focus is on "let's figure out how to collect and report ever more data and not bother with a truly immersive understanding of these channels and what makes them unique". That mental model is, sadly, extremely clear in the metrics and analysis they provide with "twitter integrations".

While there is some stale thinking in the new twitter tools, most of them have a lot of fresh thinking from people untainted by Omniture or CoreMetrics or WebTrends or, ok ok ok, Google Analytics.

I consider this massive proliferation of new thinking to be a gift from God.

To all of you developers who are toiling out there, you have my love and gratitude.

In this post four twitter analysis tools that while not yet fully developed show sweet signs of:

1. Truly understanding the medium and uniqueness of the channel and

2. Are not just reporting "hits", rather coming up with clever metrics.

Quantitative Metrics / Analyses.

Most twitter analytics tools just do data puking. They find numbers that can be computed and then proceed to puke at you as many as they can find, with wonton disregard of value being provided or outcomes being measured.

Here is one of the mild ones:

You must pause and think: So what is this saying? What action can I take?

Always, always, always ask that question when faced with tools that simply puke data out at you (twitter or Google Analytics or whatever).

But as I mentioned at the start of the post one of the beauty of twitter's open API is that there are a few pockets of truly innovative thinking.

Here are some that I humbly believe look promising. . . .

Klout. Twitter Analytics.

Klout is a wonderful little tool that measures Klout Score, a proxy for "influence":

It is easy to understand the market demand to boil things down to one number, but this is perhaps the least useful thing in Klout.

While on the surface they might seem useful, I am always suspicious of compound metrics. They can be subjective, inapplicable to many and efficiently hide the insights you need to understand what actions to take. [See more here for Compound Metrics: Four Not Useful KPI Measurement Techniques]

Mercifully there is so much more to Klout than that.

Klout measures a bunch of lovely metrics, specifically applicable to Twitter, that are grouped into four buckets: Reach, Demand, Engagement (!!) :), Velocity.

There are two lovely things about these computations.

1. Joe and team have wonderfully avoided the temptation make these compound metrics (as in Reach = Followers / Total Retweets * Friends + Pixie Dust). The factors used are laid out as individual metrics making it easy for you understand the data and pick metrics that work for you.

2. (My favorite) The metric definitions are not "crap". This seems like such a low bar to meet, sadly far too often metrics out there (not just for twitter) are just plain shoddy.

For example here are some clean definitions from Klout:

# Engagement

* How diverse is the group that @ messages you?
* Are you broadcasting or participating in conversation?

# Velocity

* How likely are you to be retweeted?
* Do a lot of people retweet you or is it always the same few followers?

# Reach

* Are your tweets interesting and informative enough to build an audience?
* How far has your content been spread across Twitter?
* Are people adding you to lists and are those lists being followed?

When I use Klout I simply pick the metrics that are most important to my own twitter strategy.

I would suggest that this is very very very very important, pick what is right for you rather then following a lemmings like strategy of "I am going to use metrics Y & Z that someone recommends".

Here's an example: I don't care about Follower/Follow Ratio. I think it is disingenuous to follow everyone who follows you just for appearances sake when you have no intention of reading what they all say. Why be fake?

As you might have read in the new book I like "Message Amplification" in Social Media, and hence I do care a lot about Total Retweets.

[Sidebar: my favorite twitter metric is: # Of Retweets Per Thousand Followers, it's a measure of efficiency and value provided and people voting with their clicks, all rolled into one!]

I care a lot about Follower Retweet % ("Do a lot of people retweet you or is it always the same few followers?") because I want to appeal to more people than my mom, dad, and best friend!

One of the biggest mistake companies and brands make about Twitter is that they think it is one more "shout channel" like TV and Radio and Magazine ads or Press Releases. Twitter is not that. Twitter is a "conversation channel", a place where you can find the audience relevant to you (and your company and products and services and jihad) and engage in a conversation with them. It is not pitching, it is enriching the value of the ecosystem by participating.

Hence I like the metric Messages Per Outbound Message , as a primitive measure of the fact that you are participating in a conversation and not just yelling.

With Klout I can choose the metrics that best reflect my personal twitter strategy, I can easily find them and I can monitor my progress (using a handy dandy graph) and ensure my strategy is a success.

Your strategy might be different. Walk up to the buffet and pick the metrics that will help you best measure your own success.

[ Contest: Notice the metrics I have deliberately ignored: # of followers, # of retweets, @ mention count etc. Can you guess why? :) The person with the best guess gets a copy of Web Analytics 2.0! Contest closed, thanks for the entries!]

At the bottom of the Stats tab Klout also includes a handy dandy Analysis table with trend indicators. . . .

As an Analyst it might be of some value to look at the trend pointers at the bottom (clearly I am doomed!), it might be cute to put this into a PowerPoint slide for the HiPPO's who might like the Chinese fortune cookie messages for each metric group.

Ok, ok, I am just teasing the Klout team, I know it is very hard to "wordify" and programmatically make valuable recommendations. :)

GraphEdge. Twitter Analytics.

The reason I believe GraphEdge is interesting is that it has a set of really cute metrics that help bring a different perspective to measuring Twitter.

If want to contrast the difference in thinking applied compare some of the metrics below with, for example, what Omniture is touting with its Twitter "integration". The difference between the old web analytics thinking and a new person's could not be more clear.

[Allow me to rush and add that while Omniture has a hack to bring some twitter data into Site Catalyst to do something, Google Analytics has nothing. Not even something that is not useful. So perhaps GA stinks even more.]

Here are some, IMHO, differentiated metrics. . . .

Legitimate Followers:

If you have spent any time on Twitter you know that spam accounts are a problem so it is very nice that the first thing you see in GraphEdge is not a follower graph but rather their attempt at telling you how many legitimate followers you have (and the trend over time, cropped out in the image below). . .

To identify "legitimate" they use the following filters, direct quote:

Any of your followers who are following more than 2,000 people are considered not-Legitimate… they’re probably not really monitoring your feed, so we don't count them as "Legitimate".

Users who have been suspended by Twitter can’t read your tweets (and probably weren’t interested in the first place!). We don’t consider these Legitimate Followers.

It is ok to argue with their filters, but it's a fine start and I think good enough.

Klout also measures something called "Reach", which is also their way of identify if you've got people or bots following you.

Churn Rate:

In my days at DirecTV one of the metrics that the company was obsessed with atleast then and rightly so, was Churn Rate. It reflected the value of not just going after new customers but doing all that was possible to take care and love the customers we already had. Makes sense?

So I have always had that obsession with tracking Churn, simply to try and understand why people quit. The hope is if I can understand why then I can do something to fix the problem.

[By now I am sure you get the feeling that I am treating twitter analysis like I would business analysis. Twitter is my brand channel and I take this very seriously. It is perfectly ok to use Twitter to tell people where you are and what you are doing and not care about analysis.]

I have not really found any decent tool to track unfollows in twitter (yes I have tried the normal ones and they are either flaky or just outright stink). Hence I was happy to have this in GraphEdge. . . .

Yea!

Actually ouch! 291 un-follows!! So sad.

Atleast now I know.

It is nice to have the over all trends on the right in the above image, as well as for the period you choose smarter metrics like growth rate.

GraphEdge will also show a list of your new followers and un-follows (so you can send them bad vibes! Kidding, Kidding. :).

[Qwitter was one of the first tools I used to track unfollows, sadly it does not work any more, and it had a great feature: It would try to guess and report on which tweet resulted in the un-follows. Nice.]

And here's what we were on the quest of. . .

I will admit to not being charmed by having three different lines above, they clutter the left and the right, and get in the way of understanding the data. But you I suppose you can learn to ignore two of them.

Here's the definition from GraphEdge for Churn:

The number of removals (un-follows) over the average size of the existing base (followers) during the period measured:

Drops / (Current Followers – ((Adds – Drops) / 2))

As always look at the trends, the longer term the better. And remember that history is littered with companies that were growing just fine but they still died a painful death because of Churn Rate.

Loyalty:

Slightly along the same lines GraphEdge has a metric called Loyalty. At the moment I think it is too limited in what it actually measures, and it only starts measuring once you join GraphEdge. But there is kernel of promise in the metric, keep an eye on it.

Second Level Network Size:

Lastly… while most people overestimate their "twitter power" (I can bring you down with a single bad tweet Avinash!) I think a few people also underestimate their reach, if they participate in twitter in the right way.

Looking at the second level report can give you a feel for your network size.

Followers' Friends is an "incestuous" number, it shows all your followers and the people they are following. If you have ten followers and they all follow each other that's 100 Followers' Friends. Feel free to be proud of this number, but then promptly ignore it.

Unique Names is are the unique twitter account id's in the network, less the "illegitimate" ones. Think of this as something close to, but not the same as, the Unique Visitor concept in web analytics.

This is a useful number.

Think of it this way: If you say something of incredibly profound :) . . .

. . .and a whole lot of other people who follow you think that and retweet it then you have a theoretical capability to reach 1.2 mil people (Unique Names in your Second Level network).

Now the reality is that that will rarely happen, if ever, but in our profoundly hyper connected world Unique Names is a good number to keep on your horizon.

Remember success in twitter comes from participating in the conversation and giving something of value, not by running "social media campaigns". If you don't internalize that be ready for a reality where both your Followers, and Second Level Network size, to be small potatoes.

Qualitative Metrics / Analyses.

Now let's tackle the much much harder analysis to do in any filed, analyze the data from a qualitative perspective.

Linguistic Analysis:

I have given up on "Sentiment Analysis". Well atleast for now. Everyone over-promises and massively under-delivers.

On paper it seems like such a great thing to want to have, this is a social / conversation medium after all. But most tools I have had the good fortune to try are simply either glorified versions of Google Alerts even if they promise you buzz metrics and the moon.

Now sentiment analysis is a very hard problem to solve. For example I just analyzed my account using an expensive "social media sentiment buzz analysis tool" and it marked this tweet from today as Negative:

avinashkaushik: There in nothing quite like AC power in your seat for a 10 hour flight. Oh and 20 hours of pending work to do.

Perhaps the tool does not find my dry wit as funny as I do, but it's hardly "negative"!

With all that context I think TweetPsych holds a lot of promise.

Tweet Psych uses the Linguistic Inquiry and Word Count (LIWC) method and the Regressive Imagery Dictionary (RID) method to build a psychological profile of a person based on the content of their last 1,000 tweets.

Dan Zarrella, founder, says: "I think the possibilities of a system like this are enormous, from matching like-minded users to identifying users that exhibit certain useful or desirable traits."

I am not sure I understand perfectly how it works (I need to send this to Joseph Carrabis!) but the analytical techniques looks very promising. . .

Hmm… interesting. I do like talking about "learning, thinking, knowing etc"! :)

As always rather then looking at my data in isolation I compare / contrast it with my friend who is a web analytics twitterer. . .

Now I understand a lot better how I am doing and how he is doing.

Remember there is nothing wrong or right here, we are both just very different people with different twitter strategy and what Tweet Psych's linguistic analysis algorithms helps us understand if our psychological profiles are aligned with our twitter goals.

Tweet Psych also provides you with Primordial, Conceptual and Emotional Content analysis, here's mine. . .

Nice.

Use this type of analysis to understand at a deep level what attributes are being associated with your brand, and if they are reflective of the goals that you set for yourself.

Content Visualization with Stream Graphs:

Stream graphs can be very good at visualizing data, content specifically. Twitter StreamGraphs is delightful for:

1. its visualization of highly associative words with the word you are querying and

2. viewing streams (tweets) for any associative word (hence sweet filtering, something so darn hard to do with twitter content)

Here's today's view of the data for my account (searching for @avinashkaushik). . .

Please click on the image for a higher resolution image.

You can choose the associative word stream that you are interested in most, click on it and at the bottom you can see the tweets. The size of the stream and shows you strength.

Once you choose the stream you can also click on the dates on the x-axis to filter down to the tweets for that particular stream for that particular date.

Sweet.

The stream graph for avinashkaushik would be different, as it looks for mentions. . . .

