Occam's Razor by Avinash Kaushik » competitive intelligence

Excellent Analytics Tips #20: Measuring Digital "Brand Strength"

Avinash Kaushik — Mon, 14 May 2012 10:05:59 +0000

A lot of digital analytics focuses on direct response (conversions, leads, etc.). But there is an additional valuable, and sexy, focus of our marketing we don't give enough analytical love: Branding!

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…

1. This is just data from Google.com. So it just reflects what is happening with the share of people who use Google.com to find what they are looking for.

If I were doing this analysis in Russia I'd be using Yandex, in China I'd use Baidu, etc.

2. This type of analysis works best for medium to large brands. If you are managing a small brand, this might not be an optimal way to understand your brand strength. (Primarily a function of how this data is collected and processed.)

3. These are just brand queries. It is possible that brand zebra is really horrible at getting people to think about their brand, but they are so magnificent and awesome at getting people to visit their site via generic and long-tail queries.

Or you might hear brand zebra say "no one goes to Google since we primary use TV for advertising, they all go to our website directly." Or they might say "everyone in the world has bookmarked our site, no one would go to Google."

All good points.

To account for these objections/scenarios an Analysis Ninja should get additional context for the brand strength analysis done using Insights for Search. You already have the search behavior data, go get the overall traffic picture from a competitive intelligence tool.

I recommend running a report like this one:

I'm using www.compete.com above. You can see how this graph is wonderful context for what you did above with Insights for Search. Now you can answer those objections/scenarios.

4. This is but one (perhaps the most easily accessible) source of data for measuring brand strength. There are other ways to measure brand strength that are also wonderful. Primary market research comes to mind as another solid option.

5. I'm sure I've missed a caveat (this is a dangerous business!), please add your caveats in comments.

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

Google Analytics Tutorial: 8 Valuable Tips To Hustle With Data!

Avinash Kaushik — Mon, 30 Jan 2012 10:52:44 +0000

It is painfully heartbreaking to realize that a very small tiny number of people who have access to web analytics tools actually use them.

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.

Step 1: Go to the report you want all the data from. At the bottom of the table, change the number of rows in the "Show rows" drop down (see immediately above). Go from the default 10 to, say, 25.

Step 2: Go to the URL address bar, you'll note that the URL looks something like this:

https://www.google.com/analytics/web/#report/trafficsources-organic/a278315w434904p401908/%3Fexplorer-table.rowStart%3D0%26explorer-table.rowCount%3D25/">https://www.google.com/analytics/web/#report/trafficsources-organic/a278315w434904p401908/%3Fexplorer-table.rowStart%3D0%26explorer-table.rowCount%3D25/

Step 3: In the URL address bar change the value after the %3D that follows explorer-table.rowCount. Like so…

https://www.google.com/analytics/web/#report/trafficsources-organic/a278315w434904p401908/%3Fexplorer-table.rowStart%3D0%26explorer-table.rowCount%3D1234/">https://www.google.com/analytics/web/#report/trafficsources-organic/a278315w434904p401908/%3Fexplorer-table.rowStart%3D0%26explorer-table.rowCount%3D1234/

See 3D1234 at the end? I added the 1234 to download 1,234 rows of data.

Now hit the Enter key on your keyboard.

Step 4: Scroll up, click on the button Export and click on the option you want (typically CSV for Excel).

Step 5: Use your Analysis Ninja-like powers to create something amazing with this data. Like a better visualization. [For example, go create glorious tag clouds with Tagxedo or Wordle .]

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

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

Avinash Kaushik — Mon, 12 Jul 2010 08:56:43 +0000

Stale.

One thing that I never want to be.

We all have a tendency to learn up to a point, we get comfortable and keep chugging along rarely investing in our ongoing education.

I call it the slow but sure path to irrelevancy.

Let me share my prescription for avoiding irrelevancy: Try new things.

Simple right?

At any given time I have six or seven interesting tools running on this website. That's not including others I actively seek out around the web. Most of them are not even related to my current job or problems I know of. And that's on purpose.

I want to constantly be in the know of new and more clever ways of working with data, tools that are often solutions to problems we don't know we have yet or tools that are sometimes seeking problems to solve!!

Irrelevancy is not fun. Stale people are not appealing (just like, as your mom taught you, a week old bread). If there is one thing you take away from it post I hope it is the importance in investing in yourself / your education / your ongoing awesomeness.

In this blog post I want to share four analytics tools that I have been playing with for a while… tools that solve an interesting problem… tools that point to what might be in terms of our near term analytical future… and in almost all cases they don't even know!

I love doing this, I hope you'll have as much fun as I do.

First Some Context.

Remember I am the creator of the 10/90 rule of investment in web analytics.

I had created the rule many years ago, early into my job at Intuit, and quite simply it states:

If you have a budget of $100 to make smarter decisions on the web…. invest $10 in tools + vendor contracts and invest $90 in people (big human brains inside or outside the company to do analysis and the process of producing insights).

When I had created the rule Google Analytics did not even exist!

The rule was borne out from my own experience having inherited a world class tool we were paying $250k a year for and produced crap. Well not crap… lots of data that no one cared about or actioned. I threw out the world class tool, purchased ClickTracks for a fraction of the cost and put money into Analysts and boom!

Ok not boom overnight… but over the course of a few months the org started to be more data driven, because analysts we hired produced analysis. That fed a virtuous cycle. More analysts. More insights. More desire to be data driven.

So as you look at the tools below remember the 10/90 rule.

In the end it does not matter who has the coolest or the biggest tool. Or for that matter how many tools.

People matter.

You matter.

Remember that, at least for the rest of this post. Ok?

Let's go look at some tools…

Measuring "Invisible Virality": Tynt.

Tynt's promise is simple. Add a piece of javascript to your web page (do a View Source on this page to see it), and it will tell you how often your content is being copied.

Copied! Say it ain't so! :)

[Please click on the above image for a higher resolution version, including all the other metrics.]

In the last month data was copied off one of my posts 5,616 times, with most of it being content and some of it images.

But that's not all.

If you look at the higher resolution version (click above) you'll see it also reports other data like Visits Generated etc.

The way it works is that when someone copies a piece of content Tynt adds a little bit of additional text and a trackable code with a hash (#) at the end of the url from where content was copied.

Like so… the text that was copied from my blog is the first two lines… the Read More and link was added automatically by Tynt…

When people click on that link Tynt can report visits generated, page views, where the links were posted (in case there is a referrer) etc.

