It is sad that we spend so little time on brand analysis, primarily because 1. there is such little accountability to brand marketing and 2. it is such a strategic part of any business.

So let's fix that problem in this blog post. Let's become BFFs with a lovely hidden gem that helps you leverage one of the largest source of data on the planet to understand the strength of your brand over time.

[Bonus One: Read: Brand Measurement: Analytics & Metrics for Branding Campaigns]

There are many different tools, both online and offline, that measure the elusive metric called brand strength. It's elusive because brand strength is, at its core deeply qualitative and none of us measurement types can really see inside your hearts and draw charts of the evolution of what's in your heart over time. So we use proxies, and we do the best we can.

One of my favorite tools to do that is Insights for Search which provides an incredible way to see how interest in your brand has grown over time and whether you are strengthening your brand over time.

Brand Strength via Unaided Brand Recall

Insights for Search sits on top of all of Google's organic search data from around the world. I believe it is one of the best possible ways to measure what humanity is thinking, and telling us via the queries they run on Google. I love using this tool to measure "unaided brand recall ."

The stronger your unaided brand recall, the more likely people recognize you, think of you, consider you when they need what you have to offer. I never search for a sports car. I search for the "best Nissan sports car."

You increase unaided brand recall by creating great products (its not called a tablet, they are all called iPads), delivering fantastic service ("their return process is as good as Zappos"), and of course online and offline advertising.

Sometimes it all works together. Recently I saw a TV ad by eBay for designer jeans. I typed designer jeans into Google (for that is what people do when they watch TV). The first ad was for Amazon. No eBay PPC ad or SEO listing showed up. Clever Amazon tying its online advertising with a competitor's offline advertising. Now I search for "amazon designer jeans." :)

For your brand Insights for Search provides an incredible way to see how your brand has grown over time, and whether you are strengthening your brand. If you strengthen it, you drive people to look for you (and not your competitors), and you can capture them more easily using Search (Organic or Paid). Brand queries, obviously, also convert better.

Leveraging Google Insights for Search

So over time, how's your brand doing?

Step 1: Type your brand name, and your direct competitor, into the Search Terms area of Insights for Search .

Step 2: Pick the right country, time period, and -this is important – high-level category in which your brand belongs.

Step 3: Click Search.

Step 4: In the middle of the resulting report you'll see a trend that looks like this:

This shows the number of searches for your brand, relative to the total number of searches done on Google over time (for the geographic region and time period you've chosen). The data you see is normalized and presented on a scale from 0-100.

This is interesting. You can see that eBay (green) rose for a while but has been essentially flat. During the same time period Walmart (red), Amazon (blue) and Target (orange) have done exceptionally well.

But (as every Analysis Ninja knows) competitive context (above) is good, but industry/category context is even better! So…

Step 5: Click on the tab that reads "Growth relative to the Shopping category" and boom!

This is a lot more interesting. [Click on the above image for a higher resolution version.]

The graph shows the change over time, starting in Jan 2004. On the right axis you can see how each brand has grown over time in terms of its brand strength, in context of the growth of the Shopping category.

It is pretty amazing to see that even as eBay has massively ramped up its offline (including big TV) advertising, at least in this context its growth (unaided brand recall) has actually lagged its competitors quite a bit.

eBay's green line is very close the performance of the category (and you'll see that often at peaks in the shopping category queries, eBay actually does worse starting holiday season 2009).

The tussle between Wal-Mart and Target is interesting. It used to be cat and mouse, but over the last three years Wal-Mart is clearly leaving Target in the dust (just look at that spike during this past holiday season, omg!).

Amazon is an interesting example. It used to fall behind lag the other two in brand queries, but you can see how starting late 2009 (bad year for Target in this context) Amazon overtook Target and now (2011, 2012) is casting a big shadow over Target. For a real appreciation of how amazing this accomplishment is, consider the TV ads Target runs, the number of Saturday mailers it sends out, the number of billboards it buys, etc.

The above trend lines, when viewed in context of your category, helps you understand how well you are doing in terms of increasing your brand strength.

Do this analysis for your company.

Brand strength is important because when I type "ebay big screen tv" in the search field, I essentially eliminate everyone else. If I type in just "big screen tv", I'm going to Amazon (they just rank so well).

Brand strength is built over time using online and offline advertising. Brand strength is not built by playing a "let's bid on just our brand terms" strategy, but by complementing that strategy with a super-smart organic and paid "let's capture all our brand and category terms" strategy.

[Bonus Two: Video: Enhancing Brand Strength (and Avoiding Brand Destruction) via Social Media]

"Timing The Market"

One thing about Amazon looked particularly interesting to me.

You'll notice that Amazon's Christmas peak comes a few weeks after Walmart and Target. See if you can notice it here:

For Walmart (red) and Target (orange) this is not surprising. These are traditional retailers who have a fixed calendar of marketing execution with an overwhelming emphasis on Thanksgiving. After that, things ramp down.

Traditional retailers often have a fixed multi-channel schedule based heavily on past traditional media plans with less flexibility in being able to incorporate real time odd trends on the web.

But look at Amazon (blue), keep an eye on the highlighted time period above and look at this:

Notice they hit their peak exactly at a time when the Shopping category hit its peak! +25% in the first image above and +37% in one immediately above.

Amazon does such a great job that their brand queries also get an extra spike during that time, from +413% to +525%. You have to hand it to the Marketing folks at Amazon. When their competitors are ramping down (perhaps due to their inflexibility), Amazon can read the market much better (notice Christmas 2010 as well) and are well placed (thanks to Paid and Organic Search strategies) to grab all these new people who are coming into the market to shop.

And precisely at that time both their large competitors are rapidly ramping down their spend! You would think that with actual stores they would ramp up during December because Amazon is at a disadvantage having to use shipping!

Here's the link that should take you directly to the analysis in the images you've seen in this post: http://goo.gl/JbUzK

#rockbranding

Data? Check. Actions?

So what can you do with this data? How can you go and destroy your competitors? :)

I've written a comprehensive post with very specific guidance on how to leverage Insights for Search to identify actions. Please check out that post here: Competitive Intelligence Analysis: Google Insights for Search

In context of the above findings, I would focus on trying to identify the geographic locations in which unaided brand recall is stronger for my competitor(s) compared to me. I would use online and offline brand marketing campaigns to shore up my brand strength.

I would also focus on the very bottom of the Insights for Search report where you are able to see the cluster of search queries most closely associated with a brand (on the left), and the most statistically significant rising terms (on the right). They are full of specific insights you can use to optimize your online search campaigns.

Please check out the blog post above for more detailed guidance.

Five Caveats!

Life would be so much better if we did not have to caveat everything. But, sadly the life of an Analyst is imperfect. :)

Here are some caveats to keep in mind when you do this analysis…

As Google Flu Trends has proven, online behavior is a very strong predictor of offline reality. I hope you'll do this analysis for your brand, get context from other data sources, and get your company to take very smart action in moving the dial on brand strength.

As always, it's your turn now.

How does your company measure brand strength/unaided brand recall currently? How cognizant are you of how your competitors are doing? Have you tried to use online data, like Insights for Search, to do this important analysis? What other caveats would you add to the four I've listed above when using this data?

Please share your experience, critique, examples, ideas and feedback via comments.

Thank you.

Excellent Analytics Tips #20: Measuring Digital "Brand Strength" is a post from: Occam's Razor by Avinash Kaushik

You know the drill: Measure. Find insights. Take action. (Or die trying.) Ascend to corporate heaven.

While there is a great deal of appreciation for the power of metrics/data, I've come to realize that Sr. Leaders don't quite appreciate the deep, and often corrosive, consequences of choosing metric x over metric y as a key performance indicator (KPI).

[Sidebar] A key performance indicator is a metric that helps you understand actual performance against preset business objectives. [/Sidebar]

The metric you choose communicates to your organization what's important to you (the POWERFUL person). It communicates to them how their personal success will be measured. That translates directly into what they prioritize when it comes to your digital initiatives.

Choose the right metric and they'll create the most glorious digital experience in the universe, the perfect acquisition campaign, the most amazing customer service channel. And they will shock you with the profits they deliver.

Choose the wrong one and they'll create self-serving, sub optimal, non-competitive, tear-inducing outcomes that will, slowly over time, bleed the business to death.

It really is that stark. Simply because it all comes down to the incentives you create.

Don't believe me?

Let's look at six corrosive metrics and their angelic twins, which illustrate this challenge – and magnificent opportunity – quite vividly.

1. Page Views vs. Visitor Loyalty

Is there anything easier than measuring Page Views? This metric has been in every tool since we started torturing web server logs to measure hits (!).

What does Page Views measure? It kinda sorta measures consumption. It is hard to know if a lot of Page Views per visit is a good thing ("The visitor loved our site so much that they read 23 pages of content!") or a bad thing ("Our site is so horrible that it took 23 pages for the visitor to find what they were looking for") or a horrible thing ("After 23 page hunt the visitor gave up, cursed us, abandoned the site, and went on to tweet to 23,000 followers that we stink").

When you look at 23 Page Views, how do you know which of the above three was the outcome?

But it gets worse.

Most content sites are currently monetized using display advertising, most commonly on a Cost Per Thousand Impressions (CPM) basis. When you are paid on a CPM basis the incentive is to figure out how to show the most possible ads on every page ("mo ads mo impressions!") and…. ensure the visitor sees the most possible pages on the site ("mo ads mo impressions mo page views mo money!").

That incentive removes a focus from the important entity, your customer, and places it on the secondary entity, your advertiser.

It does not take a degree in rocket science to see what happens next. The web is littered with examples of this awfulness.

Here's one simple example.

Photo slideshows are a great way to engage and delight customers. Yahoo! News has them. Except that they neither engage nor delight. Monetization on content websites, including likely Yahoo!, usually is on a Page View-driven CPM-incentivized mechanism.  The way this model manifests itself is that every time you click on the Next Photo button (arrow thingy) they load a new page. The new page has the next photo and lots of new ad impressions. Even on a pretty fast connection that means waiting, often for seconds. Every photo should deliver delight. Instead, every time you click on the Next Photo button, all you remember is the pain of waiting. [I'm ignoring the fact that in this day and age the photos themselves are tiny.]

Would it cause you to think positively of Yahoo! News? Or Business Insider? Or Forbes? Or all these other sites that impose a Page View-driven CPM-incentivized experience on you? More importantly: Would such a poor experience cause you to go back to these sites?

In that single session Yahoo! News made some of its Page Views quota and some of its CPM earnings. But it failed from a macro perspective. Short term gain; long term loss.

Now consider photo slideshows on (my beloved) news site, the BBC.

The BBC photo slideshows don't deliver small doses of pain every time you click the next button. Instead, they deliver small moments of joy.