Please click on the image for a higher resolution image.

It is analyzing the last 1000 tweets and such a great way for me to understand the content, and filter down and review the relevant tweets easily.

Twitter StreamGraphs helps you visualize content in a very unique way and solves a very important problem to boot.

Parting Words of Wisdom.

I hope if there is one thing I have convinced you of then it is that you need to be a lot more critical when you think of analyzing these new media channels.

It is important to put aside stale (certainly current web analytics) thinking.

It is important to participate in these mediums so that you'll truly appreciate what their real strengths are.

It is important to question metrics that have cute names, dig one step deep, just one single solitary step, to check if the metric definition passes the BS filter.

It is important to choose the metrics that help you measure your unique goals.

Finally it is important to realize there are no short cuts. Be willing to work hard. Be willing to put in the sweat equity. Be willing to try 45 things (tools / metrics / strategies) to find the 3 that work for you.

Good luck!

[Missed the contest? Go back and look for the red parenthesis, you'll win a copy of Web Analytics 2.0! Contest closed, thanks for the entries!]

Please share your feedback on this post via comments. Got any other tools that you love and adore? Please share them – with a quick comment on Why you love them. Got a piece of analysis that you think is profound? Please share that with all of us as well.

Thanks.

Social Media Analytics: Twitter: Quantitative & Qualitative Metrics is a post from: Occam's Razor by Avinash Kaushik

Admit it, you've heard it.

I suspect you've even used it liberally!! : )

Before we go any further I must clarify that I love branding campaigns just as much as the next guy.

I love campaigns that Visa runs. I love watching the IBM ads (with the Linux kid perhaps the best of the lot). I loved the I'm a PC ads from Microsoft (and I am a proud PC!). I loved the Wario Land: Shake It ad from Nintendo on YouTube (now that's creative!). I love a good billboard ad, Budweiser does good ones. My absolute favorite branding campaign of all time: Think Different.

I could keep going on.

The common theme through the above campaigns is that their primary purpose is "branding". The hope is by connecting with you, or interrupting you, a lasting impressing, a feeling, might be left in you so when its time to get a credit card you think of Visa and not MasterCard, when it comes time for hiring consultants for a multi year project you'll choose IBM and so on and so forth.

All well and good.

Here is the minor problem.

There is a very tenuous connection between these campaigns and outcomes, they are for the most part faith based initiatives. If supported by "data" then it tends to be of the most fragile kind (usually the the fact that the CEO saw it during the Super Bowl and felt happy suffices as actionable data).

None the less they persist.

Online it does not have to be that way.

It is criminal not to measure your direct response campaigns online. I think we have established that. I also believe that a massively under appreciated opportunity exists to truly measure impact of branding campaigns online. Paid Search or affiliates or email or display or YouTube or whatever channel you end up choosing.

[Oh and don't tell me that your "branding" campaign is to increase "engagement"! Remember: Engagement is not a metric, its an excuse.]

The Top Secret Hidden Never To Be Reveled Come Hell Or High Water Key To Measuring Branding Campaigns:

Answer this simple question: Why %&#$!^ are you doing the "branding campaign"?

Every campaign, and in turn website has a purpose. All you need to do is figure out what the purpose of your campaign is, no matter how outlandish (or childish) your goal.

The typical focus by companies, and the creative types in their employ, is to simply focus on figuring out what you are doing to do in the campaign.

I am recommending that they hold their horses / put their pants back on / slowly sit down in their over-stuffed chairs. You too!

Figuring our what you are going do do with your campaigns can come after you figure out why you are doing these campaigns. No, not just because you have money or because that is how things have always been and absolutely not because someone (a HiPPO!) asked you to.

Once you know the desired outcome you'll be surprised to learn all the wonderful measurement possibilities that await you online, things that would be nearly impossible offline. [The web rocks!]

Measurement Recommendations for Desired Branding Outcomes.

In order to help you make the leap in the rest of this post I want to share the most common outcomes I have heard associated with branding campaigns, and my recommendations as to what they should measure for their brand campaigns online.

My hope is that this bushel of ideas will spark your own creativity when it comes to measuring your campaigns.

Outcome #1: To attract "prospects" / new customers.

This is perhaps the most common desired outcome: "I am doing branding campaigns to attract new prospects to our website. They will come, they will be wowed by our glory, they will immediately convert."

It is not very hard to measure these campaigns.

Measure the change in the percentage of New Visitors to your website, its the orange line in the graph above.

Ideally you'll measure the number prior to your branding campaign, say Feb 2009, and then you'll measure it again during your campaign, March 2009. See if you were able to get more traffic to arrive at your site, and if they were Existing Visitors or New Visitors (hopefully measured with a first party cookie in your website analytics tool).

For good measure, just to be extra sure you'll segment out the visitors who come by clicking on your campaigns (display/banner, YouTube, rich media, whatever), and see how many of them were truly new.

At this point there is no expectation that any other outcome was delivered, just a visit by someone who had never been to your site before. A fairly low bar.

Outcome #2: To share your business value proposition.

You are a news site like The New York Times or you are a non-profit like Idealist or you are the team running Google Maps.

The goal of your campaigns is to simple share your unique value proposition with everyone. They'll be impressed enough to come visit your site and then do so repeatedly.

The ideal metrics for this desired outcome are Visitor Loyalty & Visitor Recency.

(Click on image for a higher resolution version.)

The data in the above report shows how frequently during a time period do the website's visitors visit the website. In the Before version you can see that most people, 69.79%, visited the website just once. In the After version, when the branding campaigns were running, only 63.25% of the visitors visited just once. Which means atleast 7% of the visitors shifted to visiting more than once.

You can credit the branding campaigns with that shift (if that is all you were doing). Better still you can segment the traffic from the campaigns and validate that hypothesis.

If people were impressed enough with your value proposition and visited more often the the brand campaign was a success.

Another good idea is to measure segmented Visitor Recency.

In this case the analysis will try to judge if the traffic acquired by paid search branding campaigns is visiting my website more frequently in any time period, when compared with other segments of traffic (in this case I am comparing it to All Visits).

Outcome #3: To impress people about your greatness and buy more.

I wanted to put this as #3 because if it were a "conversion" campaign then it would not be a "branding" / feel good campaign.

But there are certainly campaigns that you run to prop up your brand that will entice people to buy more from you. If they were only going to buy underwear then now they'll also buy a pair of shoes and headphones.

I recommend segmenting the traffic and measuring revenue lift but also measuring the average order size, if you did your job right then that latter number should be higher.

In this case our Yahoo! display campaigns did wonderfully in terms of conversion rate, but not in terms of the major goal of the campaign – sell more stuff.

Another thing people forget is to measure the overall impact, beyond simple conversions. Sure measure it as above but it is also good to marry up the qualitative data and measure Task Completion Rate using a onexit survey tool (use a free one like 4q from iPerceptions).

You can accomplish these goals:

~ Get an optimal understanding of what kind of people you ended up attracting to your website (look at primary purpose & distribution).

~ Were these people, even if all you wanted from them was to buy from you, able to complete their tasks.

In the above case that is clearly not true.

Perhaps your Average Order Size is not great because only 44% of the people who came to buy, as a result of your branding campaigns, were able to complete their task!

You would have fired your ad agency for a crappy campaign, turns out they did their job well but it was your website that stunk. Perhaps someone in your team needs to get fired? Perhaps you? (Just teasing!)

Note how marrying the Qualitative and Quantitative data can be helpful and identify true points of failure / success.

Outcome #4: To whisper sweet nothings to drive offline action!

Most commerce / love / stuff still happens in the real world and many many companies use various online marketing channels to drive people to take offline action (make purchases in stores or via their phone channel, show up for a woman's rights rally, meetup at a concert etc).

You can measure the impact of these campaigns right on your website, using any onexit survey tool and by applying some delightful regressions on your data. You can compute two important metrics:

Likelihood to Recommend / Brand Lift

You can measure this at an aggregate level, or you can measure it just for your campaign traffic.

It helps you understand what was the brand lift, positive, as a result of the person's complete experience (your campaign, plus your website).

Likelihood to make a Offline Purchase / Action

Simple right? Well it takes some planning but it is not that hard to measure (and if you get a decent sample size then you can also segment this data easily by source of traffic, brand campaigns, and show a causal relationship).

Phone Calls / Conversions Driven from Website

Another wonderful way to track offline impact of your campaigns is to use unique phone numbers with your campaigns (either on your display banner ads or on your website).

You can track the number of phone calls made to your call center, by campaign (or keyword or whatever) and if you have a integrated IVR then you can also track conversions / sign ups from those campaigns.

Companies like Fresh Egg in the UK or Mongoose Metrics and ClickPath in the US, amongst many others, provide these phone call tracking solutions.

[Bonus reading material: Multichannel Analytics: Tracking Offline Conversions. 7 Best Practices]

Outcome #5: To break through the noise / make an introduction to your business.

Very often when you run branding campaigns your goal is simply to introduce your business (like we are trying to do with Market Motive, our start up that provides certification courses in Web Analytics, SEO, PPC, PR etc etc).

A common mistake in this case is to simply focus on one outcome. If you are running a branding campaign then it is likely that you either have a very soft call to action or, more likely, you have a very general "our business is magnificent" message.

My recommendation is to quantify the online impact of these campaigns by measuring both the Macro & Micro Conversions.

For example if I were to measure impact of branding campaigns for this blog (remember it has no ecommerce of any sort) then this is how the report would look:

My macro conversion is to add to my current total of 27,300 RSS feed subscribers.

The above report shows the overall add to that number in this month but by segmenting my Yahoo! "Avinash is awesome" display campaigns I can see how many "macro conversions" occured.

But that's just one part of the story.

I will also measure the "micro conversions" (goals 1, 3 & 4) to get a more complete picture (for example note the large percentage of "Loyalists" that ended up being from my brand campaigns!).

This methodology can be applied to any business.

For example if I were in-charge of campaigns for OfficeMax, I would measure ecommerce conversions but that's just a sideshow for these types of campaigns.

For full impact analysis I would measure:

And so on and so forth.

In the case of Market Motive for our branding campaigns we will measure outcomes by focusing on # of sign-ups for the Master Certification program but also the # of people who sign up for the free webinars we do all the time, the Ask Us inquiries, the trial memberships, # of sample tutorial videos viewed etc etc etc.

When you are trying to break through the noise you'll take any measure of success to detect a signal, use the macro & micro conversion mental model.

Outcome #6: To destroy your competition.

Very common goal for many marketing campaigns. Show how awesome your brand value is and directly or indirectly show your competition in poor light.

DirecTV does it well, though now Dish seems to be totally trashing them atleast during NFL games [Look at them go at it: DirecTV trashing Dish vs. Dish trashing DirecTV.]

Apple of course is a master at it (though sometimes they can be mean). CPG companies are perhaps a bit more subtle about it, but their goal is clear: get you to buy their brand of chicken bouillon or diaper or ageless blush.

There are a number of wonderful metrics you can use to measure online success of such marketing campaigns.

Share of Search.

The first thing you want to measure is how much "share of voice" you have "stolen" from your competitor. One great way to do this is to measure Share of Search.

If you have done a great job of branding then the number of people looking for you (searching for you) should go up. Oh and not in your Site Catalyst or WebTrends reports! Rather in the "ecosystem reports" you can get at a competitive intelligence tool…

www.diapers.com has a 3.64% "share of search" prior to the campaign. What was it after?

Did they make a dent in the universe?

I am using Compete for the above report. When you use it you'll notice that Target is #10, Babycenter is at #15 (with a 0.77% share which seems looooow!). If you run branding campaigns for either company you now know how you'll measure success.

You can index your performance for your campaigns, and against your competitors.

If you use broad-match like I did above you get a "category" view of your performance. If you use the exact match report. . .

. . .to get a better idea about about "brand" performance (and find new competitors – facebook anyone? :)).

You can do this for pantene, shampoos, styling treatments, pro-v, 2-in-1 shampoo + conditioner… the world is your oyster.

This analysis also helps you understand how well your offline branding campaigns are doing online.