There is additional data like how many of your copies created links that were posted and then clicked on…

Gold are places were the copied text was pasted with the additional "Read more: http://…" text+link were also posted AND someone clicked on it.

You'll note that Tynt's selling point is connected to SEO. The idea that your copied text creates links back to you which in turn creates visits back to you, and per Tynt, better SEO goodness. You know links and page rank and what not!

I *personally* do not see much value in all that data. Two reasons:

1. Most likely the additional text+link will be posted as is only by someone who is quite careless and perhaps only on the least desirable sites. I mean if someone smart's going to copy they'll be clever enough to get rid of the link+text. :)

2. Search engines are complicated little beings. The days of just inbound links counting towards SEO goodness are long behind us.

So I am less enamored by Tynt data that focuses on all that.

I love the data you saw in the very first screenshot, and I absolutely love this…

[Please click on the above image for a higher resolution version, including all the other metrics.]

The first screenshot shows how often content is being copied and the above indicates the blog post / web page where the content is being copied from.

Why is this cool?

If you are a regular reader you'll notice that at the end of every blog post (before the start of the comments section) is a Topsy widget.

It measures how often a blog post is tweeted/retweeted. Goes viral. Higher the number the better, makes sense?

I also measure the # of Comments Per Post as a measure of how "engaging" / "valuable" people found the content to be. Looking at how often it was tweeted/retweeted is one more layer of information in understanding what subject / ideas in a post / things I write are well received by people and which are not.

But.

Both the above attempts measure two minorities.

1. The rarest of the rare who post a comment.

Context: I write twice a month. This blog has around 70k Visits a month, 39k Feed Subscribers and the average number of comments on each blog post is just 35. Minority perspective right?

2. The rarest of the rarest of the rare who are on social media. Who tweets after all. :)

The cool thing about Tynt is that it allows me to get some sense of "engagement" / "perceived value" / "Like" with the v a s t majority of people who will neither submit a comment nor write a tweet.

People who still use email. People who like something I wrote so much (or hate it so much) that they copy the text and paste it and forward it to others. Or copy the text and post it on their blogs (without attribution of course :)).

I like that a lot.

This entire interaction that was completely invisible to me is now a bit more visible. I can measure the "invisible virality" / "spread" by this big huge non-commenting, non-tweeting audience.

In the time period above I had written 4 posts (5,616 times copies). Check this out… It turns out the post with the fewest comments, just 25, and the fewest tweets, just 100…

…was copied an astonishing 506 times, when all other posts were copied in small double digits.

See what I mean… something I would have perhaps considered to be only a small success turns out was a huge hit with the blog's audience. I just would not have known that so far.

Here's another interesting application. . . Lots of people are measuring "influence" of a blogger (/ piece of content) using data from the "minority activity" (comments, retweets etc) and selling it as the complete truth. But how can you do that without some insight from the majority?

Tynt shares one very interesting piece to the puzzle that perhaps in the future fit some place where we can use it with all other context we have.

Invisible Virality. Cool right?

Applying Smarter Ideas to Measuring "Sentiment": Analyze Words.

Raise you hand if you are in the "If I am any more excited about doing sentiment analysis then I'll pee in my pants".

So many raised hands!

Here's the problem: Most solutions stink. Not just stink… dinosaur's breath after a meal stink.

We are algorithmically trying something that as yet does not lend itself to algorithmic measurement… "emotion". It is darn near impossible to cleanly buckets feelings and nuance into clean Positive, Negative, Neutral buckets.

We, computer programs, are simply not there yet. [Though I am absolutely confident that we will get there at some point.]

For now you are most likely wasting time (and money). Sorry.

Here's the other problem…

Even if it works… I don't think it works. [What!]

Let's say you have a 100% perfect human read and 100% human categorized analysis on hundreds of thousands of rows of text. Clean into the three desired categories. Like in the image above.

Now pause for a second and think… what could you do with this?

You have aggregated data into three pieces and we all know aggregated data stinks at delivering insights!

That does not mean wanting to identify insights from lots and lots of text is not prudent. It is.

I like a much more nuanced approach.

Analyze Words applies one such nuanced approach to text analysis.

It uses the well established and long use LIWC (Linguistic Inquiry and Word Count) methodology to categorize all your delightful text (in this case your tweets).

Why the LIWC? Here's the idea behind the LIWC:

"The ways that individuals talk and write provide windows into their emotional and cognitive worlds."

Cool right?

You go to Analyze Words and you punch in your twitter id and bam (!) your "psychological" profile, or in this case mine…

Nice eh?

No simplified over promise under deliver aggregates!

The three categories and 11 sub categories provide much much much more nuanced understanding of what your text is saying, in this case for your twitter profile.

Why is this cool?

In this case measuring "Personable": Engaged in other people's well-being and at peace with expressing your own uncertainty about the world. High Scores in personable use positive emotion words, ask questions, express their own ambivalence and reference others frequently.

Better than positive, negative, neutral right?

Or "Analytic": "If law school exams were a persona, they would rank real high in this category. Ample large words and phrases that include complex thinking styles (e.g. "if – but not …")."

Love it!

Two magnificent things about this approach (remember it's not the tool, its what you do with it :))…

1. It is very sophisticated in the approach it is applying. Nuance and segmentation rule the day. There is nothing, nothing, more sexy in the world of web analytics.

2. It is immensely actionable. You can quickly see areas where you are scoring well, where you are not and you can start to take action to fix things!

Of course you can do even more.

You know how you are doing… now compare it to your "competition" and find their strengths and weaknesses…

When you do competitive analysis, like above, find contrasts with your own profile, what your brand stands for in the world and their brand stands for.

Highlight differences where you brand strength is strong. Hopefully they'll discover where they stink and for the sake of humanity fix that.

Nice eh?

Analyze Words provides a glimpse of an approach that I hope others follow.

Rather than trying to find short cuts, where none exist, and provide aggregate data, where it just gets crapified, follow a well established methodology while leveraging segmentation and nuance.

We've applied it just for Twitter in the above case but you can easily see how you could apply it to call center data, tech support websites, forums, online survey open text voc answers and so much more.

Applying Simpler Ideas to Measuring "Sentiment": StatsIt.

StatsIt started off as a differentiated web analytics tool, but has morphed into a delightful social media monitoring tool.

[Update: Oct 18: StatsIt is evolving its solution. But in this section my hope is to focus less on the tool itself and more a type of analysis that we can use in our daily life.]