In that single session the BBC created fewer Page Views for itself, smaller CPM earnings. But it created joy and delight from a wonderful user experience. That directly translates to me using the words "my beloved" every single time I talk about the BBC website, visiting the site a lot more often (5x a day at least), consuming a lot more content, and in the long run actually seeing (and clicking on) a lot more ads. Short term loss; long term gain.

The metric the BBC is focused on is not Page Views, it is Visitor Loyalty.

Visitor Loyalty is not in every single report in your Digital Analytics tool. But it is there. It is a standard metric. And it measures not what happens inside a session (short-term incentive), but rather behavior across sessions (long-term incentive). It forces the designers, editors, merchandisers, IT team, and everyone in between to trade tawdry sensational stories delivered via slow-loading, pain-inducing pages, for a focus on customer (not advertiser) delight.

Ironically, that actually means more ad impressions in the long run. It means becoming big.

Take a look around you. Most content sites, be they thesun.co.uk, xinhuanet.cn or nydailynews.com, have home pages that are (and I'm being kind here) link pukes. On average these sites have 500 links on their home page. Why?

If the web analytics dashboard prominently measured Visitor Loyalty, would they still create link pukes?

Would they not think: "Even my mom hates our site, how can I earn her love, the thing that has eluded me all my life?"  Would they then not focus on relevance and not generic link puking? Would they not buy simple behavior targeting solutions to use past behavior to customize some of the experience to deliver delight?

Would they not buy a solution like JumpTime  to, in real time (!), look at the FloPower of every link and economic value it is delivering (still in real time!) to go from 500 to just 200 links? Would they not obsess about speed because both mom and dad despise waiting?

I believe the answer to every single one of those questions is yes. Yes, they would.

All from anointing the right metric, Visitor Loyalty, as your KPI. It forces a focus on the long term and on the right entity (the customer and not the advertiser).

Friends don't let friends measure Page Views. Ever.

2. Revenue vs. Economic Value

Ecommerce/lead gen type websites are typically incessantly focused on one-night stands. "Hello, so nice to see you, now take off your clothes and jump into bed with me!"

Of course they don't say that exactly. But the "buy now, buy now, buy now, buy now" design and merchandising on their websites makes that amply clear. Just try visiting orbitz.com or macys.com or petsmart.com. Sometimes this one-night stand obsession is subtle, sometimes it is obvious in what is presented to you when you land, but it always becomes more transparent as you go deeper into the site.

That is a reflection of a deep obsession on Revenue. It is reflected in the obsession with Conversion Rate. Every web analytics tool in the market measures single-session conversion rate, so if visitor, your potential customer, does not convert in that single session (i.e, refuses the one-night stand), the visit is marked as a failure!

Guess what that encourages? An ever-harder obsession about getting better at scoring more one-night stands.

The problem?

Most people don't want one-night stands. I know, I know, you are super cute and awesome. Still.

Most people want to visit your site, do some research, go away, visit other sites, come back to yours, get more confidence about your brand, go back to Google and compare reviews/prices, come back to your site and add the product/service to the cart, go and ask their spouse/boss for permission, come back and buy from you (or the other site).

That was 7 dates.

When your KPI is revenue, you are focused on trying to get as many single-session conversions as possible. You make bigger Buy Now buttons. You pimp product specs (ugh!). You do sub optimal things. You ignore delivering what's expected on the first six dates.

Sure, some people will have a one-night stand with you. But most won't. Then how you do grow your business? How do you move beyond the standard conversion rate of less than 2%?

Shift to caring about Economic Value.

So when someone visits your site and signs up to receive email, and does not buy anything, that is not a failure. That is a micro-conversion because that first date will lead to a second, a third and a seventh (if you play your cards right!).

When someone comes to your site and watches a video, that is a micro-conversion.

When someone clicks on the product reviews tab, that is a micro-conversion.

When someone clicks on the "Send Page View Email" link (to get permission from wife/spouse), that is a micro-conversion.

Etc., etc., etc.

Every micro-conversion creates economic value for your business. It engages in the awareness, consideration, comparison, purchase slow dance. It delivers higher macro-conversions (revenue!) over multiple visits by the same person by incentivizing you to behave optimally, in sync with your customers and at their speed. It gently encourages everyone in your company to obsess about the micro-conversions by saying they are of business value, to create better designs, more prominent placement of content/images/stuff customers want.

Over the long term it shifts your company from the corrosive single-session, conversion obsession (for that is what Google Analytics, SiteCatalyst, WebTrends measure) to a pan-session, way-beyond-a-one-night-stand experience that delivers higher Economic Value.

Rather than just focusing on 2% success, and 98% failure, you are now focused on 100% success!

Do please note that I'm not saying don't worry about Revenue. As you saw above, the definition of Economic Value includes Revenue. I just want you to obsess about macro plus micro as THE way of being massively profitable. And as in the first case above, by delivering delight.

Pick Economic Value, your parents will be proud of you.

3. Time on Site vs. Task Completion Rate

Over time (ironic, right?) I've developed distaste for the time on site metric.

Some of the reasons are the same ones outlined in the good, bad, and horrible scenarios for measuring page views. With time on site the problem is compounded because our web analytics tools (unless you implement special extra javascript gyrations):

1. Can't measure time spent on the site if you only see one page, and
2. Can't measure the time spent on the last page of the visit

These sad realities make that metric even more suspect. Maybe suspect is too strong a word. The above two make it very difficult to infer exactly what the performance is reflecting.

Is 7 mins time on site awesome? And should we assume that the visitor spent zero seconds on the last page, or 28 minutes? What is the implication?

[Bonus] How are Time on Page and Time on Site calculated? [/Bonus]

It is not completely valueless. But it is not worthy of being crowned a KPI.

So, what are we actually trying to measure when we use Time on Site?

We are trying to infer whether the visitor had a great experience ("Wow, they spent 92 mins on the site! Man we rock!"). We are trying to infer if they consumed enough of our content (to make them happy and make us money). We are trying to figure out where they had problems ("What? The avg time on site is only 2 mins? Golly we suck!"). We are trying to figure out if our latest redesign was a success ("Look, time on site moved from 3 mins to 900, awesome!"). We are trying to…

This is the operative word: Trying.

The reality is that there is a vacuum there. We are not (yet) sitting inside the brain of the visitor. So we take our biases (also called experience :)), our opinions, our psychological issues, and all that and try to fill that vacuum.

We have no idea who Kim Watkins is and what her 6.3802146 time on site means. So we say: "Look, the average is 2 and Kim spent 6.3802146 mins so that was an 'engaged' visit." Hurray.

Why infer? Why be so arrogant as to believe that our biases, sorry experience, will interpret Kim's visit accurately?

Why not just ask Kim?

Two simple questions. The first gives primary purpose. The second is a yes or a no.

Kim will let us know she was there to buy a pair of Manolo Blahnik pumps. And no, she was not able to complete her task after 6.3802146 frustrating minutes because neither your navigation nor your internal site search engine got her to the right page.

And no, it was not a very "engaging" experience.

When you choose time on site as your KPI you are encouraging your organization to apply inference, and make changes that are, at best, wild guesses with a 1/100,000 chance of fixing the core problem.

When you choose task completion rate as your KPI you are encouraging your organization to put their ear directly next to the horse’s mouth, listen, feel the breath, then go fix the problems the horse has identified.

You'll agree that only one of these methods improves business profitability, results in customer-centric experiences and reduced losses from failed expeditions to chase mirages identified as issues.

And no, Ms. Watkins is not a horse. She is fine young woman. :)

Don't infer. Ask.

4. % of Search Traffic vs. Share of Global Search Volume

This one is more subtle. It is a matter of which lens you want to look at your performance.

% of Search Traffic: This measures the percentage of traffic you receive from search engines, in context of all other traffic sources.

How do you get it? You log into Baidu Tongji  (or Yahoo! Analytics) and create a little pie of your Search, Campaign, Direct, Referral and Other traffic sources. That shows you that 45% of your traffic is from Search. [Given how people use the web to seek information, at least for now, around 50% seem to be about the optimal number.]

You feel proud because you started with just 5% of the traffic from search engines. You've worked on a robust search engine optimization and pay per click programs to steadily grow your search traffic. Bonuses have been distributed.

This is a cause worth celebrating and, unlike other metrics in this blog post, given the deep importance of search this metric can be promoted to a KPI. It will incentivize the right behavior. Working ever harder on understanding your content, CMS and business strategy to do ever better SEO and PPC. It will drive the % of Search Traffic graph to go up and to the right (bigger piece of the pie). That 45% is now 500,000 visits a month from search! It is pretty good.

The problem is that we can often get stuck just looking at our own data, and in doing so we miss a chance to understand the real opportunity. We might completely miss the boat even as we celebrate what looks like huge success (moving from 5% to 45%).

You received 500,000 visits from Google.com. There were 209 million searches in your category (say pets) on Google.com originating from the US.

So Share of US Search Volume = 500,000/209,000,000

Gives you a different perspective right?

Some questions are simple. "OMG we have such a tiny share of the visits, what do we need to grab an ever bigger share?" Sure, not all 209 million will end up on your site, but you define the pets category! You have to get more than that tiny number of referrals. This will have huge implications on your paid search strategy, your valuation of clicks you get from Google and Bing. You might have to go out and hire new people, get a new agency, experiment with the long tail, buy some behavior targeting solutions, so much more. Sure we went from 5,000 to 500,000, but that will simply not do. The opportunity is too large and too relevant to ignore.

Other questions will be much harder. "OMG we spend mmm millions on TV, Radio and Magazines trying to create demand by interrupting people. For the most part we don't even know if they care about us, our products or our ecosystem. And here are millions of people behind 209 million queries a month who are raising their hand to say they want our products and services, they are interested in our ecosystem! We are spending ttt thousands on search. Should we rethink the balance between 'interrupting to possibly create demand' and 'welcoming with open arms people who want to hear from us'?"

This is a very, very hard discussion to have. Egos, politics, years of doing the same things, opinions, and genuinely believing that the current path is the best one … all come into play.

But if you want to be an agile, nimble competitor, it is a discussion you have to have. Even if in the end the TV budget stays 5,261% higher than digital. The debate is important. Making deliberate choices is important (even if you make the wrong choice). Because deliberate choices can be revisited. Data can be analyzed. Course changes can be plotted.

If you never deliberate, you slowly silently reach the point of no return and file bankruptcy protection.

Perhaps you'll get lucky and that won't happen to your company.

But changing the lens through which you view success can ensure that you watch the right thing, you debate and deliberate, you choose to slowly experiment, you shift budget. Step one? You use a metric like Share of Global Search Volume to incentivize the people in your company to look at the right thing and then power the right discussion.

Like everyone else, I love TV. I'm not advocating that the TV budget above should be 0%. But it is profoundly sub optimal to have this mismatch: Let's spend all our money on a channel where we, at best, kinda sorta feel users with the right intent are and let's ignore the one where 100% of the users with the right intent exist (and are looking for us!). That is a unsustainable life threatening strategy for everyone. Unsurprisingly it results in a weakening of your brand and profits. Yes, even for you.