For example I don't know of airline companies that run more television campaigns than Southwest Airlines.

Yet currently for queries like cheap tickets, cheap airline tickets, cheap flights etc www.southwest.com does not show up in the top 15 in "share of search" reports, in some cases not even in the top 25. And that has not changed in the last few months (even with the barrage of new TV ads).

The TV ads are perhaps super productive in driving people to the phone or perhaps directly to the site, both desirable outcomes. But they are certainly not working in getting people who are looking for airline deals and searching for them to go to Southwest.com.

Traffic Differentials

If your campaigns are successful you'll know it from your Site Analytics tools like Google Analytics or Yahoo! Web Analytics. But in the grand scheme of things did you have an impact?

Use tools like Google Trends for Websites (or Compete or HitWise) to analyze your performance…

In the report above if you (Hilton Hotels) ran your campaigns in March 2008 (purple arrow) then you managed to accomplish nothing. Notice the competitive trends?

If you ran your magnificent branding campaigns in Jan 2009 then buy yourself some beer and dance around because you can see, against competition, you clearly narrowed the gap (black arrow).

Of course you would not stop at the simple analysis above, that's just a start. You can export data into excel, you can segment it by Geo and ensure the lift is where your campaigns were targeted, you can segment by demographic and psychographic visitor attributes to see if you got the right kind of people.

Using Share of Search or Traffic Differentials are just two of many ways in which you can measure if your branding campaigns are indeed crushing your competition. There are many other analyses you can do, there are many other tools you can use. Don't give up, look.

Outcome #7: To emboss your brand into someone's skull.

As Marketers we try and do this all the time as well.

You say Jeans, and people say Levis.

You say Jonas Brothers, and people say Pink by Victoria Secret (I kid you not, yes I was surprised, I have research to back this up!).

You say online search, and people say Bing! No, not so fast! Google!! Ok maybe it's just a matter of time. :)

You catch my drift.

Branding campaigns are particularly effective at "embossing" brands into your psyche with the goal of improving unaided brand recall.

The challenge of course is how do you measure this elusive, but very desirable, outcome.

I have two suggestions.

Primary Market Research

In the online world we don't make enough use of primary market research, and that's a shame.

Field surveys, focus groups, interviews etc can be used very effectively to gain a indepth understanding of your customers and their influences (hopefully channels and methods you use to influence them show up in the answers!).

There is a ton of math and rigor involved in these studies that helps you get a great understanding of your audiences, even with small enough sample sets.

Plug into the Database of Intentions

I love that term: "database of intentions".

For now atleast search is used by many people as they seek information online, and that allows for this data to reflect intent, rising and falling trends, preferences etc.

You can use Insights for Search for this type of analysis.

I am a Assistant Senior Vice President of Brand Marketing Campaigns at Orbitz. I have been spending my lovely marketing dollars on tons of TV campaigns (See: Orbitz Golfers). Oh and a smidgen on online campaigns as well.

So what was the impact, when people search for "hotels" do they think of Orbitz? Here's the data you are looking for…

In the online database of intentions Orbitz is not on the horizon.

The data on the left is important because it tells you what people search for when they look for hotels.

The data on the right is killer. It shows which terms (hence brands, sites, properties) have risen the by the most statistically significant amounts. This is fantastic because it mines the data that is below the surface and brings the movers and shakers forward.

Some of Orbitz's competitors show up there, their marketing dollars seems to be working well in improving the likelihood that when people are doing category searches, hotels here, that they would look for expedia, priceline, hotwire, etc etc.

But I am not going to give up, I just started running these massive tv campaigns a few months back. Let's see the data for that!

Hmm…. the "winner" here seems to be Marriott from the hotels category in the last 90 days. Good for them, sad for me.

Let me hasten to add that it is quite possible that the desired result of these offline (and online) branding campaigns was to get people to go to the site directly or call Orbitz on the phone.

The first premise you can measure easily, how is our website traffic doing when we are running all these campaigns…

Two years of data. Expedia is green, Orbitz is blue, Priceline is orange (query on compete). It does not look too good for me (and if that was not enough Priceline just crossed me for the first time in my history, boo! boo!).

[It is important to point out I am simply doing outside-in analysis, a sport always fraught with risk. You on the other hand work at Orbitz and will have the tribal knowledge to make sense of this data better.]

For the last piece of analysis to measure unaided brand recall analysis, I'll try is to correlate my brand marketing spend with the number of phone calls to 1-888-656-4546 (and keep my fingers crossed that I'll see a massive spike in phone calls from the campaigns because, on the surface, it is hard to detect a impact directly on the site).

You can do this type of analysis for anything.

Let's say Victoria's Secret has indeed been heavily spending on branding campaigns for Pink by Victoria's Secret in the last 90 days. I can look at this data to see if the brand Pink is amongst the fastest rising by those people who look for Victoria's Secret "stuff"…

Not so much. Why?

Now if I were the Brand Manager for the Jessica White line then I would rejoice! While Jessica White is not in the "top related searches" category yet, it is rising very very fast as a result of my campaigns! Yes!

(Mental note to check later: What the heck is "victoria secret application"? Sounds dicey!! :)).

So there you go. A portfolio of seven strategies that you can use in the ultimate quest for any online marketer / analyst: measuring branding campaigns.

I hope they spark your creativity and lead you to finding even more innovative solutions to your unique challenges.

Buena Suerte!

Ok your turn now.

Are there other outcomes you can think of for your branding campaigns? Can you think of other ways to measure the seven outcomes mentioned above? If you have tried one of the above strategies did it work? If it did not, why not?

Please share your feedback / learnings / critique / kudos.

PS: Like this post? Perhaps you'll consider ordering my * new * book: Web Analytics 2.0.

PPS:
Couple other related posts you might find interesting:

• Excellent Analytics Tip #15: Measure Latent Conversions & Visitor Behavior
• Multichannel Analytics: Tracking Online Impact Of Offline Campaigns
• “Dear Avinash”: Be Awesome At Comparing KPI Trends Over Time
• Excellent Analytics Tip #12: Unsuspected Correlations Are Sweet!
• Standard Metrics Revisited: #5 : Conversion / ROI Attribution

Brand Measurement: Analytics & Metrics for Branding Campaigns is a post from: Occam's Razor by Avinash Kaushik

In this blog post we are going to bring the sexyback. I am going to attempt to significantly simply your life by recommending the critical few metrics you should use to analyze performance of your digital marketing campaigns and website. You'll be able to quickly go from "omg what can I do!" to "omg what am I going to do with all the money and fame I'm earning!"

The approach I'm going to use is to 1. Use my Acquisition, Behavior and Outcomes framework to ensure an end-to-end view of important activity and 2. Recommend metrics / KPIs you can use based on the size of your company.

Each recommendation comes with hints on what analysis to perform once you have the data, and what changes you could make to your campaigns, content and overall digital strategy. [A summary in pictorial format is at the end of this post.]

Excited? Let's do this!

Best Metrics / KPIs for Small Business Websites

Small business websites are a very fragile ecosystem. People working hard to do the best they can on the smallest possible budgets. But not to worry. They have to start with just four simple metrics to start rocking!

Acquisition:

Clicks? Visits? Backlinks? Impressions? No. We have something magnificent.

Cost Per Acquisition.

Obsess about this metric. You have very little money. You need to know, obsessively, what you get for it. This metric delivers that insight. Oh, and everything has a CPA (not just your paid search or display/banner ads). If you are doing SEO then you are likely paying for someone. That's the cost.

Kill things that don't have an optimum CPA. Invest more in ones that do. Simple enough, right?

Tip: Remember this is just cost, not profit. If your product costs you $15 to make then, in the above scenario, you are shipping a crisp $5 bill along with every Social Media order!

Where is it? Most likely in Excel. For Search it is in your Google Analytics or Omniture Site Catalyst reports. But for most other programs (Affiliate, Email, Social, Display) your Cost is likely sitting outside your web analytics tool. So extract the # of conversions, import into Excel, add a column for Cost, do the math, sing or weep (based on what the data says!:)).

If you are paying someone to do web analytics and this metric is not on top of the dashboard they've created for you, it might be time to say sayonara to them.

Behavior:

Page Views? Time on Site? No. You can do so much better!

Bounce Rate.

I continue to be a believer in trying to prompt love at first sight. Okay, okay, I'll settle for delivering relevance. :) Bounce Rate helps you identify campaigns where you might be targeting wrong people (who then come to your site and leave right away) or sending relevant traffic to irrelevant (and often flash-filled hideous) landing pages.

Bounce rate helps you find campaigns and landing pages that need to be killed / improved. Everyday.

Where is it? Standard metric in every web analytics tool worth anything. Look at your All Traffic Sources report and your Landing Pages report.

Checkout Abandonment Rate.

I find the fastest way to make money is to take it from the people who have already decided to give it to you. Obsess about checkout abandonment rate (the percentage of people who click Start Checkout to those who complete that process).

Focus on checkout steps with the highest abandonment. Tweak like crazy. A/B & Multivariate tests are a good option. But you are a small business… so just take away as many fields as you can, play with where to show shipping cost (I vote for way up front), reduce the number of checkout steps if you can, ask for account creation at the end of the process rather than at the start. Try, test, measure, be rich.

Where is it? In Excel. Or if you use Google Analytics: In Paditrack for free. (Google Analytics' native funnels are pretty sub optimal, ignore that entire feature.) For other tools: In KissMetrics. Create a funnel just for the checkout process (from clicking Start Checkout to Thanks for your Order) and both these tools will give you the metric automatically. They also allow you to segment the data! Make love to it.

[Bonus: What is abandonment rate?]

Outcomes:

My favorite Economic Value? No. As a small business I recommend…

Macro Conversion Rate.

You are a small business. Obsess about conversion rates, and everything connected to improving them. What products are people buying? Every single day (okay week) look at the All Traffic Sources report and seek out the Conversion Rate metric. Ruthlessly punish sources that are not working well and reward the pretty babies. Be they Earned, Owned and Paid media – oh and have a marketing strategy that has each of those elements or as a small business owner you are not going to win a lot.

I love creating an advanced segment with just the people who buy twice the average order size. I call them the Whales. Look at sources, locations, product bundles purchased, keywords and campaigns and all that to learn where/how you can find more Whales.

Where is it? Standard metric in all analytics tools. Remember to look at both the rate and the raw number of conversions for context. People make silly decisions when they don't do that.

That's it!

You are a small sized business and these four simple key performance indicators will literally rock your world as soon as you start measuring them. Cost Per Acquisition. Bounce Rate. Checkout Abandonment Rate. Macro Conversion Rate. Don't look at any other metric until you feel you've mastered them.

Tip: If you've hired the right analytics talent/consultant to help you, they'll be measuring these fabulous four.

Best Metrics / KPIs for Medium Sized Business Websites

What if you are a medium sized business? What key performance indicators are optimal for you?

First, you are going to measure the KPIs mentioned above. But because you are running a bigger and more complex business you'll also measure…

Acquisition:

CPA

+ Click-through Rate

While CPA is a macro metric about your campaigns' bottom-line performance, Click-thru Rate (CTR) is a deeper dive into analyzing the creativity and relevance of your affiliate deals / search listing / blinky banner ads.

In the context of Search (Paid or Organic), the text in your ads, the number at which your listing is ranked, the match between the user query and your ad's intent all help you receive a higher CTR. And if someone comes to your site (and does not bounce!) then you get an opportunity to convince them of your product or service's glory.

Small tweaks to the subject line of your email campaigns can have dramatic improvement in CTR. Recency and Frequency capping of your display remarketing campaigns can have a huge impact. Changing demographic targeting options in your Facebook ads can work wonders. Etc., etc., etc.

Put another way… CTR helps you understand if you showed up at the right place for your first date. Are you dressed okay. And if you are smiling the right smile. Helpful to know, right?

Where is it?

Everywhere. Start at a campaign level. Drill down to individual creatives. Kill badness. Promote goodness. Rinse. Repeat.