It's approach is to index blogs and tweets and delicious and twitter and youtube and on and on and analyze that data to find yummy actionable insights about your social media presence / activity.

Like all tools it gives you pretty charts…

Sweet, now you know how much "activity" is happening. Give it to your boss, she'll be impressed. You on the other hand realize "activity" rarely has insights.

I want to focus on just one part of StatsIt that I adore because of how simple it is in its brilliance when it comes to finding insights from lots of text.

StatsIt has indexed a ton of content from all the social web activity. When you tell it your brand terms (or just your brand name, in my case "avinash kaushik") and it churns through that social web data to provide you with something awesome…. a tag cloud!

[Click on the image for a higher resolution version, along with a peek at other metrics.]

Why is this cool?

Mikko and his team have taken 1,000 words from the English language that are connected to emotion. Good emotion, bad emotion, ugly emotion.

They look at their social web data and in that they look at the words around your brand mention and finally identify the emotional words people are using in context of… you!

The tag cloud above shows the emotional words use around mentions of me for a month's worth of time.

Without having to read all the text I can at a glance now get a really good understanding of the tone and texture of activity around my presence. More importantly it does not take all that long to figure out what emotions should be there but aren't.

A very simple, effective and elegant solution to a complicated problem.

Oh and guess that happens when you click on one of the words in the tag cloud?

You are right… it takes you directly to the text from all the data that StatsIt has collected!

By clicking on the words you are essentially segmenting your data and drilling down to the text (tweets, blog posts) where you can learn more about what the person was saying when they express, say, "great" as an emotion. :)

Effective "sentiment analysis" baby!

Why can't we be this simple in other places?

We are always seeking complexity. Here are two ideas that popped into my head from the StatsIt's approach that might apply in other places.

We collect lots of open text from our online surveys right?

Rather than finding the perfect answer to what's expressed in the text, and of course getting it wrong, why don't the vendors show us a emotional tag cloud?

Can there be a better / easier / faster way to allow us to make sense of all that text, leverage as a segmentation tool and find insights every day?

Vendors! Come on!!

Another idea.

Reviews are important. Most ecommerce sites have them.

But why is it that we only see "quantitative" analysis of the reviews? 5 stars. 3.2 moons. 61% rotten tomatoes. Etc etc.

The richness of the review is only partly in the quantitative analysis of the rating. The real sweet nectar is in the words people write in reviews.

I recently gave a talk at eBay. So let's use that as an example.

You get quick quant rating on eBay that you typically use. But perhaps the real gold is here….

This seller, me, is 100% positively rated.

Now let's say that you want to buy a Sony digital camera that is listed by both me and Emer. We both have 100% positive ratings for our 60 or so prior eBay auctions.

How can you best decide if you should buy from me or Emer? You can't possibly read 120 reviews, or even scan them quickly.

Now would your life be much much easier if eBay choose to provide an "emotional tag cloud" for both Emer and Avinash?

Very quickly you could see that while we both have same quant ratings it turns out that my emotional cloud shows a neutral to positive feelings expressed while Emer's is outrageously positive.

Now is it easier to decide who to buy from?

As our dear friend Sarah Palin would say: You betcha!

So why does eBay not provide this simple emotional tag cloud?

Or for that matter Trip Advisor or Amazon or any site that hosts reviews and ratings?

Simplicity rocks. Especially when it's actionable.

Quick, Efficient, Effective Mobile Analytics: Percent Mobile.

It is always a really good idea in web analytics to understand how data is captured (case in point the delightful blog post on Competitive Intelligence data capture).

No where is this more true than when it comes to mobile analytics.

There are many methods of collecting data depending on the platform you are on, and if Steve Jobs gets upset he can totally shut you down with a mere update of his TOS! :)

I am not going to cover all that here today. For those of you who already have my second book Web Analytics 2.0 please jump to Page 250 to learn all about data collection options, platform limitations, challenges with campaign analysis and finally reports and KPI's you should measure for mobile.

In this blog post I want to share a lightweight wonderful mobile analytics platform called Percent Mobile.

Now most web analytics tools, like Google Analytics and WebTrends and others, will capture and report data for javascript enabled smart phones (like the iPhone, Android and some Nokia phones). Honestly that is all the traffic that is of commercial value, so even if you miss the rest it is not the hugest of deals.

But all these "big boys" have simply "added on" mobile analytics to their tools. The result is that they suffer from both a lack of imagination and, this is important, truly great databases when it comes to devices and carriers and other unique mobile information.

Not Percent Mobile.

They have two incredible benefits:

1. A really expansive and accurate database and detection mechanism when it comes to mobile platforms.

2. A really simple UI and reporting layer, even your mom will understand the data.

They also have four different methods of enabling data collection, I am using their standard javascript tag on this blog (do a View Source).

Here is what the resulting data looks like…

[Please click on the above image for a higher resolution version.]

No hunting and pecking to find the data, like you would in Google Analytics or Site Catalyst or CoreMetrics. A quick at a glance view of how much traffic is mobile, key stats about the devices, the devices themselves (go iPad!!), vendors and operating systems.

If you compare this to your web analytics tool you'll notice almost immediately how much better this data is compared to what the "big boys" are reporting.

Click on the image above and you'll see a bit more clearly other really sweet metrics. % of mobile devices accessing your site via WiFi. Phones with touch screens and full keyboards etc.

[Can you imagine how cool it would be to segment your mobile traffic for full keyboard phone vs none and see which convert better. Or does access via WiFi mean more content consumption than via 3G? Etc. So cool.]

That is not all… if you scroll a bit more you can get a country map view, the networks used to access your site (AT&T still #1 for me!) and countries etc.

Of course it would be hard for me to like any tool that does not allow segmentation. :) You simply drag and drop on to the box on top..

And what would an analytics tool be without the normal search, referrer and all that data we have so come to love (and hate!).

I particularly like the "Activity Types" box at the bottom left, I don't know why web analytics tools don't categorize referrers by default.

I am also surprised at the long tail of referrers. Yes Google is big but there are 91 other referrers for this segment. More mobile SEO!

Why is this cool?

It might seem odd that I would like a tool that would give me similar data that I can get out of WebTrends or Omniture or Xiti or whatever.

The first reason is that, as mentioned above, the data is actually much better because of the databases that power Percent Mobile.

The other thing is that getting this data causes less pain than pulling my two front teeth.