Go change your lens.

5. # of Followers (or Fans or +1s) vs. Conversation Rate

One of my most retweeted quotes about social media goes like this: "Social media is like teen sex. Everyone wants to do it. No one actually knows how. When finally done, there is surprise it’s not better."

That probably says it all.

And how do we compound the problem? As major brands we proceed to measure one of the most useless measures of success: The number of Likes we get on Facebook.

Or the number Fans or Followers or +1s on Twitter, Google+, RenRen, Vkontakte and other lovely social channels.

When your digital dashboard measures Likes/Followers/+1s, what are you incentivizing your Agencies to do?

Use every legitimate and illegitimate technique out there to beg/cajole/lead/mislead people into pressing that button. Very little thought given to what happens after the button press (no incentive!).

What is the medium or long term strategy to engage with the audience? Where is the plan to ensure your social contributions score higher on Facebook's EdgeRank algorithm? Where is the structure that will ensure you build out a real credible asset for your company?

You have a lot of Likes, but you never get to creating a robust Earned media channel for your company. [An optimal inbound marketing portfolio will have balanced Owned, Paid and Earned channels.]

To seekers of Likes and Followers, social media "strategy"ends up being something lame, like sweepstakes, polls and pimping your latest press release. That barely works in the real world. Why would it work in an ADD environment like social media?

So how do we incentivize the right behavior? Look beyond the +1s, Followers and Likes, and leverage social channels to build out a community of like-type and like-sized :) people around you, a community that converses, shares, amplifies and, over the long term delivers economic value to the company. Leverage what the channel is really, really good at, close one too many connections based on conversations and value.

I've defined four metrics (Best Social Media Metrics) that incentivize the right behavior.

I've defined Conversation Rate as: # of Audience Comments Per Social Contribution

You can compute it for every social channel on the planet.

With TV you don't know who your audience is or if they are interested in you or what they care about., In social channels, you know all of those things. You can use that knowledge to participate in and initiate conversations. You can build a better connection (social equity? :)) and you can deliver value (by sharing valuable tips, answering questions, linking to good deeds by your competitors, creating special unique content, etc., etc.).

Conversation Rate incentivizes you, or your proxies (agencies), to really understand what social contribution is causing your audience to add their voice, to have a conversation with you. That will help you optimize your contributions, force you to understand your audience, and deliver value to your audience and your company.

Get zero replies per post/tweet/status update?

Your million Likers/Followers are telling you something. Stop. Reboot.

As your agency/company moves away from a Likes quest, you'll be astonished at the incentive Conversation Rate provides your employees. That in turn, slowly but surely over time, create a credible Earned media channel for your company.

So do the right thing. Converse. Don't shout. Don't pimp. Don't sweepstake.

I was advising a stealth mobile application company (hello future one billion Facebook dollars!) and this example comes from that experience.

If you've ever created a mobile app you know that from version 0.1 all the oxygen in the room is taken up in trying to figure out how to get your first 100,000 installs, how to score the Editor's Pick etc.

That is understandable. There are fifty million apps in iTunes and Play.

So naturally, # of Installs becomes the KPI that goes on top of the dashboard.

The problem with # of Installs is that it does not provide deeper insights about the value of the app to the users. It does not say anything about what the engineers got right or wrong. There is nothing in # of Installs that drives an obsessive understanding of the customer, the app experience/value, product development and all those other more valuable strategic parameters.

My advice to the team was: "Let's keep # of Installs as a metric we track, but let's make 30 Day Actives as our key performance indicator – the thing we really, really focus on."

There are so many amazing incentives from a focus on 30 Day Actives.

First, the company deemphasizes short term win — installs — and emphasizes the long term win — retention.

Second, employees care a little less about hundreds of new installs and start to care about 50% of people who uninstall the app in the first 24 hours.

Third, the company comes together to focus on the customer in every facet of their execution.

What promises are our sales/marketing programs making? What does the post-install process look like?" "Is the app instrumented to collect the right usage data? What is the optimal number of ads in the app that causes fewer 30 Day Actives? When people cancel, what does that experience look like? How do we go about releasing updates to ensure higher retention? Do we need a loyalty program? What can do to empower our customers to spread their stories about us? 

And so much more.

When the focus is on the # of installs it is not hard to imagine that there is no overt incentive to consider the above questions, or to assign a high priority to getting those answers.

So change.

Use 30 Day Actives as your KPI. Build a stronger profitable business.

It is important to point out that I'm not advocating that you stop measuring page views, revenue, time on site, % of search traffic, # of Likes, or # of installs. They are all fine metrics. You'll most likely use them as diagnostic measures when you analyze the metrics I do recommend you shift to.

I'm advocating that you not make them KPIs, don't crown them God, don't allow your employees to solve just for the primitive six. Because none of these six metrics incentivize optimal behavior or business outcomes.

You become what you measure, so why not solve for what actually matters?

Let me close with a quote on incentives, from the inimitable Steve Jobs…

"Incentive structures work. So you have to be very careful of what you incent people to do, because various incentive structures create all sorts of consequences that you can't anticipate. Everybody at Pixar is incented to build the company: whether they're working on the film; whether they're working on a potential direct-to-video product; whether they're working on a CD-ROM. Whatever their combination of creative and technical talent may be, we want them incented to make the whole company successful."

No one could have framed it better than Steve.

Incentive structures are not a web analytics problem. They are an organization design problem. But in choosing the optimal metrics to crown as heroes we can use data to incentivize the right behavior, value creation for a company, and deliver happiness to customers.

Good luck!

As always it's your turn now.

Do you use the primitive six as KPIs in your company? Have they incentivized you, your peers, to solve for optimal business and customer outcomes? Do you have other suggestions for primitive metrics? How about suggestions for metrics that incentivize optimal focus? Got a favorite "OMG I'll die if we can just measure metric x"?

Please share your feedback, suggestions, critique, huzzahs via comments below.

Thank you.

You Are What You Measure, So Choose Your KPIs (Incentives) Wisely! is a post from: Occam's Razor by Avinash Kaushik

I believe deeply in that quote. In my life I spend an extraordinary amount of time understanding the problem and attempting to define it clearly. As if by magic, I find that it is then much easier to find the optimal solution (or realize none exists!).

Multi-Channel Attribution is a red hot topic in our industry, and yet it is so poorly understood. I'm convinced that the resulting problems (confusion, FUD, angst, daily prayers, and wasted budget) are due to the lack of a clear framework that can help clearly define the problem.

In this post my hope is share a framework that will help define the problem clearly. Included in the post are recommendations for measurement and data analysis. And as if that was not enough, :), I'll close the post with my thoughts on digital marketing attribution models.

This is going to be a lot of fun. Roll up your sleeves, put a smile on your face, grab a pinch of common sense, a heavy dose of reality and let's go…

Three Types of Multi-Channel Attribution Problems.

A huge amount of confusion and disagreement on this topic exists simply because there is no general consensus about those three words. Multi-Channel Attribution.

So let's try and fix that problem.

There are three types of attribution problems in our non-line world.

Multi-Channel Attribution, Online to Store:

This is the attempt by Marketers and Analysts to try and understand the offline impact (revenue/brand value/butts in seats/phone calls/etc) driven by online marketing and advertising. We'll refer to this quest for doing effective attribution as MCA-O2S.

While I'm using the term Store here, it encompasses sales (or leads or catalog requests) driven to a retail store or company call center, people driven to donate blood via online campaigns, or essentially any offline outcome driven by the online channel.

An example of MCA-O2S is Verizon wanting to know how many in-store offline phone activations are driven by online search advertising, for every online activation that the same search advertising drives.

[In case you were curious... It's 5 new accounts activated offline for every 1 activated online! If you are not calculating the offline impact, and you are not giving your online channel due credit. In this case, it would be 5x less credit! You can see why MCA-O2S is supremely critical for every company on the planet.]

Here's the Post-It on which I'd sketched MCA-O2S in planning this post. The red dots represent activity we would like to ensure we are measuring to 1) ensure we understand behavior, and 2) deliver insights that will influence our marketing and advertising…

I spend a lot of time with CEOs and CMOs and when they talk about multi-channel attribution, they're invariably talking about MCA-O2S. Yet when most of my digital peers talk about this topic, they're not talking about MCA-O2S. You can imagine why things might get a little confusing.

So when you meet a CEO and they use say "Help me solve the amazing multi-channel attribution problem", you say: "which type of MCA are you interested in?" Clarity will help foster a valuable conversation.

Almost all current, hot and heavy, literature on the topic of attribution modeling does not cover MCA-O2S. That's because when it comes to MCA-O2S your only bffs are a set of 16 strategies I've outlined over two posts (links immediately below) or the fantastic world of controlled experiments (as in the Verizon case above). So less automated algorithms "distributing credit" and more thoughtful deliberative discreet measurement strategies that inform strategic decisions.

Two helpful blog posts on multi-channel analytics: 1. Tracking online impact of offline advertising. 2. Tracking offline impact of online advertising.

MCA-O2S. It's mandatory. Attribution is driven by experiments. And when you win, you win huge!

Multi-Channel Attribution, Across Multiple Screens:

Senior leaders, especially in larger companies, have started to refer to this when they use the magical words multi-channel attribution.

With the massive adoption of mobile phones and tablets we are all increasingly "four screen" people (TV, desktop, tablets, smart phones). That has directly translated into a more complex fragmented influence landscape (drives the "old timers" bananas). That in turn has translated into many senior leaders deeply desiring, as they put it, "multi-channel attribution." What they really mean is MCA-AMS.

What they really really want is to understand how individuals experience a company or government's digital existence across multiple devices, what media (advertising and marketing) they are being exposed to, and what outcomes (conversions!) are happening as a result.

An example of MCA-AMS is the ability to understand that a search I did on my tablet computer while watching a television commercial resulted in a click on a paid search ad to a camera site which logged into my memory which later caused me to read reviews of the camera on my Nexus S while stuck in traffic and that finally caused a sale for Sony when I got home and happened to be on my laptop.

Attribution in this case is the quest to apportion credit across the TV commercial, tablet paid search ad, reviews read on on the mobile phone for a "direct" conversion on the PC. Amazing, right?

Here's my sketch on MCA-AMS and the raw complexity of the customer experience that we are trying to understand… the red dots indicate what we're trying to measure and understand the impact of…

The primary challenge is that as we switch devices it is increasingly difficult to keep track of the same person as they interface with our digital existence (and are exposed to online and offline marketing and advertising). Actually, I should not say increasingly difficult, I should say almost impossible (cookies, uuids, privacy, government, et al).

Perhaps the only exceptions to the "its almost impossible" scenario would be companies that service customers who are mostly logged in (think Amazon, NY Times) across all four screens all the time. Such companies usually also own massive data warehouses where they have an ability to periodically do cannonballs into the data and identify correlations in consumption and purchase patterns. Often, though not always, they can also tease out causations between devices used during outcomes (five-second segmentation in say Google Analytics) and their media plans while focusing on customer analysis (not visitors, not cookies, not uuids, customers).