Behavior:

Bounce Rate

Checkout Abandonment Rate

+ Page Depth

A very tiny percentage of visitors to your site will see more than a couple pages. That's the internet for you. As you improve the user experience, information architecture and relevancy of content on your site, it is important to keep an eye not on the rather useless metric of Average Page Views per Visit or Average Time on Site but rather on the distribution of page depth. Here's how that picture might look like (from a post I wrote in July 2006!)…

From the deep detail reported by your web analytics tool you can choose to aggregate into buckets you most care about (like mine above). Categorizing the visits into Abandoners, Flirts, Browsers, One-off-Wonders, Loyalists will dramatically change your view of content consumption. Over time, as you move to deeper consumption, you'll see direct business rewards.

The above image emphasizes a sale/conversion at the end, but even if you are a content-only website improving Page depth helps you because more pages equal (at the very minimum) more ad impressions!

Where is it? The final table will be in Excel. If you use Google Analytics the data you need is here: Audience > Behavior > Engagement > Page Depth tab. If you use WebTrends, Yahoo! Analytics, Coremetrics please click around to find the data. They all have it.

+ Loyalty (Count of Visits)

If Page Depth helps you optimize for a single session experience, Loyalty helps you optimize pan session behavior. Put another way… how good are you at getting the same person to visit your website multiple times? For ecommerce or non-ecommerce websites, loyalty can mean the difference between life of survival and raking in profits like crazy.

First set a goal for the % of site Visits you would like for people who've visited more than x times. [Set a goal for x too. :)] For ecommerce websites use your Days to Conversion report (more on this metric below) to set your goal. For content sites perhaps mirror your content update schedule. If you are the New York Times and you update the website 24 times a day then should the average person be visiting the site at least 90 times per month?

Your BFF, as always, is analysis and not just reporting the metric. Create this simple segment in five seconds…

Apply to your keywords and campaigns and referring sources reports and identify which sources drive loyal traffic. Apply it to your content reports and figure out which content drives Loyalty (Sports? Op Ed? International? Cat Stories?).

Where is it? In every web analytics tool on the planet. If you use Google Analytics the data you need is here: Audience > Behavior > Frequency & Recency.

Outcomes:

Macro Conversion Rate.

+ Micro Conversion Rate

Pick your favorite benchmark and you'll notice that less than 2% of visitors convert. Focusing on just the Macro Conversion Rate means you don't care if you received any business value from the 98% that did not convert. I refuse to accept that uber-lameness.

Identify your Micro Conversions (/Goals) and obsess about the long and short term business value they deliver. You'll quickly realize the Economic Value they create for you is often far greater than the Revenue your Macro Conversion reports! And optimizing for that will ensure you win HUGE.

Where is it? In Google Analytics it is here: Conversions > Goals. Even if you are a content site the data is there. Details in the Goal URLs report. Setting up goals takes two minutes, setting goal values might take you a week (see measurement strategies here). If you use other tools, please check with your vendor.

+ Per Visit Goal Value

This Key Performance Indicator 1. helps you move beyond the obsession of focusing on the 2% (because it forces you to focus on Every Visit!) and 2. encourages you to create a business that uses the web to deliver multiple outcomes to your visitors.

Every visitor will not convert, but every visitor will, hopefully, deliver some Economic Value. Looking at this metric helps you identify Goals that contribute higher value, and and understanding of simple things like where you should focus on. If Twitter delivers 87 cents of Per Visit Goal Value and Google delivers 97 cents then perhaps I want to keep focusing on my SEO strategies rather than following the advice of the Social Media Guru who's just informed me Search is dead.

Where is it? In pretty much every single report in every single web analytics tool. Click on the Goals tab.

That's it!

For a medium sized business we ended up with nine metrics. Seems about right if you are making more than five million dollars of economic value. They key difference from websites that are in the small business category is that we are going to shoot for multiple conversions, deeper site engagement and better analysis of acquisition efficiency.

Time now to deal with the big boys and girls… large websites!

Best Metrics / KPIs for Large Sized Business Websites

Acquisition:

CPA

Click-through Rate

+ % New Visits

My choice of this metric perhaps betrays my refusal to rest on my laurels. There are clearly a finite number of people in the world relevant for any business. But staying hungry and staying foolish is a popular mantra for me. I use this metric to constantly calibrate my acquisition strategy to understand which inbound marketing efforts are bringing new "impression virgins" to the business.

If you look at your Earned, Owned and Paid media then this metric is especially important for your Paid media efforts. Except for your re-targeting / behavior targeting campaigns, you want your paid search, display, affiliate, and social efforts to bring new visitors to your franchise.

Where is it? It's like air, everywhere! Don't forget to segment for optimal analysis.

Behavior:

Bounce Rate

Checkout Abandonment Rate

Page Depth

Loyalty (Count of Visits)

+ Events / Visit

Every awesome large website delivers complex experiences (videos, demos, dynamic slideshows, configurators + + +) via sophisticated technologies (Flash, AJAX, Gadgets + + +). Almost all of the time we leave measuring their effectiveness on faith (or the HiPPO). I love event tracking because it helps us measure these often astonishingly, expensive initiatives.

Of 110,842 visits to the site, 9,054 interacted with your delightful experiences and each of those visits had 2.24 Events per Visit. Is that good? Bad? Could be better? Are these 2.24 interactions delivering higher economic value to your business?

In the above case the answer was a big NO. In your your case you'll decide based on your strategy and goals. At the end of the analysis you'll make significantly smarter decisions about your content (especially because the Analysis Ninja that you are, you'll triangulate performance of this metric with first, Page Depth and, second, Loyalty).

Where is it? Most web analytics tools do some type of event tracking. Please check with your vendor (it might not be called event tracking in their lingo, just describe my first paragraph above). In Google Analytics the data is here: Content > Events.

Outcomes:

Macro Conversion Rate.

Micro Conversion Rate

Per Visit Goal Value

+ Days to Conversion [or Time Lag for Content sites]

Another pan session metric I adore.

Life, no matter how hot you are, is not a series of one night stands. Yet because of how they analyze the data most companies end up optimizing their web marketing campaigns for one night stands. Come here and convert NOW! If yes: Oh, I love you. If no: Kill the campaign!

That approach is not just short-sighted; it is an insult to your visitors. Convert them at a pace they are most comfortable with. This metric helps you understand how quickly or slowly your visitors convert. You can, at the very minimum, change your campaign messaging and come hither calls to action and adjust your landing pages. If the Days to Conversion are much longer, then create a robust (slow dance) micro conversion strategy.

If you have a non-ecommerce website then there is something delightful for you in the Google Analytics Multi-Channel Funnel reports. Checkout the Time Lag report . It is showing you exactly the same data as the Days to Transaction for Ecommerce sites. The metric you see immediately above is called Conversions. It is essentially your Goals (/micro conversions).

Optimize your "hello, nice to meet you, what would you like, here is what I have to offer, why don't you check with your spouse, come back and check it out again, multiple times, I'm still here, you ready to convert / deliver economic value, here's how… " process.

Where is it? Days to Conversion is in the Ecommerce section of your web analytics reports. It is a standard report. (Don't forget to segment your sources. Deep insights await.) Time Lag may or may not be a standard report in your tool. Please check with your vendor. In Google Analytics it is a standard report here: Conversions > Multi-Channel Funnels > Time Lag.

+ % Assisted Conversions

This is the newest metric I've made standard for all my clients / partners / BFFs. And it is a sweetie.

Assisted Conversions builds on the above mental model. It takes a while for a majority of your visitors to convert (macro and micro conversions), so why does almost all of web analytics focus on single channel analysis and optimizing that single channel in a silo? Just because the Affiliate click was the last one before conversion should it be optimized for that conversion? Especially if the Visitor originally came via Facebook (or Google or whatever)?

How many of your conversions had more than one ad / media / marketing touch prior to converting? Really smart Analysts at really successful companies understand that…

…and then use that data to optimize the portfolio of channels rather than individual channels for the company.

Even if you don't do portfolio optimization (and desperately hope you do) you can easily see how the above data will cause you to execute a different marketing optimization and expectation strategy for Email (1.18 Assist / Last Interaction rate) vs. Organic Search (0.61).

I am being modest when I say that this metric and subsequent analysis will have a fantastic impact on your company.

Where is it? % Assist Conversions may or may not be in your web analytics tool. Please check with your vendor. In Google Analytics you'll find it here: Conversions > Multi-Channel Funnels > Assisted Conversions.

And we are done!

For large businesses we've identified 13 key metrics that would give a robust end-to-end view of business performance. The key difference vs. medium sized businesses is that we are really, really, really focused on pan-session (multiple visits) behavior. Put another way, we really care about people here and not just a single visit.

Here is a summary of the metrics I am recommending in this post…

I hope the picture above will quickly help diagnose where current gaps in your measurement strategy might be.

Additionally if you are a small business you'll know what else to measure when you start to become medium sized and if/when you cross that threshold you'll know the metrics that come with your large business status. :)

You'll notice that I'm not focusing on KPIs like AdSense Ads CTR or Page Load Time or Actions per Social Visit or Search Exits (I love this metric!) or Content Distribution vs. Content Consumption Rate or Conversation Rate (in case of a content site) etc. That's simply because these KPIs tend to be unique to the type of business you are running. My strategy above was to focus on just the KPIs that would be applicable across all types of businesses.

That brings me to a very important point.

While it is my hope that you'll find my recommendations above relevant and yummy… the most optimal way to identify that best key performance indicators for your company will come using the process and structure outlined in the Digital Marketing & Measurement Model.

I'll end with the thought I started this post with… we have more data than God wants anyone to have. But web analytics does not have to be scary or impenetrable. Use the roadmap above, focus on all three elements (acquisition, behavior, outcomes) and I promise you'll soon be on your way to being as happy as God wants everyone to be.

I wish you all the best!

Okay as always it's your turn now.

Does your business use the above recommended metrics / key performance indicators? Do you have an absolute favorite metric that's not mentioned above? Which metric above do you find most useful? Which one most useless? What is your strategy for identifying the most relevant metrics?

Please share your suggestions, critique, and helpful best practices via comments.

Thank you.

PS:
Couple other posts on metrics / KPIs you might find interesting:

• Web Metrics Demystified
• Your Web Metrics: Super Lame or Super Awesome?
• Kill Useless Web Metrics: Apply The "Three Layers Of So What" Test

Best Web Metrics / KPIs for a Small, Medium or Large Sized Business is a post from: Occam's Razor by Avinash Kaushik

I am going to break the second part of the rule.

I am going to define a way for you to think about measuring social media, and you can't actually easily measure what I am going to recommend.

So why break the rule?

Social media is evolving at an incredible pace. Most of us have no idea how to participate optimally in this unique channel – we are doing TV on Twitter (breaks my heart). The impact on the data side of the ecosystem is that massive amounts of data is being generated and much of what goes for measurement in "social media tools" is profoundly sub optimal (I'm being polite). We have IT-minded people engaging in massive data puking (one report with 30 metrics anyone?) and Marketing-minded people who are using lousy measures of success ("I got 158,632 Fans! Hurray!").

I want to propose a framework you can use to measure success using metrics that matter for one simple reason: They actually measure if you are participating in the channel in an optimal fashion.

Isn't that revolutionary? Use data to incentivise our companies to do the right thing by measuring what matters, what makes this channel so unique.

No more embarrassing your brand on Twitter, Facebook, Google Plus, YouTube. And we build out a loyal cadre of followers / friends / subscribers to boot!

So what actually matters in Social Media?

Not the number of Friends / Followers / Subscribers. Not the number of posts / tweets. Not the ridiculous Followers to Following ratio. Not the… well there are so many horrible ones to choose from.

What matters is everything that happens after you post / tweet / participate!

Did you grab attention? Did you deliver delight? Did you cause people to want to share? Did you initiate a discussion? Did you cause people to take an action? Did your participation deliver economic value?

The "so what? " matters!

Oh, I totally forgot to say this…. the advice in this blog post is only for businesses and brands that participate in social media. Businesses as in Red Bull and T-Mobile and Johnson & Johnson. Brands (all of the aforementioned plus…) as in Mitch Joel and Stephen Colbert and Nancy Pelosi. If you don't fall into those two categories then this social media measurement framework might not apply to you.