Finally I so do like supporting pretty tools, especially if they have good data!

The one thing Percent Mobile lacks is some way of measuring any outcomes. I can certainly dig to my "conversion pages" but it would be great if they just let me just input them into the tool and then they could measure outcomes for me (even if it is like the Goals process in GA).

But if you want a light weight easy to use free mobile analytics tool just throw Percent Mobile on your site and have fun. Go to www.percentmobile.com , click Sign Up (top right) and use the Invitation Code "Avinash" (no quotes).

Mobile rocks no?

Summary Of Our Lovely "Let's Keep Learning" Cruise.

It is important to point out that I am not affiliated in any way with any of these tools / companies. I am also not recommending overtly or covertly that you buy / use them. That is totally your call.

Of course I would not personally use them or write about them if I did not thing they had value. :)

My sincere hope is that you'll internalize:

1. How important your ongoing education is. DBS: Don't be stale!

2. What it is that each tool does that is so unique, what unique problem each solves.

3. Why it is important that you can separate the wheat from the chaff, notice how I quickly put aside most data from Tynt to focus on just what was important to me.

4. Where are new places in your business you can apply things you learn from analytics, like in my example of emotional tag clouds for Ebay or Amazon.

5. Why simple and effective is better than expensive and complicated (even if "perfect").

I hope you got that, more than names of interesting tools.

I cannot tell you how much fun it is to step outside the world of Omniture and Google Analytics and other traditional web analytics tools. It stretches your mind and sometimes you look at these new techniques and data and you notice you are smiling and feel so happy.

Try it, and have fun.

[In case you were curious at the moment I am playing with these incredibly cool tools: PostRank, Next Stage Sentiment Analysis, SEO Effect, and Colligent. Each in its own way does something magical and quite unlike anyone else.]

Ok your turn now.

What do you think of the work that Tynt, Analyze Words, StatsIt & Percent Mobile do? Have you tried any of 'em? What obvious flaws did I overlook? Are there other tools you are using in the Viral, Social, Sentiment, Mobile space that you really love? If so would you please post them in comments?

Please share your feedback / critique / ideas.

Thanks.

PS:
Couple other related posts you might find interesting:

Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions is a post from: Occam's Razor by Avinash Kaushik

Win With Web Metrics: Ensure A Clear Line Of Sight To Net Income!

Avinash Kaushik — Mon, 28 Jun 2010 08:59:27 +0000

We have more web metrics and data than there are stars in the universe (slight exaggeration!).

Yet we stink at informing decisions. Our reports are ignored. Sites & online marketing continue to suck.

A large part of the reason is that a large part of our job seems to consist of glorified data puking, hoping someone will be impressed. After all there is so much data in those reports!! #fail

This blog post encourages you see the forest, the much hyped big picture, and shares a framework that will help you ensure that every single moment of your day is spent on activity that will be:

1. of value to your organization, hence appreciated and acted upon

2. has a clear line of sight to the one thing that matters: profit

If you don't want your professional life to be frittered away then please come along this short journey.

First some context…

If you have seen one of my keynotes recently then you have heard my near evangelical fervor when it comes to trying to convince you to compute Economic Value.

If you have Web Analytics 2.0 then you already know who much attention is paid to this concept in the book (jump to page 159 for how to compute it for your website).

The reason for this emphasis is to help fix our miserable failure at at creating data driven organizations.

To steal your energy away from being just in the report / data production business.

To encourage you to do better than spend a lifetime implementing analytics tools, building data warehouses, chasing the next shiny object.

My recommendation has been:

1. Identify your Macro Conversion (focus on this a lot!).

2. Report revenue. Report like crazy on the 2% conversion rate.

3. Identify your Micro Conversions.

4. Compute the Economic Value (see page 159). Show your bosses and HiPPO's the complete value of your website.

That last one will get any organization to sit up and pay attention.

Why?

Because for the first time in their young and passionate life they'll see the complete value your website is adding to the business. And because my dear it will be a huge number that no one can ignore! You are going to tie your work to the bottom line!

Revenue = Good. Economic Value = God! [Also slight exaggeration :)]

Professor Ken Wong's Magic Potion

Prof. Wong is the award winning Commerce '77 Teaching Fellow in Marketing at Queen's School of Business (and an awesome speaker, you should hire him for your next event!).

He took the stage after my talk and said, I am paraphrasing here, "Avinash did not go far enough in his keynote. Economic value is important but the only thing that matters is Profit!"

That was awesome!

One of Prof. Wong's key points was how the success of our work, as Marketers, is measured based on a lot of things but not often enough based on perhaps the most important metric of them all: Net Income.

Prof. Wong covered a lot of key points (as a MBA with a minor in Marketing I wanted to take off my clothes and jump for joy when he said the 4P's of Marketing are killing Marketing!).

I wanted to share two of his slides that left a lasting impression on me.

They are particularly applicable in the web analytics context. In sharing my interpretation of them my hope is it will change a little bit how you think about your work and success.

The very first slide, "Profit: The Ultimate Client Need", shares the key elements that need to function for the outcome (ROI) that causes companies to remain in business.

My interpretative points.

Net Income is driven by two important variables:

Unit Margins (how much you make on each X you sell or Y service you provide)

Unit Volumes (how many of X or Y you sell)

Margin times Volume gives you the golden metric Net Income!

[Keep this formula in mind, your life should be revolving around it else you are wasting everyone's time.]

Peel the onion back one more.

Unit Margins is in turn driven by two more variables:

Price (how much you charge for X product or Y service)

Cost (how much it costs you to make X or provide Y)

Price minus Cost equals Unit Margins.

Get it?

So if you want to have very high Margins you have two variables you can control. You can charge lots for your product or service (think of a Vertu phone).

You can also make it at the cheapest possible cost (no phone costs $100k, you make it for $300 and sell it for $100k).

You can of course also charge lots and lots and it costs you a lot to produce (think of a Tesla car). But give some thought to how you'll stay in business.

Continuing the onion peeling…

Unit Volumes, our other variable to have high Net Income, is driven by two variables:

Market Share (is your share 90% or 5%?)

Market Size (is that share of a market the size of Maldives or China?)

Both share and size are important.

You'll sell lots of X or Y if you have a high market share and the limit you'll hit is the size of the market (you can then play in the current size or grow the pie).

Line of Sight.

Having a clear line of sight means that you are able to map every metric you report on (or better still torture with segmented analysis to find insights) every single day directly to the strategic objective of the company.