Even then it is hard, very hard. And for the rest of us this will remain a complex, and I'm sorry to be so real, unsolvable challenge. At least for now.

Some ideas from the two multi-channel blog posts above can help with MCA-AMS. I've leveraged controlled experiments to get very good "kinda sorta understanding" of reality.

I believe that real solutions will come from the evolution of cookies, updating privacy policies, government decisions and evolving user habits. All that first, then our ability to have nonline data.

Because of all of the above you can see why attribution models don't even enter the picture with MCA-AMS. But when you meet executives and they say "help us with our multi-channel attribution problem", most definitely ask the clarifying question: "do you mean MCA-O2S or MCA-AMS?"

MCA-AMS. Complex, hard challenge. Not a huge problem yet for most, but heading in that direction.

Multi-Channel Attribution, Across Digital Channels:

Almost all of the time when people in our ecosystem (unlike CEOs, CMOs) talk about Multi-Channel Attribution, this is the one they are referring to.

MCA-ADC is the effort to understand which digital marketing channels (Social, Display, YouTube, Referral, Email, Search, others) contributed to a particular conversion (or multiple conversions).

At the moment all web analytics tools, like SiteCatalyst, WebTrends, Google Analytics, CoreMetrics, and others, by default attribute a conversion to the channel immediately prior to the conversion. This is also known as last click attribution.

With MCA-ADC you are trying to go beyond the last click and get this, complete, picture of all marketing activity prior to the conversion (in this case from Google Analytics):

For this website, 767 conversions came from people who visited the site in the above precise order starting with social then a direct visit then an organic search then a referral click-through and finally one last direct visit which lead to the conversions.

The attribution bit here is the burning desire inside all digital marketers to figure out how to dole out credit for the above conversions. Should Direct get 50%? How about Social? 100%? What about Organic? 2%? But let's put that delightful thought on the back burner for just a minute while we understand a critical, often hidden, nuance. [Analysis Ninjas are magnificent at understanding nuance!]

When people talk about MCA-ADC they are still just talking about one device. Because in very close to 0% of the cases do any of these analytics tool have an idea about the behavior of one homo sapien across multiple screens (AMS).

So what you are seeing above are all the conversions that can be tied to multiple visits by a unique browser (notice I did not say person) to your website/digital existence. BTW it is fantastic that GA does this because most other tools don't even show you this.

Say, the Organic Search above had happened on a mobile phone… regardless of the digital analytics tool used, to most websites today that visit would be invisible in the above chain (cookies!). #omg

Hence it is important to separate out MCA-AMS (across multiple screens) from MCA-ADC (across digital channels) – at least for now, until the cookies, ids, privacy policies, government guidance and user habits problem is solved.

When it comes to measuring MCA-AMS you'll use the guidance provided in the above section. For MCA-ADC you'll use a different set of reports (multi-channel funnels ) and attribution models.

I'm sure you are already familiar with nuance number two when it comes to MCA-ADC. A blind-spot if you will.

The above picture does not capture what the impact of this behavior was on your offline existence (O2S). Web Analytics tools are not awesome at that. Ok, they stink at it.

So it is possible that an additional 3,835 people went and made purchases in your stores or via your phone channel (taking the Verizon numbers from above). That would also be invisible from the above report. None of the channels above, whether glorious social, beloved direct, magnificent search, sweet referral, would ever get "credit." Unless you are willing to use the methodologies outlined in the MCA-O2S section above.

When you talk about MCA-ADC, ensure that you are aware and communicate to your leadership, that you are not reporting on MCA-O2S (online to store) and it is extremely unlikely to be reporting the impact of MCA-AMS.

Here's one last Post-It sketch. The red dots are what you are likely measuring when you attempt MCA-ADC…

And if I wanted to be pedantic I would say it is really MCA-ADCFOD. Multi-channel attribution across digital channels for one device.

Now it is true that with sufficient analytical skills, time, patience, and God's direct blessing to you, it might be possible to do complete multi-channel attribution analysis where the multi-channel includes multiple online ad channels, behavior of the person across devices and the impact online and offline. Sadly, that is incredibly hard to do as a whole. And when I say incredibly hard, I mean almost impossible. And when I say almost impossible, I mean only attempt that after you know you've fixed all other problems with your advertising, your online and offline existence and your people. All three.

I know that sounds like a bummer, but a dose of reality is particularly needed in this discussion. There are simply too many fake promises being made by vendors, consultants, tweeters, gurus and fairies. That is unhelpful to the entire ecosystem.

To close this section…

Next time you hear someone utter the words multi-channel attribution, the single greatest gift you can give yourself is to ask in your sweetest possible voice: "Are you referring to MCA-O2S, MCA-AMS or MCA-ADC?"

You'll earn their respect for knowing that there are three types, and you'll be able to put into context what they are asking for and proceed to have a career and business-enhancing discussion.

Multi-Channel Attribution Models.

For MCA-O2S and MCA-AMS, it is a complex undertaking to identify "which advertising/marketing vehicle deserves how much credit." It requires patience and skills. And it requires your execution of multiple of the 16 strategies I've outlined for tracking online impact of offline and offline impact of online. Even more, it requires an ability (people + skills + desire) to execute controlled experiments.

So the question "who deserves how much credit" is tertiary at best.

With MCA-ADC that quest is a little bit easier. We have the multi-channel funnel reports at our disposal. Additionally in some tools we also have an ability to apply attribution models to the behavior you see in the two pictures above in the MCA-ADC section. #sweetness

The most common attribution models bundled into even the simplest web analytics tools are: Last click, first click, and even distribution.

If you are lucky, you have access to a more sophisticated tool which would include: Adjustable, based on mathematical algorithms, time decay model.

If you are among the chosen few, you'll likely have access to a digital analytics tool that allows you to create a customized attribution model.

Each of these models are applied to MCA-ADC (still without benefit of O2C or AMS) and provide you with incrementally better understanding of your digital media spend.

Each of these models comes with its own pros and cons. [If you have my book Web Analytics 2.0 please jump to page 358.] Some of them have more cons and barely any pros. Those should be avoided like the plague.

A couple of them pass the common sense test, and hence will put you in a better place than staying with last click attribution.

But most of what you'll get out of playing with these models is a deep and profound appreciation for how they'll, even in their most shining moment, give you directional guidance how to adjust your media spend (shift dollars/euros/pesos from Search to Display or from Display to Email or… other combinations).

You'll realize (even if you use the greatest customized model created by your most magnificent consultant at a equally magnificent cost to you) that success then will come not from that rough output, but rather from your ability to take that rough output, make changes, observe the impact (over weeks, or months if you are small sized), identify insights and be less wrong over time.

If you happen to be in a larger company, say you spend more than $10 million on digital marketing per year, you'll quickly see, having learned to be less wrong over time, that the question you want to answer with multi-channel attribution modeling is not "who gets how much credit" but rather "how can I optimally balance my digital marketing portfolio."

That will then drive you to seek solace in the arms of the only solution that actually works. The solution that is hard. The solution that requires unique people skills and an undying desire to scale un-imagined heights of glory. Media Mix Models. Executed via persistent controlled experiments.

When you reach that point, fame, fortune and happiness will be yours.

Multi-Channel Attribution: Closing Thoughts

This is a tough challenge. Simply because reality is complicated.

Customer experiences are ever more complex, influence channels intersect a lot more, content consumption is fragmented, the three-step "attract, acquire, retain" model is now broken into 37 different pieces.

So, you don't have a choice. You are going to have to deal with the multi-channel attribution problems, all three of them, if you want your company to have an effective advertising and marketing strategy.

Here's the good news: You don't have to try to boil the ocean in one go. In fact, that might be hazardous to your health if you attempt to do that. Take gradual steps. Increase your sophistication over time.

Here's what I recommend:

1. First clarify what problem you are solving for your management team. O2S or AMS or ADC.

2. Use the appropriate set of solution (see sections above). If MCA-ADC…

3. Get really, really good at understanding your multi-channel funnel reports. They are free. They are awesome. Use the Venn diagram in the Overview report to display reality to your management team. They'll love you, and stop wasting money.

4. Start to experiment with the simple models. You are moving away from last click, you'll abandon first and even very quickly. Spend some love and attention on the time decay attribution model (ideally with several mathematical options to apply).

5. Experiment with changes in your digital portfolio based on your time decay results.

6. Measure outcomes. Go back. Analyze the data. Change some more.

7. As you master that, shift slowly to playing with media mix modeling type controlled experiments.

If at any step you notice diminishing margins of return, go back to the previous step and optimize that one some more until it is truly worth the incremental company investment to take the next step.

If you understand the frameworks, if you internalize the challenges, if you define your company's immediate unique problem clearly, and follow a step wise approach outline above you'll not just do fine. You'll be fantastic.

Good luck!

As always, it's your turn now.

Which multi-channel attribution problem are you solving in your company? Do you distinguish between the three outlined in this post? Is there a fourth one not covered in this post? Which one do you find to be the most challenging? Are you more optimistic that we'll solve AMS (across multiple screens) than I am? Which MCA-ADC attribution models do you swear by? Who's your BFF? Do you have a attribution model that's not covered in this post?

Please share your thoughts, feedback, critique, and brilliant new ideas via comments.

Thank you.

Multi-Channel Attribution: Definitions, Models and a Reality Check is a post from: Occam's Razor by Avinash Kaushik

So what is big data really? No one quite knows.

As I interpret it, big data is the collection of massive databases of structured and unstructured data. The data sources include traditional (now considered puny) sources like corporate ERP/CRM systems and non-traditional (massive) sources like every technical ping from every human or mechanical sensor, all web behavior by everyone across the entire Internet, increasingly digital data from analog sources like hospitals or the atmosphere, and (good lord!) our collective tweeted wisdom.

That is a lot, right?

Because so much of the big data talk is focused on the promise of zettabytes of data, big data also tends to be about massively parallel computing, fantastic storage systems, the "cloud," Hadoop and MapReduce and other such deeply technical delights.

That explains why so much of big data talk comes from Oracle, IBM, Microsoft, SAP and other vendors. And not so much from practitioners, yet.

I believe in the promise of big data and the awesomeness of the insights that can come from it. But that should not come as a surprise. All the way back in 2007, I was evangelizing the value of moving away from the "small data" world of clickstream data to the "bigger data" world of using multiple data sources to make smarter decisions on the web. Clickstream + qualitative data + rigorous statistical analysis of outcomes + deep mining of data from competitive intelligence sources + rapid experiments + more.

Here's the "bigger web analytics data" picture from 2007… Multiplicity!

The big data we are dealing with today puts the 2007 picture to shame. We have even more types of data, becoming ever more complex, distributed across multiple existences, and we are left with the task of parsing out terabytes of noise to get to a megabyte of signal.