I'm proposing four distinct social media metrics we should measure, (and this is so cool) independent of the social channel you participate in.

Excited? Let's go….

1. Conversation Rate.

When I say most brands do TV on social media what I mean is that we do the same uninformed shouting and pimping on social media that we do on TV.

We know little about who is on the other end of the TV set and the medium places limits to what we can do. So to make our marketing more efficient we shout more loudly, more frequently!

We don't have to do that. We can get a very good sense for who is following / friending / subscribing to us. We can measure if what we are saying connects to them (in near real time!). And unlike all others, this channel has the word social in it! Social as in talk and listen and discuss.

So why not measure that?

One beautiful thing… you can measure this on every social channel on the planet. Blog. Twitter. Facebook. Google Plus. YouTube.

What to do with it?

A high conversation rate requires a deeper understanding of who your audience is, what your brand attributes are, what you are good at, what value you can add to your followers and the ecosystem you participate in.

That is why I love this metric. It forces you to do the right thing right away. And it is a lot of work.

So aim for a higher Conversation Rate. Build your own watering hole in the digital universe. Have meaningful conversations with your audience. That's Marketing money just can't buy.

You can always be provocative, say silly things and get a high Conversation Rate. Pick Sarah Palin for your topic. :) But that would not be accretive for your brand equity, would it?

Remember we do not measure to manipulate the metrics, we measure to know if we are adding business value.

How to measure it?

Individually this is not that hard to measure. But across channels there does not seem to be an option.

This is where I need your help. Do you know of a tool that measures conversation Rate easily as defined above across the main social media channels? Please share via comments and I'll add it here. Thanks!

Up next, our second delightful metric…

2. Amplification Rate.

Every channel has inherent limitations, often exhibited by the number of ads you can buy. On Google (paid search), on Facebook (display ads), on Radio (audio ads), and all other channels you can think of.

But social media has a profound advantage you can tap into.

Not only do you have a network, but every node in your network has a network of its own! If you follow my advice and post something "incredible, relevant, of value" to your audience then they can allow you to break free of the limitations of your network and spread your word around to a more massive audience!

Take me as an example. I have, as of today, 57k followers on Twitter and around 12k on Google Plus. That's the limit. Even if every single person who follows me reads every single thing I write, I can at most reach 57k people on Twitter.

But the size of my second level network (the unique people who follow the people who follow me) is 6.3 mil. My real "reach" it turns out is not 57k, it is 6.3 mil!

So measure Amplification, the rate at which your followers take your content and share it through their network.

On Twitter:

On Facebook, Google Plus:

On a blog, YouTube:

(Share clicks as in number of times your social media buttons were used to spread the content.)

What to do with it?

As you post and tweet and you rock and you roll… measure what pieces of content (type) cause amplification (allow your social contributions to spread to your 2nd, or even 3rd, level network). Understand times and geo locations and topics and things.

Then do more of the type that increase amplification. You'll get more sharing and spreading of your content. But this is very, very important: You'll be giving your audience content they consider to be of such incredible value that they want to share it (and hence you'll know what your audience wants / loves).

Oh, oh, oh and…. over time your 2nd level network becomes your 1st level network… because they discover that you rock!

Marketing, relationships and a reach that money, honestly, can't buy.

How to measure it?

I don't quite know how to do it easily across all the channels. Individually you can, see image above, pull out Excel and make magic!

Do you know of a tool that precisely measures Amplification across all channels as defined above? Please let me know via comments, and I'll add it here.

Now on to a metric that had us at "hello"…

3. Applause Rate.

I'm sure you've noticed my secret evil plan to force you to understand your audience (and not just pimp your agenda in Social Media).

One powerful, more immediate way, to understand them is to measure Applause.

One Twitter:

On Facebook:

On Google Plus:

On a Blog, YouTube:

What to do with it?

Simple… you want to know what the audience likes (to use the Facebook terminology) and what they don't. You get a much deeper understanding of what your audience likes so much that it will +1 your content (or contribution) and allow for that to be then shown to others in their social graph.

And consider this…

If you +1 this blog post, you'll not help me understand its relative quality, but when someone in our extended social graph does a search on Google for Social Media Metrics your endorsement of this content will show up in the search results. That's reassuring to your social graph, and it is great for me because your endorsement makes this post stand out over others and I get a relevant visitor/customer.

Sweet, right? Your selfless social media contribution comes back to assist you in driving valuable business outcomes.

That's why you measure Applause. It matters in ways you can't imagine!

How to measure it?

Individually the numbers are available in most tools. Easy to find in Google+ (see example in the end). For Facebook the number is included in Facebook Insights, though it is not available as easily in a simple way (at least not as expansively as outlined above). For Twitter, sadly I could not find it anywhere (inside Twitter or other tools).

So help me. Do you use a tool that will allow us to measure Applause Rate? Please share via comments.

Finally the metric any company leader will adore…

4. Economic Value.

I am smiling. I know that the long time readers of my blog would know that I would never let you get away without measuring hard business bottom-line impact of any digital effort!

It is foolish to believe that just Conversation Rate, Amplification Rate, Applause Rate will get you the eternal love and gratification (and perhaps budget!) of your company's leadership. Yes they care a little bit about this "social media thing." But if you want their adoration (and let me repeat: budget!) you are going to have to quantify the economic value created via social media.

You don't participate in social media to only drive business outcomes. I cannot stress that enough. If that is your primary objective you are going to suck at it (and the above metrics will reflect very efficiently how much you suck).

But.

A small percent of the people in your company / brand's social graph will come to your main digital outpost (usually your company website) and choose to do business with you. Some of them will buy something, others will sign up for your email marketing list, others still will order a catalog or write reviews for products on your site or sign up as an affiliate or create wish lists or marriage registries or phone your call center to order something or… stay with me…. buy your products or services in your supermarket / store / real world thing.

And you know what all of those things are? Macro and Micro Conversions!

And you know what you can do with macro and micro conversions? You can measure Economic Value!

On all social media channels:

Social media participation, done right, adds value to the company's bottom-line. Some of it can't be computed. That is okay. But some of it can be and it is your job, nay duty (!), to quantify that.

It is not very hard to do. Read the two posts immediately above. They share very specific guidance for businesses of different types (B2B, B2C, A2K) about how to identify the macro and micro conversions and then compute economic value.

What to do with it?

Those of you who have been at one of my recent keynotes have seen this slide:

Your job is to identify that blue arrow, and the orange box (what it stands for and what the amount is). It is not very hard, just takes a little patience and imagination.

And here is the incredible, amazing, magical thing. Once you have your highest level segmented view of the acquisition strategy, above, you can in two seconds segment down to individual channels you participate in.

Your view will look something like the one below, from Google Analytics:

I can focus on the Per Visit Goal Value (economic value delivered by visitors from social media channels across my macro and micro conversions – note the 0% in the macro conversions column, ouch!) for each channel. StumbleUpon rocks ($1.43), Twitter takes the next spot (around $0.60) and then comes Facebook ($0.26, clearly not a winner for me).

Now, not only can I tell my CEO what the small amount of direct value added to the business is, I can also report to her/him exactly which channels are contributing how much. You can't be in every social channel that pops up. The above data can give you guidance on where to be.

You do Economic Value and you will never, ever have to beg for investment in Social Media, and your career will get on the fast track. I promise.

And just to repeat one more time. A vast majority of value your business / brand gets from social media will be owning your message, building the watering hole I've mentioned, having a direct relationship with your customers and so much more. But showing some direct economic value will get you permission to do more of that. Without it you are just another "smarty pants" promising "vague outcomes" via "the next hip thing."

How to measure it?

Use Google Analytics, Omniture, WebTrends, CoreIBMInsights, etc.

Takes less than five minutes to set up. Provides a lifetime of joy.

Four Metrics That Rock.

Conversation Rate. Amplification Rate. Applause Rate. Economic Value. Four simple measures that get you to focus on the right thing from a social media participation perspective, help you understand how well you are doing at it, and quantify the business impact.

The challenge is that thus far it is hard to pull them all together in one place. As I had mentioned earlier, Excel is your bff for now. My hope is that vendors will stop creating tools in silos (just do Twitter or Facebook or Google Plus or YouTube or…) and start to think of real world needs of Brands and Businesses and pull together metrics we need into one place (from all social channels).

There are small signs of hope.

Crowdbooster has a very interesting view of twitter:

You can see Retweets (x-axis) and Replies (size of the circle) overall and individual tweet perspective. So both Conversation and Amplification. The other two metrics are missing, but it is a start.

All My + is a early prototype of data from Google+ and provides three of the four metrics recommended in this blog post:

It is missing Economic Value. But you can get that out of Google Analytics or Site Catalyst in five minutes.

If you are a tool vendor… I would love for you to adopt the aforementioned metrics, and definitions, into your tool. All I ask for is a donation of one million US dollars to Doctors Without Borders. Doable?

What About Social Media Advertising?

If you are engaging in brand advertising on social media channels then the metrics you should solve for should be the first three. If you do a Promoted Tweet or Facebook Like campaign or whatever Google+ decides to come up with then you want to measure resulting Conversation, Amplification and Applause (of course only if you did not stink at your campaign).

If you are engaging in direct response advertising on social media channels then the fourth metric, Economic Value delivered, comes into play from a strategic perspective. It covers both the immediate value (revenue via macro conversions) and the longer term value (economic value via micro conversions).

For tactical reporting of your direct response social media campaigns, the metrics you'll use will be the ones I've recommended for all other advertising channels (paid search, display, affiliate, whatever).

Here's that picture, applied to SM DR campaigns:

Value Per Acquisition. Shoot for that.

It will be hard. The enchanting temptresses that are Clicks and Impressions and Avg. CPC will try to lead you astray. Resist their charms. Trust me.

Go for Ninja-hood.

Closing Thoughts.

Social media presents and incredible opportunity to rethink what it means to connect with and influence customers. You need to forget what has worked in the past (and that is why this is so incredibly hard to do. The biggest brands in the world embarrass themselves every day on social media). You'll have to rewire your brain.

In presenting new metrics for you to measure, what I'm really trying to do is provide a very small assistance in helping you think differently.

I hope it works.

Update: Bonus:

Erik Ohlen was inspired by this blog post to create a very simple, and effective, dashboard where you can track the four recommended social media metrics.

As I had stressed above, currently if you want to report these metrics exactly as defined above and from ALL the social channels mentioned then you have to do so manually. Small price to pay for communicating the actual impact of social media to your management right?

Sheet 1 is the dashboard itself, with instructions. Sheet 2 is where you type in the raw data. Could not be simpler.

Download: Social Media Metrics Dashboard. Adapt it to your business. Rock a lot!

As always it is your turn now.

How do you measure the success of your social media efforts today? Got a favorite super lame or super awesome social media metric? Does one of the four (or all four!) metrics above resonate with you? What did I miss about social media? Is there a benefit / outcome / facet that I missed in my measurement framework?

Please share your feedback, critique, suggestions, and cool tools to measure these four metrics, via comments.

Thanks.

PS: A few helpful links for you:

A couple of my older posts with thoughts on social media measurement:

~ Social Media Analytics: Twitter: Quantitative & Qualitative Metrics

~ Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions

A post how to segment your social media data in Analytics (includes a downloadable advanced segment):

~ 3 Advanced Web Analytics Visitor Segments: Non-Flirts, Social, Long Tail

A comprehensive post on the most important gift you can give your business, compute economic value of your digital efforts:

~ Excellent Analytics Tips #19: Identify Website Goal [Economic] Values

Best Social Media Metrics: Conversation, Amplification, Applause, Economic Value is a post from: Occam's Razor by Avinash Kaushik

Not true. Email remains an immensely credible and profitable channel, with an immense reach to boot. To not have it as an active part of your marketing portfolio is sub-optimal.