Prof. Wong is suggesting, rightly so, that that strategic objective is Net Income.

And you have only one of four things that you'll move through actions your company takes: Price. Cost. Market Share. Market Size.

Here's my crystallizing question for you. . . .

When you report the metric Page Views Per Visit which of the four are you solving for?

How about with Bounce Rate? Or Time on Site? Or % of New Visits? Or Visitor Loyalty? Or…..

Is there a direct line of sight between what you as a Marketer are being incented on, or you as an Analyst are spending time analyzing?

If not, are you surprised that no one loves you? Sorry… I mean… no one loves your work?

Here is a simple exercise you could go through: Pick out all the metrics you are reporting today (on your dashboards and top reports). Try to put them into one of the four important buckets from Prof. Wong's slide.

The clear line of sight exercise. . . .

Were you able to cleanly bucket all metrics you currently report? Time on Site and Conversion Rate and Task Completion Rate and % Internal Site Search Exits and Cart Abandonment Rate and % of the Page Scrolled and % of Visitors Refreshing Pages and all the other sweet things.

Some of the metrics in the above paragraph are complete crap, you are wasting your time and everyone else's time with them. And you'll now discover that very quickly because you won't have a place where you can bucket them.

Other metrics will make you think harder. Where do you bucket Conversion Rate? Are you impacting Price or Cost?

What about Customer Satisfaction? Or Page Rank!

Not every metric will map cleanly, and that is ok. I had to think really really hard to bucket each of my metric in the above picture. Some of the metrics were controversial. But bucket I did.

If it turns out your web metric has no line of site then it might be time to kill.

If the work you do can't be mapped into Price, Cost, Market Share or Market Size then why are you doing it?

Before you dip your hands into Omniture or WebTrends or Surfaid, :), answer that question.

I know it seems like a lot of work for a "lowly" Analyst to do. It is. But without it there is little hope for your personal success (promotions / bonuses) or your company's success (higher Net Income).

"What Matters Most" Fishbone Analysis

As you look at the picture above it is amply clear that the metrics I have chosen in each of the four buckets are perhaps unique to me/my business.

The reason is simple… they are a reflection of the strategy my company is currently executing, i.e. our "world domination via an effective data driven online marketing plan".

This simple truth, that metrics should reflect current business strategy, is the reason I loved another slide from Prof. Wong's presentation.

It leveraged the same framework, but added "what matters most". . .

[Click on the image above for a higher resolution version.]

The focus is still on Net Income driven by, hopefully, improved Margins and Volume which in turn are driven by much beloved 4 levers of Price, Cost, Share and Size.

What is awesome about the "fish bone" above is that it drills down to the 14 specific strategies that most businesses will use to become great (or simply survive).

You Ms. Web Analyst now have a framework you can take to your Marketing Directors and CMO's to discuss which of the 14 strategies they are currently executing to drive the 4 beloved levers.

Ask any Web Analytics "Guru" or "Professional Speaker" or "I am so important you are paying me $5,000 an hour to give you generic advice Consultant" and they will always tell you that all good journeys in web analytics start with asking your bosses this question: What are the goals of the organization?

The advice is sound (and well worth $5k/hr). The problem is that we never get an answer from the customers of our data / our management. You are $5k x 8 hrs short and still none the wiser.

Get off the slow train to nowhere…. You now have a new BFF: Prof. Wong's "What Matters Most" slide!

Don't ask the generic "What are the goals" question. Ask "Of these 14 specific strategies which are we currently executing".

Once they tell you which ones (be patient, it might shock them that you are giving them something tough and specific to think about), you'll be in business.

The 5 strategies they pick from the right-most column will help guide you in terms of picking the right Key Performance Indicators / Web Success Metrics for your business.

And you know why a win now is guaranteed?

Because each metric you identify starts with a specific business strategy which has a direct line of sight to the 4 beloved levers which will have a impact on Net Income!!!

Minorly orgasmic right? [Trust me, you do this and you'll agree. :)]

Summary:

Recommendation #1: The Web Analytics Maturity Mandate!

For far too long we have been like toddlers… bumping into things, having a limited vision, working just what we know (which is little).

What I love about this approach is that it forces us to grow up. It forces us to understand what we are solving for: Net Income. It forces us to have a line of sight between our work and the ultimate goal: Net Income. It forces us to not live in our dungeon but rather take a well defined framework to enable the discussion that will yield wins all around.

No lip service to how important process is. This blog post shares what you specifically must do to succeed!

Recommendation #2: Win With Web Metrics: Steps

Here are the specific steps I recommend you follow for optimal execution of the recommendations.

Step 1: Learn Finance 101 and the terms outlined in the slide titled "Profit The Ultimate Client Need".

Step 2: Don't pick any metrics, don't run reports, resist the charms of Google Analytics, Omniture Discover2 etc.

Step 3: Meet with your Management team (or the senior most Marketing person) and identify which strategies outlined in "What Matter's Most" the company is executing (/wants to execute).

Step 4: For each strategy identified in step 3 identify the Web Metrics / KPI's with a clear line of sight to the 4 beloved levers.

Step 5: Use the Web Analytics Measurement Framework as the foundation of all your reporting.

Step 6: Spend you work day on focused segmented analysis to identify actionable insights you can report using the Web Analytics Measurement Framework that will help drive data driven actions on "What Matters Most" so that your company will improve in the one thing that matters: Net Income.

Step 7: The happiness you'll get from leading a meaningful professional life will make you irresistible to the opposite sex which in turn will lead to happiness in your personal life! Enjoy it.

A simple but effective 7 step process.

Good luck.

Ok now it's your turn.

Do you agree that a focus on Net Income and a focus on "what matters most" is key to success in web analytics? Can Web Analytics tie the work they do, the metrics they report, into Price, Volume, Market Share & Market Size? Or is our work simply not that important? In your job today how do you ensure line of site? Will you change anything based on the recommendations from Prof. Wong?

Please share your feedback / critique / ideas.

Thanks.

[UPDATE]

Zach Olsen, who blogs at By Data Be Driven, has taken the Clear Line of Sight framework outlined in this post and applied it to a medium sized eCommerce website. It is so wonderful, take a look:

[Click on the image above for a higher resolution version.]

Zach's effort is awesome for these key reasons:

Really clear line of sight from Business Objective to Net Income.
Clean flow from What Matters Most to 4 beloved levers (Price, Cost, Share, Size).
(This one I love the most…) Identifying of Targets for each metric! You can't be serious about Web Analytics without doing this!