That last part is what I love to focus on, what I worry about, what I think everyone should focus on. It is great that we have big data. It is greater that we have such amazing promise in that big data. It is sucky that almost no one knows what to do with it in the context of driving actual business value.

Hence my interest in big data is not about the zettabytes or Hadoop or unstructured variables or one of the n technical things that seem to dominate big data conversations.

My interest is deeply and passionately rooted in trying to figure out how to ride big data all the way to the bank (or world peace). How to find insights? How to structure organizations that will use this data to ensure that they get timely value from it? How to drive action? How to find frameworks that force a different type of thinking so we don't make the mistakes we so brilliantly have made in the world of small data?

If we don't answer all those hows big data will be a big disappointment.

Avoiding big disappointment and the hows were on my mind as I prepared my keynote for Strata 2012 Big Data conference . My goal was to take my TED-ish 15-minute timeslot to present my perspective on why driving big action was the big imperative for big data.

It was an incredible challenge, thanks to Strata co-chairs Edd Dumbill and Alistair Croll. In this post, I want to share the result with you.

I'd structured my keynote into three big pieces:

Here's the keynote…

[You can also watch this video on YouTube. You're also welcome to Like, Share, Tweet, Facebook, +1 it on YouTube as well.]

It is not my hope to encourage you to copy/paste the strategy outlined, or to use the tools shown.

My hope is to simply inspire you to think a little differently about organization design, share a framework to influence the focus of your analysis, and find the types of practical solutions that will really spark profitability from all this big data.

I welcome your feedback and thoughts on the video and the solutions via comments. Please also share your experience with big data. Any big or small success you've had would be inspiring to all of us.

Preparing for my keynote also got me thinking about all the implications of big data and my own longish career in trying to create superb decision support systems. The database has moved from my floppy disk (true story) to an infinite storage cloud, yet, amazingly, some of the biggest challenges have remained the same.

So big data revolutionaries…

Six Rules That Should Govern Your Big Data Existence.

Here are some rules from my experience in the small data world that I've come to believe also apply to the big data world, perhaps even more so. As you go about your big data journey you'll meet with even more immense success if you consider these valuable life lessons:

1. Don't buy the hype of big data and throw millions of dollars away. But don't stand still.

2. Big thinking about what big data should be solving for is supremely important.

3. The 10/90 rule for magnificent data success still holds true.

4. Shoot for right time data, not real time data.

5. "Data quality sucks, just get over it."

6. Eliminating noise is even more important than finding a signal.

Six simple rules for you revolutionaries to follow to ensure, well, revolutionary success.

Notice, none of them have to do with hardware or Hadoop. One important reason is that I'm solving for the CEO and not the CIO/CTO, so it is a matter of perspective. The second (main) reason is that we do face some big data technology challenges for now, but the things that will determine if big data will deliver big value have nothing to with technology. They have to do with the six rules above.

If you are really thinking big data value, think CEO and not CIO/CTO. It will dramatically change the focus of your work, in a good way.

As always, it's your turn now.

Did you find the keynote to be of value? Did you find the framework to be of value? Will it drive you to change your approach to big data? With regards to the rules above … is there one rule above that is your favorite? Is there one that should have been there but is missing? What is the biggest big data advice you would share from your experience?

Please share your wisdom, recommendations, and feedback via comments.

Thank you.

A Big Data Imperative: Driving Big Action is a post from: Occam's Razor by Avinash Kaushik

We work very hard. We torture SiteCatalyst. We send out a lot of data. Then we resend it again and again. And yet our work results in very little impact on the business in terms of action taken by company leaders.

Why this sad state? Almost always we dive into the ocean of data first. Sadder still, we don't ask questions later. We never ask questions.

No questions. No tie to what's important. No impact from the data.

Result? Our work lacks purpose. It is that simple.

My normal recommendation to address this supremely corrosive issue is to encourage each company to go through the process of creating a Digital Marketing and Measurement Model . It is a fantastic five step process that forces the engagement of key stake holders to produce a blueprint of why digital exists in a company, and what it is trying to accomplish.

No touching Google Analytics. No going to web analytics conferences. No tweeting for help.

Just doing the four things, in five steps above, will deliver what we lack… purpose.

Unfortunately a very tiny fraction of companies, or Analysts, want to put in this lifesaving effort up front.

If you fall in the "Analyst unwilling to do the hard work" category, I'm afraid I can't help you.

If you fall into the "Analyst really wanting to do the hard work but does not have the connection to Superiors, or other teams, and looking for any way out to identify business purpose" category. I have a very very simple approach for you to follow. You are going to love it.

But there are two prerequisites: 1. You are going to have to throw away the shackles, and think like a business owner. Even if you work in a multi-headed hydra called "global corporation." 2. Have the courage to move beyond the office politics/bickering, move from waiting for a savior to tell you what the purpose should be to investing some time in figuring it out yourself.

If you meet the prerequisites, and have a pinch of business savvy, we are together going to change the world!

My recommendation calls for you to take a structured approach and answer five questions. The insightful answers will help you create your own understanding of the purpose of the digital existence. You'll end up creating something very close to the DMMM above.

The result will be an astonishingly high level of focus for your digital analytics work (even on day one) and hyper-relevant insights to the business. That, in turn will simply blow people's mind (relevant insights always do), creating love for you. And love like that is hard to come by. (Conveniently that type of love also translates into a sweet raise. :)

Perhaps I've over-promised. But I'm just so excited about this process and its power to make our professional lives better.

Ready?

In my experience the best teaching happens with real world examples, rather than spouting theory. Hence, I'm going to use Credit Karma as an example to illustrate the process. I don't know anyone at Credit Karma. I'm not an expert in the credit score reporting business. So I'll be just as blind as you might be walking into any business and going through this exercise.

Here are the five questions (plus one special bonus in the end) I/you have to answer to get a very good sense of the business to bring astonishing relevancy to our data analysis:

#1. Why does the site exist?

This is the holy grail. But here's the trick: We are not looking for just the obvious answers. We want to identify as close to 100% of the purpose for which the site exists, how it makes money/gets leads/raises donations (as the case may be).

In the case of Credit Karma my first job is to identify what the Macro Conversion is. The single biggest reason for the site's existence.

Luckily except in the case of the most incompetent websites, this is easy to find. In our case it is right there staring us in the face on the home page: Free Daily Credit Card Monitoring!

Just to be sure, since I don't know them at all, I might poke around a few pages to make sure. But usually it is pretty clear.

And in this case the cool thing is that they give you one score, the TransUnion one, for free. No credit cards required to sign up! My favorite report is the Credit Report Card. Great visualizations and really great data. Sign up today! [Disclosure: I'm not affiliated with nor do I know anyone at Credit Karma.]

OK, back to being the business owner.

The next thing to answer this question, and ensure that I'm not a newbie Analyst who will only focus on 2% of the business success, I have to figure out the Micro Conversions.

To do this you'll go to the main sections of the website. You'll look for other calls to action. "Sign up for the mailing list." "Order our catalog." "Download the trial version." Et al.

After 10 minutes of browsing, I found all these valuable Micro Conversions:

Some are pretty straight-forward. Affiliate links (Take Offer, Compare Rates) that link to other sites from which Credit Karma makes commissions. Advertising on the site is a Micro Conversion (the SavvyMoney ad above with the link Manage Your Debt). The Write A Review call to action (the more reviews there are on credit cards, the more valuable the site is for comparison shoppers the more people will come and do business with them). In the same vein, completed Compare Credit Card offers is an important Micro Conversion (and a sign of deeper engagement with the site). Finally, the links to connection on social platforms are Micro Conversions as well.

Now you have a fantastic understanding of the business objective (make money via credit reporting) and the Goals (a combination of Macro + Micro Conversions).

And, I can't stress this enough, you are not just looking at 2% of business success, you are looking at 100%.

Bonus: Identifying Macro and Micro Conversions also gives you a list of Ecommerce Tracking to set up on the site, and Goals to set up in the Admin interface. You'll also note small thinks like outbound link tracking (using Events) to set up for social actions and ensuring all affiliate links are tagged with our company's tracking parameters.

Don't open Google Analytics or Yahoo Web Analytics yet! We have more work to do…

#2. What parts of the website should you focus on first?

One of the biggest problems we have with digital analytics is that we have waaaaaay too much data. And because the reports only show the top ten rows, we might not easily be able to see what matters.

Hence it is very important to figure out where to focus your analysis first. My method for doing that is to browse around the site and answer this question:

You can quickly see how those simple questions help you understand what data might be the object of your analytical horsepower.

Another 10 or 15 minutes of exploring various links and pages yields the answers I'm looking for.

For me, as a lay person and not a credit score industry veteran, the most important section would be /learning. The more the website visitors are aware of how important credit scores are, the more likely they are to sign up.

This was a bit hidden but the second most important piece of content would be the Credit Simulator (/preview/simulator). I can go play with the simulation and be informed (scared, actually) of the implications of taking credit and become a more qualified lead for Credit Karma.

The other sections I found valuable, using the framework outlined in the questions above, were: /help/howitworks (no one would sign up without looking at this page, we have to A/B and MVT test this to the max), /tools (this creates a great affinity for the brand, even if people don't sign up) and of course /creditcards (if they don't sign up, let's at least get an affiliate click :).

You can quickly see how you've got a short list of things to do in the Content section of Google Analytics. The filters to apply to those reports, to understand which KPIs would be most important as you value this content.

Rather than letting the data take you somewhere randomly, let this approach put you in the drivers seat and then you take data for a ride to a specific destination. That is what being successful is all about.

Awesome, right?

#3. How smart is their digital marketing strategy?

If you are a regular reader of this blog you know how deeply fond I am of the Acquisition, Behavior, Outcomes framework. We covered Outcomes with the first question and behavior with the second. Now it's time for acquisition.

What I try to probe, without talking to anyone at the company, is how savvy the company is in digital marketing. I'm also trying to figure out all the places they might be doing advertising. I want to know if they have even a simplistic understanding of how to rock social media.

Here's my process for doing that…

All this goes into creating starting points for what I'll do when I get into the web analytics tool. Will I analyze Search first or Campaigns? Will I focus more on referring sources or social traffic first? Will I measure the value of YouTube first or Display ads?

Additionally the above investigation also gives me a set of insights I can deliver to my CxOs. Channels where they should exist but don't. Things they might be doing badly in Social or YouTube or wherever. Missed opportunities in Organic search or SPYW. Things like that. And these recommendations will come from my own digital marketing sophistication (earning respect from my Senior Leaders).

Bonus: In the digital marketing savvy section I've also started to pull out my Samsung Galaxy Tab and Nexus S to preview the mobile and tablet experience of the company. If it stinks that tells me a lot (remember the year of mobile was 2010!). I'll also run a couple of quick searches on Google or Yandex or Baidu to see how the landing pages look on my mobile phone and tablet.