The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. Every fiber of your being. Every thought that crosses your mind. Every person in your company. Embrace permission marketing and email will be a surprising and loyal BFF. I dare say even more than Search (and without a shred of doubt, more than Social Media). Because you control everything. The message, the customer data, the ability to reach current and prospective customers, drive new sales as well as repeat sales, experiment with new ideas and offers, and so much more.

So… Email = totally worth dating, engaging, marrying and having babies with.

You just have to have the right mental model (see Seth Godin above) and you have to… wait for it… wait for it… measure everything you do! Just to ensure you are executing against your right mental model.

Analyzing email campaigns requires three important insights:

      1. You must use metrics that are unique to the medium.

      2. You can’t track everything. Sorry.

      3. You need to think end-to-end, and not just your silo.

Seems simple enough, right? Amazingly, and sadly, I see examples of failure simply because the above three “simple” things are not clearly understood or executed against.

One of the core challenges with email is that you have to deal with multiple data sources. There are three primary sources:

      1. Your campaign data. How many emails went, to whom, what happened to them, yada, yada, yada.

      2. Your website data. What happened after someone clicked on your email links?

      3. Your company cross-channel outcomes data. Multi-channel customer purchase behavior, customer lifetime value. You know, Analysis Ninja territory!

This means that the key enabler of gorgeous analysis is your ability to ensure that for #1 your email campaigns are tagged correctly (with tracking parameters), for #2 your website landing pages are tagged completely (with the correct JavaScript tag) and finally, for #3 you’ve thought of the primary key that you need to pass into your backend database.

Do that, and you’ve met the minimum requirements and the possibility of achieving awesomeness.

Ready for the best email marketing campaign metrics?

Macro Perspective: End to End means ABO.

For everything you do it is important to measure your effectiveness of all three phases of your effort:

      Acquisition. Behavior. Outcomes.

ABO. I’ve found it increasingly easy to use the ABO acronym to ensure that I’m thinking end to end.

If your measurement effort is missing one of those three, you’ll be less successful than you deserve to be. So for our email campaign analysis let’s look at metrics using that framework.

Optimal Acquisition Email Metrics.

You’re trying to measure how well you are doing to: Send emails. Get people to read them. Click.

The key metrics you’ll measure will typically come from your email service provider (so make sure you check they can provide these metrics before you sign up and fork over the cash!).

A secondary thing would be to ensure, or your mom will really be mad, that *every* link in your email is tagged with campaign tracking parameters your web analytics solution needs (without this you can’t do the B and O analysis). Most good email providers will do this automatically for whatever web analytics tool you use. Here’s a post detailing vendors and process for Google Analytics: Tips for Tracking Email Marketing Campaigns.

Beyond the standard tactical stuff like number of emails sent etc., here are the metrics I’d recommend to analyze your acquisition greatness:

      Delivery rate = (# of emails sent – # of bounce backs) / # of emails sent   

This is your bread-and-butter outcome metric when it comes to your campaign; it answers the following simple question: did we stand a chance at success? Note that the increasing use of junk and spam boxes means that bounce backs are not the cleanest way to measure deliverability. The emails might have just ended up in the junk email box where they never stood a chance of being opened. But life and lemons and lemonade. Right?

[Bonus, from Yahoo!: What can I do to ensure that my email goes to the right folder?]

[Update: John Duffy, in comments below, has a great tip: Segment delivery rates by Email Service Provider (ESP). Try different ones every now and then and see which one works best.]

Up next, the excitement of the first engagement:

      Open rate = # of emails opened / # of emails delivered    

Allow me to rush and point out that this metric is usually just directionally accurate. Most email programs now have preview panes that typically block images and scripts (Outlook, Thunderbird, Gmail, everyone), and default settings prevent data collection due to concerns about viruses. That is okay. Over time compare rotten apples to rotten apples. Even if only directional, this metric is of value, for example in helping you understand how effective the single most impactful thing in your email campaign is: the subject line (no not the offer!).

And now to the reason we actually sent the emails:

      Click-to-deliver rate (CTDR) = # of clicks / # of emails delivered

IMHO this is a key measure of the quality of your email list, and of the effectiveness and relevance of your message. Segmenting this metric is really powerful. You can learn whether text messages or messages with images get a higher CTDR. You can compare customers in California, Idaho, and Florida; new and existing customers; or various demographics, etc., etc., etc. This should drive aggressive experimentation of email content / offers / targeting / every facet by your team.

Finally let’s not forget a very, very important signal of our email marketing effectiveness:

      Subscriber retention rate = # subscribers – bounce backs – unsubscribes / # subscribers

How much do you stink? This is perhaps as strategic an analysis as you could do for your email campaigns. Here you are measuring both the technical effectiveness of your email campaigns over time (reducing bounce backs) and the relevance of your messages and the targeting of the same (reducing unsubscribes). Measure retention rate over time in aggregate – or for optimal health, segment retention rate – and measure it for the various objectives you have set for your email marketing program.

Four simple metrics that help you understand how effective you are in the acquisition stage of your email marketing.

But there is more…

Optimal (Website) Behavior Email Metrics.

Analysts will almost always measure emails sent, and will often measure some type conversion signal. But it is rare that we obsess about what happens between the email click and the possible conversion. Did the website deliver the goods? What worked in terms of website content and customer behavior? In your analysis, don’t skip this part because what happens after that email click will either deliver high conversions or kill the most valuable offer you’ve ever sent.

The challenge is to figure out what behavior to track.

Here would be an obvious choice:

      Bounce Rate = # of email campaign visits with a single Page View / # of email campaign visits

Simple reason: “Never let your campaigns write checks that your website cannot cash.” That’s really what you are measuring. Are your landing pages delivering on the promise you made in the email campaign? It does not matter if you have a 100 percent response rate on your email campaign if the website Bounce Rate for the campaign is 99 percent. This metric helps you find opportunities for immediate improvement – such as pages and calls to action you should test, and content that fails to deliver.

You’re still not safe even if the bounce rate is 0%, but at least now you are in the game to get valuable business outcomes.

For non-ecommerce content based websites here’s another obvious behavior metric:

      Depth of Visit = percent of email campaign visits that last longer than xx pages

The xx in the definition forces you to think up front and plan for site behavior before you send an email blast and, of course, measure your performance against that goal. The assumption here is that more page views is more ad views or more future visits or deeper brand impact or… think that through up front as well.

For ecommerce websites here’s a behavior metric:

      Actions Completed = percent of email campaign visits that watched a video

Watched a video or searched for a store location or added to cart or reached the Technical Specs page or customized a car or rated our products or logged in using Facebook or Google+ to get product recommendations based their friends circle or played a treasure hunt… so many other things. I’m using the term action generically. You can use Event Tracking or Custom Variables to capture customer behavior that creates value for your business (online or offline).

Depending on your type of website, other metrics will also be important to you. Bottom-line: do not ignore web behavior; it is an important part of measuring your email campaign success.

Optimal Outcomes Email Metrics.

I’ve declared, frequently, that I have OOD (Outcomes Obsession Disorder). If you are not reporting deeply on the outcomes from *every* digital effort…. why even exist?

With our email campaigns, that’s even more true. If you spend money on acquiring traffic you’d better be delivering 50x (or whatever low bar you want to set for yourself :) return on investment.

You can start with the most overused metric in web analytics:

      Macro Conversion Rate = # of One Big Thing / # of email campaign visits

One Big Thing could be ecommerce sales orders, the number of leads received, downloads of your product, sign up for trials, people volunteering to protest, donations to your church, new accounts opened for your new social network… anything. In my earlier posts you might have seen me refer to this as “your Macro Conversion,” the most important thing to your business when you use email marketing.

Oh, and don’t forget to segment it like crazy. Be ruthless at identifying causes for low performance.

It might seem logical that you’ll also measure the second most overused web metric:

      Average Revenue per Email Sent = total revenue / # of emails sent

I am very fond of this metric because it stresses productivity. Notice that you can play some really nice games by substituting the denominator with # of emails read or # of emails delivered. But my perspective is, why settle for lower standards? Isn’t the point of our email campaign to get maximum value? So, let’s set a high bar. Using # of emails sent will force quality in your email campaigns because it will mandate that the list be very clean and targeted. It is important in your journey as an analyst or marketer that you influence the positive behavior of your company by choosing your metric definitions wisely. 

[Here’s another example of using a metric’s definition to change behavior: Excellent Analytics Tip #16: Brand Evangelists Index]

But why measure only 2% of the outcomes on your website? Why not measure the complete business value delivered by your email campaign visitors? Measure the macro AND micro conversions:

      Micro Conversion Rate = # of Many Small Things / # of email campaign visits

Many Small Things are other things your campaign traffic can accomplish on your website. Write product reviews, follow you on Twitter / Facebook / Flavor of the Month, download your Android app, sign up for an email newsletter (see the circular reference? :)), sign a petition, sign up for an affiliate account, and so many things that add value to your business. More ideas here and here.

Now you’ve measured 100% of the business value delivered by your email marketing efforts.

And speaking of that… if I could only measure only one metric for my email campaigns, this is it:

      Average Economic Value per Email Sent = total economic value / # of emails sent

Economic value is our obsession. Not revenue. It encompasses macro plus micro conversions. It will ensure your job promotion and a happy marriage. I won’t go into too much detail about how to do that. If you are interested in a promotion (and a happy marriage!) just hop over here: Excellent Analytics Tips #19: Identify Website Goal [Economic] Values.

Finally for those of you measuring revenue, and think of yourselves as Analysis Ninjas, you are measuring this delightful metric (don’t let me down!):

      Profitability = (revenue generated – campaign cost – cost of goods sold) / # of emails sent

Most email Marketers and Analysts will measure revenue and order size and other such obvious metrics. But we rarely spend time measuring profitability. In my experience that is normally because it is hard to find data related to true costs. In the case of email, that means the cost of the campaign (the cost of buying the list, sending the email, using resources, and so forth), as well as the cost of creating the products and services (cost of goods sold).

The difficulty in getting the numbers (bug Finance!) should not stop you from trying to measure Profitability along with Revenue. It is very easy to imagine that the most successful email campaign in the history of your company could very well cause bankruptcy (Costs greater than Revenue), and campaigns that look like mediocre performers could be most profitable. See why this is important?

Email marketing works. You just need to resist the temptation to abuse your customers. Don’t pre-select “sign up for our email” boxes and have an extra step to confirm opt-ins; always think of the customer benefit and not just what you will gain. You should have a big, clear, one-click unsubscribe link on top of every email. But most of all, you need to be relevant. That’s really all it takes: treating your customers exactly as you would like to be treated.

Oh, and computing the above metrics to optimize your email marketing. That’s it.

Okay it’s your turn now.

Is email a key part of your marketing portfolio? What metrics do you use to measure success of your email campaigns? Which of the above 12 is your favorite? Which is the most overrated? Is there a #13 (or more) that you would like to add?

Please share your perspective via comments.

Thanks.

Email Marketing: Campaign Analysis, Metrics, Best Practices is a post from: Occam's Razor by Avinash Kaushik

I recently did a webinar for the Search Engine Strategies conference (I am doing the opening conference keynote at SES London and SES New York) and my Market Motive co-faculty member Greg Jarboe sent me this KPI via email:

"Your webcast was a big success. Your KPI questions per attendee was off the chart!"

I don't know why I had not thought of this wonderful KPI. So much better than # of attendees.

As always though context is king.

It could be a good thing ("you were great, engaged the audience") or a not such a good thing ("no one understood a thing you were saying, hence so many questions"). Only upon reading the actual questions could I figure out which case it was (mercifully case #1 for me!).

End of a minor web analytics lesson on going beyond obvious metrics and never, ever, never forgetting context.

Back to our story. . . an hour is too short a time to answer all the questions (even in a webinar just focused on attendee questions). So here is a small selection from the 80 questions I could not answer in the wide ranging webinar.

We will cover measuring success of SEO efforts on one web page, how to do search engine optimization for b2b websites, how to rank for highly saturated industries / categories / keywords, and which competitive intelligence tools do I use for search program optimization (and targeting display ads using search data!).