I hope you are as impressed by Zach's effort as I was.

He has also done something sweet for all of us… he has created a excel spreadsheet that you can download and customize for yourself, and hence get a jumpstart! You can download it at this blog, bottom of this post: Web Analytics Framework Example. Please download it!

My thanks to Zach for his effort and for his permission to share it here.

[/UPDATE]

PS:
Couple other related posts you might find interesting:

Win With Web Metrics: Ensure A Clear Line Of Sight To Net Income! is a post from: Occam's Razor by Avinash Kaushik

Online Marketing Still A Faith Based Initiative. Why? What's The Fix?

Avinash Kaushik — Tue, 01 Jun 2010 09:11:55 +0000

The world of the intertubes should be a lot more data driven and awe-sexy than it really is.

Yet for all our collective efforts at writing and tweeting and kvetching online marketing is still based mostly on faith. Not data.

Surprising at so many levels right?

Last week I had the privilege of being invited to deliver the keynote at the annual CMA President's Dinner. John Gustavson, President & CEO of the Canadian Marketing Association, invites a hand selected audience consisting of the crème de la crème of Canadian executives from a vast array of industries. This year they were joined by senior Canadian government officials.

It is difficult to choose something for an address to such a diverse, accomplished and senior audience. My choice was the above thought, faith & data.

My plan was to challenge the status quo, deliver tough love, and inspire transformation.

There were no slides, no notes, just me up on the stage talking. Ok there were around 10 or so bullet items, the talking points. On the flight to Toronto in order to prepare I also wrote down the speech (though I don't read my speeches, so it stayed on the computer).

I wanted to share the speech with you in the hope that it helps you accept the challenging reality we face. I hope it also provides you with a practical set of recommendations to kick your work up a notch or two so we can all win at this web thing.

TV. Internet Marketing. Faith. Data. Problems. Solutions. . . .

__________________________________________________

CMA President's Dinner Keynote.

Good evening.

It is a pleasure to be here tonight and address such a beautiful audience. I want to thank John for inviting me.

My plan tonight is to present some thoughts on how to transform people and companies in the age of the Web, for about 15 minutes, and then address your questions. You are welcome to ask me questions about my talk or anything else connected to the web, companies – marketing – opportunities.

I must admit up front that I am as hard core as any evangelical born again Christian in my passion when it comes to the web. The raw innovation and empowerment that a connected digital world has unleashed is the reason I lovingly refer to it as "God's gift to humanity".

To truly appreciate some of this let us consider the world where marketing is done on faith. Television. Or for that matter magazines or newspapers or radio. All wonderful channels, that are needed and will be around for a long time! But when it comes to measuring success of our marketing efforts all of these channels are largely faith based initiatives.

Consider how we measure success of our TV campaigns.

At a time when there is massive fragmentation of channels and content consumption, where the head is becoming ever smaller with each passing day and the tail becoming really really loooooong, it is amazing that we rely on a measurement system of sampling a handful of viewers who help determine success of tens of millions of dollars of content and millions of dollars of advertising spend. It is outright mind blowing that we use a system whose own legal disclaimers essentially boils down to: "Our data is massively suspect".

Now think of how thin the ice is when it comes to measuring the impact of our precious marketing dollars in magazines and newspapers and other offline channels.

Yet we accept it.

We continue to use faith rather than data to make decisions on $120 Billion (!!) of advertising spend because we don't have much of a choice. We chalk it up to: "It is just the way things have always been." Or: "TV is really hard to measure, those boxes just don't connect or share." [It is rare that we blame the fact that we have not carried out our duty to demand more from both the channel and offline measurement systems.]

All that should explain why I have minor mental orgasms when I think of the online marketing channels and measuring actual business value delivered by our ever more precious marketing dollars.

Just thinking of all the data you can get is enough to put give you a temporary high. With 90+% accuracy you can measure the number of impressions of your ads. You can measure interactions with the ads. You can measure how many people end up on your websites. You can understand how many of them puke and leave! You can measure every facet of success (micro and macro conversions!!). You can measure revenue and economic value! For every dollar you spend! Oh my!!

And to think I have not yet started to talk about how finely you can tune your marketing by leveraging geographic and demographic and psychographic targeting. Leverage powerful metrics like Loyalty, Recency, Brand Perception, Task Completion Rate, Size of Second Level Network, Competitive Share of Voice and more. These are not "loser" metrics like visits and pageviews!

Oh, oh, and you can run experiments! You can fail faster! You can involve your customer in helping you choose the look and feel of your site or the prices you should charge for maximizing profit. You can run controlled experiments to measure incremental online/offline impact and balance the portfolio of media channels you are exposed to, rather than getting distracted by sideshows like "attribution analysis".

So much promise. So exciting. And these are all things you can do today. Don't get me started on the future and what lays ahead, the excitement of it all might cause me to faint.

Yet.

Yet if you look around you on the web you'll see that we swim in a sea of mediocrity. We still see irrelevant blinking banner ads. You'll see astonishingly sucky websites, belonging to come of the best companies in the world. You'll bump into advertising that is remarkable in how irrelevant it is to customer intent. You'll see horrid landing pages. You'll experience missing calls to action, rambling text, and waterboarding through Adobe Flash.

All of it largely driven by faith.

It breaks my heart.

If for no other reason than because your employees are frustrated (they want to be, and can be, so much better) and your customers are being tortured each and every day.

So in a channel that is so full of promise, so full of data, so empowering when it comes to relevance and creativity… why is it that we suck so much?

Based on my humble experience I have boiled it down to three important things:

1. The web has been around forever and yet it is not in the blood of the executives who staff the top echelons of companies.

Make no mistake, they are smart, they are successful and they want to do better. But the web is such a paradigm shift that if it is not in your blood it is very difficult to imagine its power and how to use it for good.

How do you demand innovation & creativity & radical rethink if you can't imagine it?

2. We still believe in and live in the world of "shout marketing", the thing we have practiced on tv and radio and magazines all our lives.

It is not that we don't mean well. But our mental models are jaded.

We still believe in getting lots of impressions. We want to interrupt. We don't despise irrelevance enough. We care about "eyeballs". Because that is all we know. Unfortunately the web (/interactive /digital /social) mandates new mental models, and we are the old dog that won't learn new tricks.