Super Bonus: Only for the most passionate amongst you… run a quick query in the iTunes App Store and the Android Market to see if the business exists there in the form of an application. If yes, download it. Play with it. Download some competitor offerings.

Most companies that are on the bleeding edge of digital marketing savvy are leveraging Google, Yahoo!, Email Marketing, Blog ads, Social channels AND mobile experiences AND mobile applications. The analysis above, will bring remarkable brilliance when you dive into the data. You'll take your company from bad to good in terms of acquisition-savvy, or from good to great.

#4. How well are they doing in context of their competition?

It is almost criminal to dive into doing any analysis of a company's website data without first getting a little bit of context about their competitive performance. Context after all is king .

Here one simple example of how it can be helpful. You log into CoreMetrics and you see a line traffic going up or down. Is that good or bad? You don't know. No one at the company will talk to you. Why not jump on to a free competitive intelligence tool and figure out the answer for yourself?

I'll usually start with looking at the company's data in www.compete.com (if they are US-based with primarily US-based traffic) or Google Trends for Websites . And in five seconds I'll end up with a graph that looks like this:

The above data is from Compete. I've included not just the data for Credit Karma, but also for two relevant competitors, freescore.com and myfico.com.

Initially I was wow-ed by the spike in the blue line (Credit Karma), that is quite spectacular. But then I see that it might be an industry thing, as the competitor spiked as well. Good context.

While at Compete I can also dig into a whole bunch of metrics like Visits, PageViews, udience segmentation, and so much more.

Now, I better understand visitor acquisition.

Time to understand a bit more about the visitors themselves. My BFF? Google/DoubleClick AdPlanner , perhaps the largest source of demographic and psychographic data out there.

The above data is for freescore.com. I can also quickly run queries for Credit Karma (and others) and compare and contrast the demographic profiles of people who visit the website. Are our competitors particularly stronger in some Educational categories or Incomes compared to us? What are our areas of strength?

While in AdPlanner I also highly recommend looking at "Sites also visited," a fantastic way to understand who a site's real competitors are. What are the clusters of options when people consider a credit report? This is also a great place to get ideas for websites you can show ads on, exchange links, etc.

The last stop of my journey is Google Insights for Search , your direct source for all Google organic search data from across the world. Here I particularly like to look at a metric I call "share of search." How often are people looking for the generic query for the industry, for me (/my company) and for my direct competitors?

Think of it as unaided brand recall …

Just look at that massive spike in queries for Credit Karma at the end of Dec! What the heck happened there? Great question. What where the related keywords people searched for? Check the Google Analytics reports. Was this traffic any good? Check the Google Analytics metrics. Are we going to dominate the world and crush our competitors? Time will tell!

The purpose of competitive intelligence analysis is to understand your place in the world, to highlight from an industry/ecosystem perspective what your strengths and areas of opportunity are, and to collect a list of questions like the ones immediately above for analysis in your web analytics tools.

Is that not simply orgasmic?

#5. What is the fastest possible way I can have a impact on the business?

One final thing.

I look for a low hanging fruit to fix/analyze. Something I can quickly analyze, find insights for and get fixed to show the value of data (and my employment at the company).

Here are some examples of things I consciously look for:

And other such things. Depending on the website you are analyzing, and your web-savvy/UX expertise, you might find other things. But the criteria to apply is that you are looking for big, obvious broken things that can mostly likely be fixed quickly and for which the impact can be quickly measured.

You are trying to find something with a clear purpose to show the power of actions taken through data.

One of my most beloved low hanging fruit for lead gen/ecommerce websites is to identify and improve the checkout abandonment rate .

That would be measuring the efficiency of this process for Credit Karma:

For a lead gen/ecommerce website there is no faster way to improve the bottom line. The potential customer has already discovered us. They've survived our website. They've gone from consideration to purchase. Now, all that remains for us to make money is to get them through these three simple pages. Let's make sure we do that! 100% of the time! (I love being aggressive in this case.)

This is directly tied to business purpose. It is absolutely focused on something important (getting the macro conversion). It is small (3 pages), and it is very well defined. And it is easily measureable (hello my dear funnel analysis, I've missed you!).

That is how an Analyst achieves glory. Through data. Powered by a clear purpose.

So five simple questions that help you focus on the end-to-end view of the business (Acquisition, Behavior, Outcome) without ever touching the data (except CI) and help you create your own Digital Marketing Measurement Model.

What I love more than anything else is that it forces you to become the Marketer for the couple hours you'll spend on it. It forces you to think like a business owner for that time. It forces you to pull out any UI/UX chops you have.

It is rare that Analysts get to flex those muscles. It is important, though because I don't know of a single Digital Analyst who has become great without flexing those muscles.

And now, my dear, you are ready to log into your web analytics tool!

But before you do that, I have one last parting gift for you…

Special Bonus: #6. Any technical notes I can make for the future (analytics or coding)?

As I'm clicking around I also like to make note of these things:

Remember. You are not a technical implementer or a javascript tagger – two valuable roles. You are an Analyst. Your primary objective should be data analysis and finding insights. So the first five questions and the answers you'll find are your focus area. The sixth is a gift you can give the javascript tagger/technical implementer in your company.

That's it. My humble attempt at sharing with you everything I know about avoiding the single biggest mistake Digital Analysts/Marketers make: Execute their jobs without a clear business purpose.

If any of the above makes you feel that I hold data secondary and understanding what data is in service of first then I've succeed in my mission with this post.

As always, it's your turn now.

What are the approaches you use to identify business purpose? Do you dive into the data first, and still find insights without doing the above mentioned five investigations? Is there a strategy outlined above that you feel works better than others? What are your favorite low hanging fruits to fix for a digital business?

Please share your recommendations, war stories from the front lines, and feedback via comments.

Thank you.

The Biggest Mistake Web Analysts Make… And How To Avoid It! is a post from: Occam's Razor by Avinash Kaushik

I mean really use the tools. Ravage all the features. Exploit every possible button. Produce built-in visualization magic. Poke into the hidden crevices and discover exotic delights. Nourish yourself with the "info snacks" the tool's engineers and product managers cooked up.

This post is all about that.

When it comes to data analysis, you are usually more likely to see me share guidance on advanced segmentation or custom reports or advanced social metrics or controlled experiments or economic value or competitive intelligence or web analytics maturity or one of an infinite number of difficult, if hugely rewarding, things.

Not today.

Today is going to be about healing heartbreak. Ravaging data. Poking and prodding. Nourishing ourselves. And doing so with simple mouse clicks inside the standard tool interface (!) with the reports and features you can already access.

Here is a summary of the eight incredible recommendations in this post:

#1. Create a Customized Dashboard – Earn Love, Drive Change

#2. Leverage Custom Alerts – Let Data Kick Your Butt Into Action

#3. Use Table View Options (Comparison, Pivots, In-line Filters) – Faster Initial Insights

#4. In-Page Analytics – Re-imagine Traveling Through Data

#5. Perform Recency, Frequency & Pan Session Analysis: Fall in Love with People not Page Views

#6. Matched Query Type, Keyword Position, Day Parts: Sexier PPC Analytics

#7. Custom Report Filters, Tabs: Bring Deeper Relevance To Your Custom Reports

#8. Quit Google Analytics: Move Beyond Tool/Creativity Limitations

If you are an Analysis Ninja, focus on the mental model and approach used in each recommendation. If you are an Analysis Ninja in-the-making, close the door to your office/room – you are going to repeatedly squeal with delight.

Ready?

#1. Create a Customized Dashboard – Earn Love, Drive Change!

Who does not love dashboards? Humans love them. Aliens love them. HiPPOs adore them.

So why is it that we don't spend time creating customized ones for our stakeholders? After all, humans, aliens and HiPPOs have different needs.

Pledge to shift away from a one-size-fits-all data puke, and use your web analytics tool to create a customized dashboard.

One day, Google Analytics will default to be the Home tab when you log in, but until that blessed day arrives, just click on the Home icon in the orange top navigation. Then click on Dashboards, and what do you see? Oh yes! + New Dashboard. Click!

I love that phrase "Blank Canvas." So open. So full of possibilities. So much hope and wonder.

Now just because you can do anything does not mean you should. My process is to name the dashboard first. Seems odd, right? But by naming it, I am giving it a purpose; and a purpose requires asking questions and focusing. And great, relevant, dashboards spring from asking questions.

I named my dashboard: VP, Digital. It now has a specific audience and a purpose. Rather than data puking, I'm now forced to go talk to the VP of Digital and ask this question: "What are your business priorities for the next six months?" That will lead to: "And how will you know if we've successfully executed on priority x?" That will lead to: "Awesome, I know exactly which critical few Key Performance Indicators I'll be showing in our dashboard."

Boom!

Every element in the dashboard has a purpose and is tied to a business priority. She/he wants more Social traffic. You, the Ninja that you are, are showing all segments of traffic to give context (you rock!). She/he wants time on site, you have no idea why, but you add it (along with a sparkline that shows the trend – sweet!). It is a content site, so rather than silly things like page views you use Loyalty (more on this below) and you also show consumption of videos (events). Finally, you bring together Conversion Rate with the Goal Value delivered by the Social obsession.

Charming!

[Update: If you would like to download the above mentioned dashboard into your Google Analytics account please click on this link: VP Social Media Performance Dashboard.]

Pro Tip: Always, always, always let the Acquisition, Behavior and Outcomes framework be your guide. After you've created a dashboard, check to see that you have all three elements. If you don't, you are not showing the end-to-end picture. Without this you fail in your duty (and the data recipients will make poor decisions).

Create a customized dashboard for your Search team, one for your Display team, one for the folks doing onsite merchandizing, one for the nice lady that owns the ecommerce shopping cart and all the other key clusters of your audience. Give them hyper-relevant starting points, collections of "info snacks."

The cool bit is that in addition to standard widgets and simple tables, you can also bundle along your smarts into the dashboard and delight your users.

One way is to use the awesome built in inline Filters feature when you use the dashboard widgets, to show just the data that is relevant (did I already say less data puking? :).

In this case, I've done that by adding a filter to segment revenue to only show social value.

And it is not all social media, it is just the money made from the company's own social media efforts by using the right campaign parameter. I'm (secretly) trying to show the VP how much (or how little!) money our own efforts are generating. Smart widget, smart insights, smart decisions.

So go forth and multiply! Create a small cluster of hyper-relevant (secretly smart) dashboards!!

#2. Leverage Custom Alerts – Let Data Kick Your Butt Into Action!

Sometimes (actually frequently) it is not enough to rely on our own diligence in terms of remembering to log into SiteCatalyst and look at the right set of numbers (across a hundred reports!) to know what's up with the business. It is especially undesirable to be surprised about something awful happening to our digital existence.