I hope you all find the answers to be of value.

#1. How do you measure SEO performance on a page level? I'd like to know how well my seo efforts for a particular pages have performed.

Every measurement question should start by taking one step back and thinking of goals.

In this case here are some obvious ones:

Now it is not hard to figure out how to measure performance! [Before you do any kind of measurement please consider going through the above exercise. It is simple, effective and works like a charm - not to mention allows to get going faster.]

Before you analyze do one small thing. Log into the Advanced Segmentation tool in your web analytics tool. Create a segment for Organic Search traffic. Sources -> Contain -> Google, Bing, Yahoo! etc. Save. Another way to cheat at this is to simply use Medium Matches Exactly Organic.

If your web analytics tool requires you to call the vendor to set up advanced segments, or re-tag your site to get segments, then switch. There are too many better choices in the market.

Now log into whatever web analytics tool you use and drill down to the specific page you are interested in ("Top Pages Report" / "Content Title Report" etc). Apply the Organic Search segment to that report (in Google Analytics segments are on the top right, in other tools please refer to user manual).

More traffic, not that hard. Stretch the time period to six months (or some large date range – remember SEO takes time). What do you see? Nice and gradual up and to the right trends. Do your happy dance! Something's working. Now look down at the table under the graph that shows traffic sources. If you did your segment correctly you'll see just the search engines and how much each is contributing to your overall traffic. Does the distribution match your goals?

Ready for the next step? Click on Referring Keywords and now you are looking just at the keywords bringing traffic to this page. Do the keywords match the intent of the page? Do they contain keywords you were specifically targeting? No? Why not? On the other hand what are the surprises? Is the customer intent contained in the keywords telling you how to change / improve the page? Do it!

Indexing. . . I am a big fan of Google's Webmaster Tools because of the wealth of data available, use this free resource (no matter if you are a SEO or not). Bing's Webmaster Tools have also evolved a ton, please claim your account right away and dive in. [I have not had much fun with Yahoo!'s web master resources.] In either tool you are looking for how well your site is indexed (report: Your site on the web -> Top search queries -> Impressions), how well your pages are indexed and, my absolute favorite, which keywords your search results are showing up. You are checking to see if:

1. the pages you are targeting are being indexed frequently and

2. if your site is showing up for the keywords you were targeting.

You want validation that you are showing up for the set of keywords you are optimizing for (above) and that your pages are being recorded as being optimized for the right keywords (above the above :).

Success. . . I humbly believe that the biggest mistake most of us doing SEO make is that we are far too obsessed with ranking and meta this and that and how to work back algorithms etc etc. We should focus more on what was the business impact of our SEO efforts.

So in this context go back to your page report (from step 1 where you applied the organic segment) and look at the $Index [which is: (goal value + e-commerce revenue) / unique views of the page you are analyzing]. That is a crude measure how how efficient your page is being at converting. Of course look at our favorite metric bounce rate by keyword (that tells you if you can get people to give you one solitary click, the most primitive measure of SEO success).

If you truly want to kick it up a notch as a SEO please please please go to the Goal and Ecommerce / Conversions reports and apply your organic segment, stretch the time period, and report (aggressively) how well your SEO efforts are delivering value to the business.

Do it at a overall level, do it by country, do it by search engine, do it by specific keywords you were targeting. . . . and take two minutes to straighten your clothes because a new level of love and praise are about to be dumped on you by your company / client!

[Does the above seem like a lot of work even if it is straight forward? It is. I know we look for short cuts. There is no such thing in real life. But if you are willing to put in a little bit of sweat equity then you'll stand miles apart from your SEO competitors. Not a bad trade off, right?]

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#2. Is there a fundamental difference in SEO strategies for business-to-business sites vs consumer focused ones?

[It is worth pointing out I am not a hard core SEO, that would be Todd Malicoat, I just play one one TV! Think of below as my personal lessons from the front-line of doing this work to the extent my humble skills allow.]

The basic techniques you use to do search engine optimization between b2b and b2c do not change all that much.

Ok All that is the same, no matter if you are a b2b, b2c, b2a (business to aliens, yes they do exist!). Do all that first to make sure you are not coming to play the super bowl naked.

Here are a few things that are different with b2b. . . . .

* Some very effective SEO strategies like allowing users to add reviews and comments and extend the scope of the page do not work as well with b2b as it is a differ net type of engagement and experience with your customers. Well don't give up. You have many many white papers, though leadership papers, webinars, Big B2B Association publications where you contributed and more locked up in pdf or, much worse, behind a forced "give me your login" / "create a account" page. I am going to give you a false email, why not just give me the content, AND let the search engine index it efficiently after all you want people to consume the content.

Did I say already content is king?

* One of the most common issues with b2b websites is that they often have a very specific understanding of their space when it comes to how their potential customers search for information. This results in not speaking the same language (say keywords) as their customers. When I work with b2b websites I spend a lot of time in the AdWords Keywords Tool, Insights for Search, Compete etc analyzing keywords and search behavior in my category. This knowledge goes back into re-doing content, urls etc.

This is of course a good method for b2c as well, but it is significantly more important for b2b.

* Start a conversation. There will likely be a lot fewer individuals talking about you / your industry, a lot fewer tweeting and expressing their love (or hate). I get it. But conversation on your site and away from your site is key (obvious fact). Why not host a user forum on your website for current and future customers to come together and share their thoughts / ideas / complaints / rave about your competitors (scared?)? Why not seek out the few people who do talk about the industry on twitter and engage with them? Why not start a YouTube channel with a series of how-to videos? Why not, : ), start a blog? Not just to highlight your own pomposity and press releases but to really share and lift your industry (not just your company)? Why not become the destination for industry professional?

So few people in the b2b space bother to start conversations, why not use that to your advantage? Even if you can hook 100 people is that not more than worth it?

Three small things that I would prioritize higher when I work with b2b sites.

What do you do differently when it comes to your b2b clients?

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#3. When trying to help your rank in search engines. . . when you are in a saturated industry like health or travel insurance – how does the approach change or differ?

Two words: Long Tail!

When you say saturated most people mean that for the "top" keywords they are interested in there is too much competition. For example: "hotels in las vegas", "cheap health insurance" etc.

When there are a lot of players in the field it can be difficult to show up for the "head terms", especially if there are some strong players in the field. In these cases I have had a very positive experience focusing not on the head terms (terms for which there is a lot of traffic) but rather focusing on the long tail (usually key phrases that individually have little traffic but collectively these key phrases can deliver a ton of traffic).

So, if relevant for your business, try to rank for "california health insurance plans" or "california individual health plans" etc. Key phrases (not just words) that each have much less competition (and will likely deliver more relevant audiences).

You can use various keyword tools out there to identify these key phrases and then adapt your SEO strategy (pages, content, urls, etc) to focus on them. One way I use is to just type in competitor urls into AdWords Keyword Tool and then research what is working for them and adapt my strategy.

Targeting the long tail with SEO can be a bunch of work, hence I have recommended in the past that one effective and cheap way is to use paid search to monetize the long tail. But I can tell you from experience that it works. For example for this blog the top 10 (head) keywords bring in something like 5k visits and the long tail (around 25k keywords) bring close to 34k visits. All organic (I am not rich enough to afford paid search!).

One more bonus tip: Leverage "universal search".

Videos, pictures, downloads, offers, buttons, maps, uploaded menus, coupons, and on and on and on.

When you search for many terms relevant to me you'll see videos pop up, my book (uploaded into Google book search) show up with preview thumbnails, some of my flickr images and my twitter account and so on and so forth. For many of these searches I don't rank #1. But man do those listings (when triggered by the search engine's algorithms) stand out and grab the Searcher's attention. Often for competitor or big paid web analytics tool queries where I have a snowball's chance in a hot place of standing out.

It is ironic that most big companies (with so many assets to leverage) are pretty bad at this. So you win! :)

Also Google (I work there) Local Business Center is really good: http://www.google.com/local/add If you are a small business then this is one more important arrow to have in your quiver!

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#4. Can you look at your competitors sites in the analysis tools you have discussed?

Yes.

But first. . . . it is important to realize that you need to have two skills before you look at competitive intelligence tools:

If you meet the above two requirements. . . . .

I love using competitive intelligence tools because they give me a perspective and context that is simply missing from Omniture or WebTrends or CoreMetrics or the clickstream tools.

In the search context here are some of my favorite tools and what I use them for.

Insights for Search:

I adore I4S because it is perhaps the most comprehensive "database of intentions" thanks to providing us all with access to worldwide Google organic search data.

Use it to understand the latest trends in your category. For example: "How is interest in the computer security category (All Categories -> Computers & Electronics -> Computer Security) and what are the top 100 search terms and the fastest rising brand names / products / searches in that category?"

Use it to identify opportunities. "What states do people search for credit cards the most? What states do people search for Visa credit cards?" Oh look the states with really high credit card searches don't have really high visa card searches, maybe we should do some offline advertising!

Use it to time your campaigns. "When should I have started SEO and PPC campaigns for Italy Tours 2010?" In April 2009!! That's when people first started looking for them. Now go plan for 2011.

Helpful article: How to use Google Insights for Search.

Ad Planner:

This wonderful tool is really built to help you do better display advertising. You log in and you have the delightful ability to do demographic (male, female, age, education, income etc) and psychographic (baby boomers, extreme sports fan, household decision makers, luxury goods consumers, moms etc) segmentation. You can hone in precisely which websites most likely contain your desired audiences. Show them relevant ads and get clicks!

But in the search context there are two things that you use this tool for.

Type in any website you want, expedia.com in my case, and checkout the site and search affinity data:

[If you don't see the image above, turn off your ad blocker.]

"The affinity score estimates how many times more likely you are to reach an audience who visits a specific site or searches for specific keywords versus an audience on the internet overall." Source.

Sweet 'eh?

Second, click on the tab that says Search by Audience and then the Keywords Searched button and now you have an ability to use search behavior to identify audience pools.

To use the examples of my beloved Indianapolis Colts (go Colts!!!). . . . I have an ability to type in a bunch of related keywords (the tool suggests most used ones) and find out which websites are most likely to be visited by people who search for these keywords:

[If you don't see the image above, turn off your ad blocker.]

At the top are keywords I typed. On the bottom are most commonly searched keywords, I can choose these if I want.

I hit ok and then sort by Comp Index, to ensure I sort the data by the highest audience concentration (audience that searches for all things Colts in this case).

I can use this search and web data to identify where audience I am most interested in exists. I can use it to find out the keyword data for those sites. I can use this to identify sizes (visitors, page views etc) of those websites.

Nice right? Actionable too!

Helpful article: How to use Google Ad Planner.

Compete.Com:

Compete is a paid tool (and it only contains US data). I really love using it because of the wealth of search data it can provide, at an affordable prices.

[I have had a complimentary Pro account for the longest time thanks to the nice people from Compete, that might bias my opinion. Other than that I have no other affiliation with Compete.]

In context of Search I use the data for. . .

1. Identifying what are the top referring keywords for any site that I am interested in:

Above data for www.clickequations.com (the paid search analytics company I am on the advisory board of). Of course when you log in with a paid account you would see rest of the data like paid and natural search split for each keyword and time and what not.

Craig will not be happy that he ranks only #12 on the keyword list! :)

I can either use this data to go after keywords that are not currently referring traffic to ClickEquations (more for me!!) or I now know what keywords I need to target to take ClickEquations down in my quest for world domination! Ha!

See how focused you can be with data?

2. Identifying share of search for a keyword:

In this case I would like to own the pear fruit market, though at the moment I only own two trees. So I go into Compete to find who my current competition is (above exact match data for query "pears"). I can get lots of details about volume, paid and organic share, what percent of traffic comes to a site from that keyword, etc etc.

Now that I have a benchmark I can go about my super awesome kick butt SEO efforts and one way I know I am winning is to check this report in a month or two (or three weeks after whenever I think I am done). If I show up here I know I am having a impact.

These are just three of the many tools I use. There are a whole lot out there that sometimes give you similar data to the above three, or often give you a lot more. Just remember that there is a lot you can learn from what is going on in your ecosystem and at your competitors.