3. Our lousy standards for accountability.

Pause and think of how we measure success today. We measure "reach", we measure "exposure" and other such lame metrics. Partly because that is all we have been trained to expect.

We never say: "Here is a 100,000 for my search campaigns, please come back and report on task completion rates across the top three primary purposes and the economic value added." We never say: "Don't try to fool me with page views generated, did we impact page depth on our content site?" We rarely push hard by saying: "I don't care how frequently our content was updated, what was the impact on visitor loyalty." Or say: "Fine we improved online conversion rate by two percent, but what was the impact on the sales in our retail stores?".

Our bar for accountability is less than low. It is almost non existent.

So…. It turns out the problem is not the web, the problem is not the opportunity, the problem is not measurement.

The problem is you.

The problem is every person in this room.

Our raw understanding, mental models and expectations.

I am sorry. It is kind of a bummer to hear that.

But if you are the problem then the nice thing is that you hold in your hands the power to change your companies and bring about the promised revolution of data driven customer centric online marketing.

Problem identified, how do we fix it?

At the risk of being booed out of this impressive ballroom let me say that the solution is to Embarrass Management!

People who report to you and ask people who report to you to embarrass you.

Why is it awesome?

Turns out no one likes to have their egos bruised. Leverage this powerful force to start to address the three problems I had just outlined.

There are two specific strategies I recommend.

1. Leverage Your Customers.

They want to help. You just have to politely ask.

Not being polite is popping up a 35 question survey on your site. Being polite is inviting them to answer just a couple of questions about their experience when they leave the site. Being polite is uploading your latest "oh my god they are so going to love this (!)" design into fivesecondtest or usertesting and letting your customers share feedback at the cost of a few Tim Hortons coffees. Being polite is running a/b tests on your site so your customers tell you which call to action, piece of content, navigation structure or even product price will yield highest customer satisfaction AND revenue!

Leveraging customers means that when the HiPPO / Boss (perhaps you) opens her mouth to say: "I don't think that will work" or "I like that other way better" or "No one will buy a toothbrush priced $299" or "Twitter is dumb"…. you can say: "Why don't we mock up a quick experiment / online survey / media mix model to validate your hypothesis?"

Allow your customers to help you evolve your mental model. Allow you customers to teach you new and effective marketing strategies. Allow your customers to complement your existing intelligence and savvy.

And if it is hard to get to the above point…. leverage embarrassment!

I recently spoke at a major conference about how one of the top camera companies was disappointing its customers by stinking at the long tail of search. I searched for a digital camera, wireless printer and digital camcorder as a normal undecided customer would. None of my 18 or so searches threw up a single link for this company (not organic, not paid). And yet I was ready to spend $500.

Then I copied exact text from their website for multiple products and searched for them another 20 times. Result? They still would not show up.

Trust me nothing hurts like that raw view of massive failure of your online marketing on the single best acquisition channel on the web today.

Caused embarrassment. Forced a rethink at what is a glaring football field size hole in their marketing strategy.

Who wins? Customers. And the company, they will reduce acquisition cost and make more money.

When there was an argument at a top financial services company about what the home page, the holiest of holy properties per this company, should look like what do you think the company was going to do? Go with the version the President & CEO of the company liked. One smart person interjected to say: "Why don't we take your instinct and convert it into a HiPPOthesis?".

The CEO smiled. They tried three versions. The CEO's performed worst, on goals he had chosen. He still smiled after the test because 1. They made more money. 2. Avoided a big mistake. 3. Created happy customers. 4. He learned something new.

By involving customers companies have figured out that garish zebra print bed sheets are a perfect fit for being sold in their offline stores, identified the perfect song for their tv commercial, designed the best selling dvd covers, discovered pricing / discounts / product bundles that they would never have thought would have worked.

All faster and at a lower cost, with a higher impact on the business. Mental models evolved. Accountability increased.

2. Leverage Competitors.

I have rarely found a strategy that works better at elevating the game of any company than contrasting their efforts with those of their competitors.

It is astonishing that in a medium where your competitor is just a click away, the experience is absolutely frictionless, that we still live as if the burden and hurdles of the offline world exist online.

It is in comparing to competitors, known and unknown, that you can truly get the management to pay attention. Something about the size of the hit to the ego.

Here's an example.

Recently I visited the Sr. Executives of premier technology company and showed two sets of numbers. The ACSI has been measuring customer satisfaction for more than a decade. During that decade Apple's customer satisfaction went from 77 to 84. During that exact time period this tech company's numbers went from 78 (one point higher than Apple!) to 74.

Ouch. That hurts. Especially because they have poured many millions into "improving" the site (and a few million on analytics!).

Sure they don't have the "fanboyism" of Apple, yet Apple had that 10 years ago too. It is painful to realize that Apple started behind them and moved so far ahead, during a time where they not only did not defend their lead…. they actually regressed.

What do you think the management is doing now? Yep, questioning key things like who makes decisions, what the org structure looks like, how can they replace current hyper matrixed accountable structure with something that forces the right behavior at all levels.

Here's another example.

Rather than showing a CPG company how one of their sites was doing I took the liberty of comparing their tea website with their detergent website with their shampoo (personal grooming) website. It was astonishing how each was doing. For example the much smaller tea business was doing better than their key personal grooming business.

But I did not stop there. I compared them to an external benchmark.

What do you think I used? Their direct competition? No. I compared them to my blog's traffic.

It turns out I get two times the traffic when compared to all three of them combined!

Now my blog has nothing to do with a large multichannel CPG company. Yet I write a blog on an esoteric topic (I know that no one really cares about web analytics) and I write twice a month.

Yet I can get more traffic! Part time. With no marketing.

And they spent a couple of million dollars building their websites. To deliver what outcome?

Can you guess the result of this effort?

If you guessed a massive evaluation of their online strategy, ordered from the very top, then you would have guessed right.

Competitors provide a great contrast to your lameness or awesomeness. Be it leveraging the full power of online marketing channels. Be it creating optimal customer experiences. Be it bringing a new layer of imagination and accountability to your existence.

Embarrassment works.

Of course you have to do it right and be absolutely transparent that comes from a place of deep love and from a desire to to be better.

Because you see the goal is not to embarrass. The goal is not to be rude.

The goal is simply to provide context, fast. The goal is to get you, and your companies, to move beyond faith. The goal is to see the obvious potential in front of us. The goal is to throw away the shackles that have for far too long weighed us down.