We can't predict the unknown unknowns easily, but we can be magnificent at proactively identifying the known unknowns by leveraging the custom alerts feature in our web analytics tools. Here's a screenshot from Google Analytics:

These alerts will let you know if engagement on your website crosses certain thresholds or when the bounce rate spikes for traffic from Google or if there is a spike in conversions (praise the lord!). All things you know will happen, you just don't know when. Known unknowns.

With smart alerts set, you don't have to remember to check the data every eighteen seconds. An email, or a text message, will poke you into action. Your boss will be impressed at how you seem to always have your act together!

Here's one of my favorite custom alerts. I would like an alert when goal conversion rate for any day is greater than 25%. My normal is around 18%, so if it jumps up by that much I can get an alert and I can do deeper analysis to figure out what might have caused the spike.

You pick the period for comparison, your the necessary dimension and metric, add the condition, type a value and you're in business.

If you don't have at least five custom alerts set up, you can't call yourself an Analysis Ninja in training. At least not a serious one.

Five of my favorite alerts are in the second part of this blog post: Identify The Known Unknowns: Leverage Analytics Custom Alerts Here are more clever examples from the team at Google: Five Custom Alert Examples

Don't rely on yourself to remember to look for your site’s magic moments. Put yourself in position to be proactively informed when they happen.

#3. Use Table View Options – Faster Initial Insights!

Enough dancing around the outside of the tool. Let's rip off our clothes and jump into the cold inviting water!

It is very hard to quickly understand a lot of numbers when they are presented together. When you log into WebTrends or Google Analytics or CoreMetrics, you're lucky if the standard report does not contain five or seven metrics at the very least for every table row. Data puke!

Not only will you not see the forest, you'll be lucky to even see the trees.

My preferred path is to leverage the tool's built-in features for filtering/visualizing the data.

In Google Analytics there are a few super cute options. Click on the table like icon next to View. You can see five different ways to look at the data in any table: Percentage, Performance, Comparison, Term Cloud and Pivot. All exist to make your life easy.

My personal favorite is Comparison. This option takes the site average for a metric and compares the individual performance of every row to that average, and it visualizes the data for you.

For the top websites that refer traffic, I wanted to know quickly (without having to do the math) which source sends traffic that tends to see more than one page. AND I want to know contextual performance of every row with site average AND every other row. Hard? Nope. I simply choose Comparison. Then I choose Bounce Rate. And in two seconds…

Like every two-year-old child, I know that red is bad and green is good. GA is telling me is that Twitter (t.co) traffic bounces 14.59% more than site average. Ouch.

Scanning the rest of the table, remember I want contextual performance analysis, I can quickly see that I should love the GA blog, Linkedin and SEOmoz more and other folks a little less. :) But I am also now a lot more curious about Ycombinator. That is a lot of traffic. What post on YC did they come from? What content did they read here? Why might they not have cared for anything else? I can analyze and then identify an specific optimization/engagement strategy to reduce bounce rates.

You can literally do this for any metric in the standard tables in GA. Try to look at your top 25 campaigns and compare conversion rate. Or open the new search engine optimization reports in Google Analytics , for your Queries look at Impression and try Comparison for CTR.

Pretty cool. But that is not all.

I've always been partial to pivot tables in Microsoft Excel, hence it is not surprising that my second favorite view option in Google Analytics is Pivot.

Now I can create a lovely report, for example, to find "arbitrage" opportunities across search engines? Here's how you do it.

1. Go the keywords report (in Traffic Sources section). From View choose Pivot (as above).

2. Click on the box next to Pivot, type in Source, select it.

3. Click the box next to Pivot metrics and choose Visits (or whatever else you like, go crazy!).

4. Look at the performance. I typically look for anomalies. For which keywords do I get more traffic from Bing when compared to Google. Or Yahoo! compared to Ask, etc.

Every search engine's SEO algorithm is unique. For example I get twice the traffic for "digital marketing" from Bing than from Google. I use the data above to customize my SEO strategy for each search engine.

You can use pivot tables in pretty much every GA report.

In this case, I can more easily figure out which of my top pieces of content are delivering the micro-conversions that are valuable to me. I track these micro conversions as Events, here's my Pivot table:

Use your creativity when it comes to pivot tables and you'll be delighted at how wonderfully they help you answer hard questions.

One last bonus item when it comes to using tables in web analytics tools spectacularly: Use the in-line table filters. Just click on the link called advanced next to the magnifying glass on top of the table you are viewing (in any report).

Now, rather than looking at half a million rows and trying to find an answer, you can simply type in your question. In this case I only want the rows of data (keywords, campaigns, pages, products purchased, videos watched, whatever) only for those people who:

1. Saw more than 3 pages during their visit AND

2. Entered my website on the cluster of 900 pages about Aruba.

These people are of particular interest to me … I click Apply and, voilà, I have them cornered!

Using this strategy I can go to the standard table with hundreds of thousands of rows of data and quickly only look at data for my brand keywords or just for my email campaigns or just for people who visited more than 10 times or just for those who came via Yandex or just those that read a segmentation post or just those that donated or…. anything. And I can do it fast.

Why stare at a table, or worse just the top ten rows, wondering what to do? Speed up your time from data to information by using the Comparison view, Pivot tables and in-line Filters.

#4. In-Page Analytics – Re-imagine Traveling Through Data!

This is one of the hidden gems of Google Analytics, especially for traversing lots and lots of data in context of the web page itself. It is fantastic at communicating data, complex data, to people whose primary job is not data analysis.

The In-Page Analytics report takes all the data you would find in the Explorer and Navigation Summary reports (essentially all the links you have on a page and their performance) and shows it to you in an elegant visually appealing view.

There are two ways to get to this report.

1. Just go to Content > In-Page Analytics.

2. Go to Content > Site Content > Pages, then click on the URL you want (or use the in-line table filter mentioned above to find the URL), and click on In-Page at the top.

On top of the report you'll see the scorecard, or aggregate performance of the page via metrics like Pageviews, Unique Pageviews, Time on Page, Page Load Time (!) and Bounce Rate. Having the % of Total (grey text, small font below) provides great context.

Below that, in blue, green, red and orange I see the percentage of clicks on each link. I don't have to infer data in the table, it is all laid out for me nicely!

And note the orange bar at the bottom, it is particularly nice. It shows how many people click on links below the fold. The fold is defined by your browser size. As you resize the browser windows you'll see that number dynamically change. This data is extremely valuable for long pages, especially if you have valuable links below the fold. IF you're New York Times or Amazon, you want to know if people scroll!

This is so important if you are responsible for merchandizing. If you have a few different layouts of your web pages, this is a great way to know which links, promos, and annoying dancing banners are attracting the clicks.

But you don't have to watch clicks. Aren't clicks are the new HITS :).

You can click on the Viewing drop down (#1 below) and choose any goal. When you choose a goal, the display changes to show what percentage of people who click on a particular link go on to complete a goal in that same session!

In my case, below, 15% of the people who click and read the comments end up meeting my goal of going to Market Motive (and hopefully sign up for the Web Analytics Master Certification program!). But only 1.9% of the people who visit the Digital Marketing section of the blog do the same.

In this case you can also see that the links on the top are especially valuable for this goal. Only 9% of the people who ultimately went to Market Motive clicked on any links below the fold (and the fold here is pretty much the top of the blog post!). So I have to be particularly good at the information architecture on top of the page. Once they scroll, the chances for goal conversion go down dramatically.

I can do this type of "conversion click" analysis on any of my 8 goals. How awesome is that? With those insights, I can go and optimize my key pages for my individual business goals.

Imagine what you can do with your home page optimization if you know this. Now when everyone wants a link on the home page or the category pages you can show them which links your visitors are actually interested in and let data fight your political battles!

I rarely find anything really sexy (in an analysis context :) unless it comes with segmentation. You saw that in every single recommendation above. And my choice for this report is no different. You can segment like crazy.

When I use the In-Page Analytics report I don't want to look at all the traffic in one ugly bucket. I want to analyze groups of like type people, like type behavior. For example, I want to know how the behavior of search traffic is different from direct traffic. How hard is it? Three simple clicks…

1. I click on the Advanced Segments drop down and choose the standard segments (or one of my 50 custom segments).

2. I click on the In-Page tab to go to the report. (I was in the Pages report.)

3. I choose the metric I want. In this case I, selfishly, want to know if there is a difference the money I make (Goal Value) if Visitors from Search and Direct traffic click on the exact same link on the page.

4. Bam! Bam! Bam!

There is a substantive difference. When people come from search I make $142, on average, when they click on that link, but if they are direct I only make $58 (boo!).

Imagine what a gift this is when it comes to figuring out how to create the best landing pages. I know what the Search Traffic gravitate towards, I can now optimize their experience on the site rather than serving them random/generic links!

You can do this analysis for social media visits, for a particular keyword, for people who watch videos or download catalogs or, well, anything you can segment in Google Analytics (which is pretty much everything).

Forget tables. Be sexier. Let your site tell you what to do.

But there is one fly in the ointment.

The implementation of In-Page Analytics in GA is frustrating and silly. When you first go to see that report (if you are using Internet Explorer), you are going to see this insane warning:

If that box was not scary enough, the whole darn text is wrong. My ga.js (and most likely yours) loads from Google, and I have the snippet on my site. #aaaarrrrrhhhhh

In addition to the above you'll also see this at the very bottom of your browser window at the same time…

So, how do you make this report work?

It is supremely annoying that the Google Analytics team and front end does not make that clear.

But it is simple. Ignore the first error, and click the "Show all content" button on the second error. Magically, everything will work.

If you are using an older version of IE you might see this error:

Classic useless error. Don't click the default Yes – just click No and the report will work fine.

In Chrome, mercifully, it works fine with no errors.

While it is disappointing that the error shows up initially, the report itself, as you can see above, is quite valuable. I hope you'll give it a chance.

#5. Perform Recency, Frequency & Pan Session Analysis: Fall in Love with People not Page Views!

I'm a big fan of pan-session behavior. What happens across multiple visits by the same person? (And are there multiple visits at all in the first place?)

Having grown up in the traditional business intelligence and direct marketing world, I'm also a huge fan of RFM analysis .

In Google Analytics, you'll find them in the Audience Section under Behavior.

Here is a great example of the type of business-critical question you can answer with these reports. We are a photo-sharing website (think little sister of Flickr ). We make money on content consumption (via display ads) and premium subscriptions to the site. But we can only make money if other people come and upload their photos, and still others come to view those photos. Long-term success is achieved if our audience becomes loyal and we don't have to keep spending money on Google and MSN and Yahoo! renting traffic.

So, are they loyal? Check out the Frequency (count of visits) report. It shows how many people visited only once (42%) and how many 2 times and 3 times and… so on and so forth.

For this business the results are fantastic:

While a chunk of people come only once and never again, notice how bottom loaded the report is. 43% of the traffic comes to the site between 9 and 200 times in a month! That is loyalty! We can feel better about our marketing and engagement strategy.