Ok now your turn.

Got a couple tips you want to share with us about how best to do SEO for B2B sites? How would you measure success of SEO efforts spent on a page on your website? Would you use any of the four ideas I have suggested? Care to comment on how to do SEO for crowded industries or for keyword categories where one or two players seem to dominate? What is your favorite search competitive intelligence tool?

Please share your tips / best practices / comments / critique.

Thank you.

Dear Avinash: Search / SEO Metrics & Analytics Questions + Answers is a post from: Occam's Razor by Avinash Kaushik

Sure your organization could be to blame (org structure, bad boss, ineffectual team). Perhaps your client did not provide you with the all important Web Analytics Measurement Model. Maybe something else (surprisingly, excuses are not all that hard to find when you are looking for them :)).

Even with all that, I think the problem is you.

You: The person responsible for "providing data" / "analyzing metrics" / "reporting."

You are the problem because you, mesmerized by the enchantress that is web data, are reporting "crappy" metrics.

Since crappy sounds bad, let's just say you are reporting super lame metrics. (Sounds better right?)

Oh, and I don't mean reporting super, super lame metrics like % of Exits from a Page. I think we've realized that is absolutely useless for unstructured experiences (most web behavior).

I mean the metrics contained in 99% of web analytics reports: Visits, Page Views, or Time on Site. I mean the metrics contained in 99% of digital advertising reports: Impressions, Clicks, or Emails Sent. I mean Social Media campaign metrics like Number of Followers (or Likes), Video Views, or (just kill me now!) # of Press Reports.

Surprised to find the bedrock of your (and my) existence classified as super lame?

They are. And it is time we accept that. How else will we be recognized as the magnificent creators of business value, rather than people who whine about data (or worse, much, much worse, JavaScript tags and s.props and custom variables)?

These metrics are super lame because. . . .

1. They are supremely tactical.

There is nothing hugely business bottom-line impacting we can learn from them. Okay we got 17 million fans on Facebook. That was success? People saw 3.2789 pages last month and it was 3.3592 this month. Okay, now what? What does the number of press reports or views of our video actually tell us?

2. They simply report, usually, "top of the funnel" activity.

Getting people to just show up to our digital existence or just sending them email is not the point of what we do. Our goal as a non-profit is to ensure we get more people to our protest, more donations, more leads for places we can add value to. Our Goal as a B2B business is to connect a high value prospect to an Authorized Dealer / Rep, to try to revolutionize our marketing by going after the search long tail with AdWords, and more. It is not hard to imagine what a B2C site is trying to do.

So why is our reporting dominated by reporting of visits and time on site and impressions and all that? It's work, but what about what happens from that work?

3. They require too much inference.

When you present a large number of Visits or Page Views or Followers, what you are essentially inferring is that more is better. You are inferring something that is not there: success. Or you are hypothesizing, when you report that data, that these large numbers mean that customers are happy and business is successful. I believe it is dangerous to make that inference. Why not seek direct success indicators? Even for our branding campaigns?

4. They are "one night stand" focused.

This is perhaps the thing that makes me hopping mad and scream like a banshee. In the early days of the web it was cute to just get someone to come to the site and most stuff happened within one session / visit.

By 2011 as web experiences have become richer and more frequently and more complex I am so mad that our life is not dominated by pan-session metrics. That our campaigns are not focused on Visitors (not just Visits!), longer term understanding of people and customers (not just entrances and bounces!), customer lifetime value (and not just single visit conversions!), and all that stuff that is mandatory for the long term success of any company.

Frustrating when you think about it, right?

The bottom-line is that while they are all "standard" metrics, they measure tactical top of the funnel activity requiring too much inference and cause us to simply chase one night stands. Let us not make them the center of our web analytics existence.

Let us banish them to the land of "oh yes that was cute when our intern measured that when we first got a web analytics tool, but don't be silly, our dashboards don't contain that anymore."

Let us move to the land of the "OMG that was such high speed from data to business action that I think I might have experienced a datagasm!" I.E. super awesome land.

Super awesome metrics. . . .

~ Force strategic analysis of metrics that contain data material to the business bottom-line!

~ They are infused with direct customer voice so we don't need to infer and look at data with our own biases (just let them tell us!).

~ Make us look at behavior across sessions to encourage a business focus on long term value (customer and business).

~ Due to their inherent nature in most cases they make it very obvious that the performance you are looking at is good or bad. It is hard to get to this, but it is so magical!

Salivating? Can't wait to hop into bed with 'em?

Now here is the amazing thing. You know which metrics I am referring to. I doubt I am going to shock you below. The problem is reaching datagasms requires some hard work (surprise!); it requires some fine caressing of the data; it requires going beyond the standard report in Google Analytics.

Here are some metrics I consider super awesome. . . .

Visitor Loyalty & Visitor Recency.

Notice my immediate focus on Visitors. Cookie issues be dammed (use first party okay?), this is the best we can do for now, use it! I love Loyalty and Recency because they measure what actually matter (repeated frequent visits by an individual. Do not be tempted by the not very useful % Repeat Visits).

For content sites (NY Times) it is focusing on a longer term relationship (and that might even mean not focusing on Page views per visits and reducing the number of ads on a site!).

For non-profits (The Smile Train) it means focusing on creating a connection that causes future donations.

For ecommerce sites (like Amazon) it means focusing on an experience to cause the next 20 purchases, or (like TurboTax) focusing on such a delight this March that I'll be back next March.

I love these metrics. When you focus on their performance it will force you to materially change your website experience, customer relationships and business value.

Why focus on conversion rates from your AdWords or DoubleClick campaigns? What was the quality of that traffic beyond the one night stand? Visitor Loyalty, bam!

[Learn ins and outs and how-to: Visitor Loyalty, Recency, Length & Depth]

Days & Visits to Outcome.

In some sense an enabler of the above. If your business leaders continue to want one night stand data then report these metrics (standard in Google Analytics and WebTrends and every tool).

Two different visits shows the length and depth of an experience leading up to an outcome (typically a ecommerce conversion, but could just as well be a lead, a donation, or any outcome you desire).

It helps force changes in customer experience (why just have a Search Travel Deals now and not a Save Travel Deals You Found So That We Can Email You When The Price Goes Up Or Down button?), it forces you to worry a out multi-channel campaign attribution (or not!), and more such delightful outcomes.

Strictly speaking, it is a pan-session metric (as are the above two), but I still think of it as a single outcome focused. So do this, migrate to above.

[Learn ins and outs and how-to: Analytics Tip #6: Measure Days & Visits to Purchase]

Conversation Rate.

Social Media is all the rage. Tweeting, Facebooking, Blogging, YouTubing. For most brands that, thus far, has simply translated to: "Oh we shout at people via TV, Magazines, Radio etc, what is the best way to shout via these new channels."

Sad. Heartbreaking. Your inner happy child just died.

Fear not. (She has nine lives!)

The true essence of social media, if you want to do it right, is the ability to reach your audience directly (no intermediaries) and have a relationship with them. If you want it. The essence of that relationship, one key facet, is the ability to spark and participate in conversations.

So why measure Followers and Likes and Posts? Why not measure Conversation Rate? For blogs: # of reader comments per post. For Twitter: # of replies sent per day, # of replies received per day. For Facebook: % Feedback.

If you are conversing, find out who you are conversing with. Is it adding any value to your brand; should you be doing it more? The roads to all those interesting questions, and incentivizing the right behavior by your company, starts with this super awesome metric.

[Learn ins and outs and how-to: Social Media Analytics: Quantitative & Qualitative Metrics]

Task Completion Rate.

The sub-title of Web Analytics 2.0 is: The Art of Online Accountability & Science of Customer Centricity.

That second part? That's why I love Task Completion Rate as a metric.

I see Analysts and Marketers and HiPPOs (of all sizes) torture the clickstream data, make leaps of faith, and start their recommendations with: "I think." Sub-optimal on so many fronts.

Don't think. Just ask.

Task Completion Rate is the % of people who come to your website who answer yes to this question: "Were you able to complete the task you came to this website to do?"

Combine that with the Primary Purpose question ("Why are you here?") and you have a gold mine of fantastic data. Why people come, how much you let them down. No guessing. No making stuff up. No inferring things from Time on Page or % Exits!

Use the customer voice, hit people on the head with it, reward those who work hard to improve the customer experience (based on, I can't stress this enough, customer identified pain and Omniture or Yahoo! Analytics guessed), and reassign those who don't. I call that #winning.

[Learn ins and outs and how-to: The Three Greatest Survey Questions Ever]

Economic Value.

What can I tell you about Economic Value that you don't already know? Oh, after Task Completion Rate, it is the single greatest gift a Digital Anybody can give to their boss / company / mom / angels.

It is the total $$$s (or Pesos or Rupees or Kroner) in Economic Value added to your business bottom-line by visitors to your website completing all the possible Macro and Micro Conversions.

Our inability to quantify the value of our digital existence is the single biggest reason for the sad undesirable level of appreciation. We just focus on the 2% conversion rate (orders or leads) value added. The thing we call Revenue. We forget all the other Micro Conversions. Absolutely criminal.

If your web analytics tool does not allow you (in five minutes without touching the JavaScript tag) to set up goals and goal values, then ditch it. You are wasting everyone's time; your mom is not proud of you.

Once you identify the Economic Value of your website you'll be able to clearly articulate: 1. All the jobs your website is doing. 2. True and complete value of those jobs. 3. Identify holistic value of your digital marketing campaigns. 4. Identify where you are falling short and where you are glorious. 5. Make your mom proud.

How is any metric that can help you do that not super awesome?

[Learn ins and outs and how-to: Excellent Analytics Tip #19: Identify Economic Value]

Five simple, effective super awesome metrics.

It is not that Visits and Page Views and Impressions and Fans and Clicks are useless. They are not. They are just not worthy of all the attention you give them. They are not. . . super awesome.

In the small chance that a picture is worth a thousand words, why take chances :), here's a slide from my recent keynote. . .

So you can see there are many more super lame and super awesome metrics than mentioned in this article.

My hope is that the specific examples outlined help you find specific examples you can start your own revolution with.

My wish that you'll look not at the specific examples above, but more why I choose them for my Super Awesome list. The thinking behind the decision, the insights and value I desire. If you get that you'll be prepared regardless of what your company is up to on the web, regardless of what web analytics tool you are using.

. . . . and one more thing

I'll leave you with a practical example of how evolution from super lame to super awesome metrics (with a just regular awesome stage in between) looks like.

This is a real example of trying to measure how advertising done by a company is performing. They start at the normal place everyone else does; Clicks and CPC's. The company impact is just keeping costs low. I call this the Toddler stage (who, let me point out as a father of a small child are super duper awesome and I don't mean to imply anything else), but it is, super lame. How are you winning? How deep are your insights?

Then we mature to the regular awesome stage. The focus is on Conversions (yea!!!) and Revenue. Company impact is maximizing Revenue Per Acquisition. The Rockin' Teen stage. Good. But not great.

[Click on the image above for a higher resolution version. The slide above is from my 60 min. webinar on YouTube: Agile, Outcomes Driven, Digital Marketing]

The super awesome stage is the last one. Ninja's live there. Measuring complete Economic Value to the business as the company impact. Kissably super awesome.

Consider this. Just look at the colored rows. You could have made a decision about which campaign was working better at each stage. But at only one would you have made the right decision and, because of how important that number is, earned a promotion.

That's what I'm talking about!!

Being super awesome.

You can create the above evolution for your company for any metric you desire, for any outcome that is expected by your business. And now you know how.

Good luck!

As always it is your turn now.

Surely you have your personal favorite super lame metrics. . . what are they? Why do you think they are "super lame" (or to be polite, less than useful)? And which metrics, if any, do you consider super awesome? Where have you fully focused your attention in order to identify valuable insights that have actually been actioned? What do you love?

Please share via comments.

Thanks.

Your Web Metrics: Super Lame or Super Awesome? is a post from: Occam's Razor by Avinash Kaushik