How about for your site? Are you having one-night stands or building longer-term relationships with your audience?

Another nuance of loyalty is that you not only want people to come to the site multiple times, you want a shorter gap between two visits. You're looking for recency. This report show us how spectacularly we are doing for our photo site:

The vast majority of visitors visit the site every day! Analysis Ninjas know that the 83% number above includes new visitors to the site, so we should subtract that (why are web analytics tools so annoying some times!). But, it is still a huge number, and we should be happy.

How about for your site? Does the recency line up with, for example, the rate at which you publish new content/launch new products/execute new marketing campaigns?

Another facet of pan-session analysis is looking at the number of visits it takes to convert our visitors. Not everyone wants to marry you on the first date, right? (Yet almost all digital marketing and almost all landing pages are constructed as though this were the case. Sad.)

My favorite report to use to answer this question about customer behavior is the Path Length report in the new Multi-Channel Funnels section in Google Analytics.

In our case, around 23% of our conversions happen in the first visit, and then there is a long tail and then look…

OMG! 48% conversions that took 12+ visits to convert! We can specifically look at that segment of customers and figure out what combination of Google, Atlas, YouTube and Email Marketing (or whatever) it took to get that conversion!

We can use this data to create better experiences for our users. We can optimize the ads and marketing messages (across channels) it took to get these folks to come to our website multiple times, prior to conversions.

This is hard work. Most definitely senior Analysis Ninja work. But that is how you win big. When you skip this type of analytical effort, you doom your company to live on scraps. And really, who wants that?

#6. Matched Query Type, Keyword Position, Day Parts: Sexier PPC Analytics!

I've always been a bit miffed that most web analytics users are less than sophisticated when it comes to analyzing search/AdWords campaigns. So many companies spend so much money. Why not do some incredible analysis? Especially when our web analytics tools make it so easy.

My first example is a good representation of that.

Most people don't realize that when you view the keyword report in the AdWords section, you are looking at the key words/key phrases you bid on, not the queries that were typed by users into Google. If you base you AdWords success on just the keywords report, you might end up making substantially poor decisions.

For that reason, I love and adore the Matched Search Queries report (in the Advertising section). It shows what users typed into Google when your ad was served. The report is standard in Google Analytics.

All you have to do is click on the box next to Secondary dimension and type in Keyword. Now you are looking at both the word you'd bid on (right) and the word the user typed (left):

You can quickly see the differences between your bid and the matched query (#2 above). The next obvious step is to look at the performance and optimize your Match Type strategy based on the results.

In the screenshot above you can see that the keyword bid on was "calico critters toys." Those ads were matched to the user queries "little critters toys" and "calico critters cloverleaf manor." And there was a 9 points difference in the bounce rate (ouch!). Good to know. Go back, optimize your match types in AdWords and optimize your landing pages.

Fun right?

My second favorite? Keyword Positions report. Why? SEOs obsess about their rank on the search engine results page (SERP). That obsession is often valueless. But for your PPC campaigns? Obsession will deliver glory!

So why not analyze which position your ads show up in when it comes to AdWords?

A combination of your max bid, your quality score, match type will determine the position of your ad for every search query. Google Analytics will show you that information beautifully.

Here it is…

Just click on a keyword and the visualization on the right comes to life. Now you are better able to determine which position gets you the most clicks. Top 3 is better than Top 1 (the position your boss was obsessed about – "I WANT #1 RANK!!"), and neither can beat Side 1 (the cheaper position!).

Another lovely thing you can do with this report is look at the performance once those clicks (ok, people) land on your website. Just click on the down arrow and choose the metric you want, Bounce Rate in my case below:

You can see that every position has a bounce rate. Side 1 still has the best performance. You don't have to just use Bounce Rates. You can also use % New Visits, Time on Site and Pages/Visit as your metrics. The goal is still the same: find the position that delivers best performance.

If a position works optimally for you, then you can use AdWords Automated Rules to have your ads show up in particular positions.

You use your money wisely and get higher ROI. #winning

One small bonus tip: I love looking at the AdWords Day Parts report a couple of times a month. Most of the time, the data shows the normal trend, more clicks and conversions during the business day.

But every once in a while for certain keywords, or segments, I'll discover that the pattern is very different. For example, you can see below that the conversion rate actually peaks at midnight…

We did not know that people were searching for us late in the night, and they were highly qualified (!). Hence sadly our AdWords budget was capped at that time, we did not to "waste" money. Sad. Once we saw this data we loosened up the budget and picked up loads of extra conversions.

You'll discover other delights like this. In the view above I'm using the Compare Metric feature of Google Analytics. It is cleverly hidden in light gray text on white background on the top right of the main graph in every report. Just click on the drop down and choose the comparative metric you want.

If you spend money on AdWords, be smarter about the analysis you do. There is no better way into your boss's heart. If you spend money on other types of campaigns, I hope you'll find inspiration above to do interesting off-the-normal analysis.

#7. Custom Report Filters: Bring Deeper Relevance To Your Custom Reports!

It is hard to keep pace with all the changes that web analytics vendors make to their tools. I wanted to share two clever features in Custom Reports that make them even more super magnificent (and mandatory if you are a Ninja!).

The first one is the filters that are built right into the custom report you are creating.

I love custom reports because you don't have to data puke any more, you can just show the data that is needed. [Helpful post: Leverage Custom Reports For Better Insights]

Now you can focus even more by embedding the segments your leadership cares about right into the report!

Above is my awesome Visitor Acquisition Efficiency Analysis report (click link to get it). But if my leadership team is only interested in understanding how good the company is at acquiring mobile traffic, I can include a filter right into the report (see above) to just show mobile traffic.

And if they only care about USA (and why not?), I can limit my custom report to show just that. Why bug them with everything?

Now my custom report is not just relevant, it is hyper-personalized. I have shortened the distance between data and insights.

Your imagination is the limit in terms of the clever filters you can build into your custom reports.

Second tip on custom reports: Create micro-ecosystems.

I was not too pleased with the eight or ten standard mobile reports and their data views and all that. So, why not create my own custom report? Wait, not just a custom report but rather replace all the standard reports with my one Awesome Mobile Report? [Click to grab it!]

My primary strategy was to create three tabs. One for device drill downs and metrics, a second one for search performance, and a final one to understand performance of content:

Each tab has specific metrics relevant for just that dimensions (Device, Search, Page), and it is all in one place to give decision makers one go-to place for all their mobile performance needs.

Same outcome: Faster movement from data to insights.

You'll know you are an Analysis Ninja when you can replace 100% of your company's reporting needs with just five such micro-ecosystems. (Not 100% of the analysis needs, 100% of the reporting needs.) It is entirely possible, and think of how easy your life will be then…

And I have to tell you it is a tremendous amount of fun.

One final, surprising, way to do the data hustle with GA…

#8. Quit Google Analytics: Move Beyond Tool/Creativity Limitations!

Sometimes all the reports and features are simply not enough.

You can't understand why it is impossible to see Keywords in rows and a monthly count of Visits in columns. Weird, right?

You can't fathom why something so amazing and straightforward as tag clouds are so uncool and utterly useless in Google Analytics.

You are frustrated with the insane report/table formatting requirements by your business leaders. They want a particular font type, or your dashboard goes into the junk folder!

When you run up against the tool's limitations, weird implementations by tool vendor, or hard-to-please clients… quit the tool. Get the data out. Unleash your creativity.

It is, of course, possible to take data out of Google Analytics. The straightforward way is to simply use the Export button in the top nav.

The problem is the second image above. You can only download 500 rows easily, when you actually, in this case, have 122,397 rows of data. [And you all know how much I love mining the long tail by moving beyond the top ten rows of data! Not possible with 500 rows.]

Option one is simple, yet slightly painful: "Trick" GA into giving you all the data that you want to download.

Happy?

Now here's the caveat.

Using the method above it is possible to download all of the 122,397 rows of data. The challenge is that you might not have enough cache allocated to your browser. Or you don't have enough memory. Or you might have an older browser. Or one of so many things that will cause your browser, not the web analytics tool, to hang. It is just hard to get that much data rendered into a browser.

Of course where there is a problem, there is an incredible solution.

If you want to export all your data frequently just use the free Google Analytics API. It is pretty cool. [Tools like WebTrends and Adobe have APIs as well. WebTrends is free, for Adobe API pricing please call your Account Rep.]

If you want to have a quick naughty flirtation with the GA API, visit the Data Feed Query Explorer. If you enjoy that (and you will, because that is what naughty flirtation is all about) get more context about the Google Analytics Core Reporting API. End your journey devouring the handy dandy Dimensions & Metrics reference guide.

Now allow your inner geek to rejoice!

If, like a majority amongst us, you want to skip the flirting and jump to marriage, mosey over to the Google Analytics Application Gallery. Everything you can dream of is there. Data Warehouse integration? There. Business Intelligence? Got it. Campaign Management with a side of Email Marketing? Sure. Mobile Apps and Widgets and Gadgets? Absolutely!

It is pretty cool to use the API to integrate your offline phone call data with your Google Analytics data, understand the demographics, gender, income, etc. of people who come to your site, or overcome the sub-optimal standard GA Funnel report by using PadiTrack.

Going back to extracting data efficiently and making magic, three apps you'll find particularly useful are Excellent Analytics , Nextanalytics and GA Data Grabber.

Excellent is free (hurray!). Nextanalytics costs $199/year and GA Data Grabber costs $299/year. Both tools are full of pre-built dashboards, reports, cool visualizations and easy ways to collect data from tons of sites and pull it all nicely into one report. Both also contain loads and loads of automation capabilities. They allow you to shift from 90% data collection and 10% actual work, to 10% data collection 70% data analysis 20% social media time-wasting. What's not to love? :)

It may seem odd to spend money on a free tool. But not paying just one dollar a day to make your life better is most likely a Class 1 analytics crime. Don't commit crimes!

Regardless of if you use WebTrends or Google Analytics, the API allows you to do better reporting, smarter analysis (with offline data) and automate the mundane. Create a better life for yourself.

So that's it.

Eight simple ways you can hustle with data, convert skeptics, earn the love of your website visitors, and improve profitability of your web business. All without leaving the confines of standard reporting features already inside your tool (except that last tip).

I hope this post will accelerate your mastery of Google Analytics (or IBM or Yahoo! Web Analytics or Open Stats). And I hope it will mean less time spent wrestling data and more time taking action based on intelligent insights.

Good luck!

As always, it's your turn now.

Are the strategies outlined above already a part of your daily data hustle? Which recommendation surprised you the most? Which one do you think is most over-rated? If you are a GA power user, did I miss a feature or approach that you love a lot? From your experience, with any tool, do you have a tip to share with your peer readers?

It would be wonderful to hear from you. Please share your feedback, ideas and awesomeness via comments.

Thank you.

Google Analytics Tutorial: 8 Valuable Tips To Hustle With Data! is a post from: Occam's Razor by Avinash Kaushik