That is what I mean by, now in quotes, "embarrass".

I hope you take away the passion I feel for making sure that advertising on the internet has to be magnificent and accountable. I hope you'll go empower your organization to "embarrass" you and that you'll do the same to them. I hope tomorrow will be the first day of a revolutionary transformation for your business.

Good luck!

__________________________________________________

The speech was received better then I expected (never easy to tell your audience they are the problem, or lay out tough to swallow solutions). I was profoundly grateful for that. The Q&A session following the speech was a of fun as well (always nice to get a chance to give my "It's not a OR world we live in, that's for super lame folks, it's a AND world!" mini sermon).

It's your turn now.

I would love to get feedback. What are your thoughts on the promise, the three problems and the two possible solutions to jump start a magical revolution?

Online Marketing Still A Faith Based Initiative. Why? What's The Fix? is a post from: Occam's Razor by Avinash Kaushik

Analyze This: 5 Rules For Awesome Impromptu Web Analysis

Avinash Kaushik — Tue, 04 May 2010 08:44:22 +0000

The hardest kind of "analysis" to provide is in response to open ended questions. That is why I love asking open ended questions!

They expose a person's critical thinking ability (something I highly recommend you test when you hire web analysts: Interviewing Tip: Stress Test Critical Thinking. Please).

They also help you understand if someone really grasps key concepts.

Recently on behalf of Market Motive, my start up that focuses on online marketing education, I had the opportunity to offer one scholarship for the latest round of Master Certification in Web Analytics.

So at the end of my 10 Fundamental Web Analytics Truths blog post I requested readers who were interested in the scholarship to complete this simple task:

Pick a site you love and tell me three things you would change about it, and why.

Seems straight forward right? It is not!

First I must say that I was overwhelmed by the responses (thanks!) and I was impressed with the time people took to do the analysis. I got wonderfully created pdfs / Word docs and well written emails. I was amazed at the creativity on display (which validated the fact that I have chosen to be in the right industry!).

Based on the responses, some wonderful and some not quite as wonderful (!), in this post I thought I'll share with you some tips should someone (like me!) ask you an open ended question ("what would you and why").

The first part covers 5 rules, sourced mostly from what people did not do. The second part contains 4 things people did that delighted me.

Let's go.

When someone asks you an open ended question, at least connected to web analysis, here's what's important. . .

1. Don't offer your opinion, at least not right away.

This is a very very hard temptation to resist. But try.

These were most common fixes people wanted to make on sites they loved:

Remove big header
I don't like the colors.
I would change the entire site design.
Reduce font size / increase font size.
The font type is not great.

I have to tell you that the last thing anyone wants to hear, in this context, is your opinion.

Not your boss. Not your friend. Certainly not the HiPPO (Highest Paid Person's Opinion).

Even if you believe that you are "absolutely right"! [Note: I often think I am "absolutely right". :)]

You and I are poor proxies for the customer. And just because you don't like something… how should I put it so you'll understand…. oh let's try this…. you not liking something is not a statistically significant sample of data!

On a serious note… offering your opinion on something, unsupported by any data except "I think", is probably a really poor way to start a conversation with anyone in the Analytics field.

If you express your opinion then present it in the from of a hypothesis that can be tested. Win-win.

So for example consider saying something like:

"I have viewed the site through Google Browser Size. The huge header on the website is causing the main content to be visible to only 40% of the website visitors. Based on this my hypothesis is that reducing the size of the header will reduce bounce rate and increase click-through rate to key pages/products."

See the difference?

It is ok that you started with a hunch. You went and got some kind of data. Finally you offer a hypothesis that I can test, and you were clever enough to point to two things of value to the business (both of which can be measured!).

Your HiPPO / Boss is much much more likely to listen to you and accept your wisdom.

In the rarest of rare cases if you must express your opinion, present your credentials. Something like:

"I would change the layout of the site and eliminate the images because I am Jakob Nielsen and I know what the heck I am talking about!"

See that would be acceptable. :)

Overall: if you can, try not to offer your opinions (at least not in the opening statement).

2. Always offer alternatives / Think things through.

One of the persistent flaws in Web Analysts (and Marketers as well I am afraid) is that far too often we take a siloed view of things. We only see our slice of data. We only see our little world. We only care about what bothers us / what makes us happy.

You should always take a much more expansive view of things and when you make recommendations think of the big picture, think things through.

Here is a good example.

I was astonished at how many Ninja's included this in their fixes: Remove Ads.

Now I love adblock as much as the next guy and wish advertising (especially Display) were more relevant.

But when you as an Analyst recommend removing ads because you find them annoying (and they can be super annoying) you are essentially recommending the removal of a revenue stream.

Ok so if I accept your recommendation of removing ads what do you recommend I do about the revenue stream?

The "remove ads" recommendations did not consider that implication of their recommendation.

Now I don't expect you to be an expert on the intricacies of the business you are analyzing when I give you an assignment to do "impromptu analysis". But I would have loved to know that you thought about the big picture, what you thought about the implications of your recommendations.

You could have said:

"I would remove the ads because they are super annoying. I would recommend replacing them with an investment in targeting email campaigns which I believe will more than make up for the missed revenue.

Or:

"I would remove the ads and instead add a prominent "If you love the content donate money" button on the top navigation. The money we lose in advertising we will more than make up in donations."

Or:

"I would remove the ads. While that will mean we lose revenue in the short term, my hypothesis is that customer satisfaction will improve by 18 points which will lead to increased Visitor Loyalty and is that not what ESPN really wants?"

Give me a clue that you have: 1. Thought through the implications of your recommendations. 2. Have some alternatives handy, no matter how pie in the sky.

Here is another recommendation that is more nuanced, and something I think we as Analysts rarely think through.

The recommendation was that Flickr should allow posting of anonymous comments because it will likely result in more comments being published on pictures which will potentially increase User Engagement.

A very nice suggestion.

But by now it has been well established that anonymous comments very quickly lead to unintended consequences. [New York Times article.] All kinds of people jump in and, quite literally, say all kinds of things.

I would have loved to hear what your suggestion was to deal with this absolutely sure to happen outcome from your recommendation.

Think things through. As an Analyst, as someone who thinks more broadly.

[Note: I am not saying comments are bad. I am not saying all anonymous comments are bad. I am not saying comments should be 100% moderated and neutered before being posted. There is a happy medium and there are many wonderful options to deal with this problem.]

3. Offer data, even when you don't have access to the site's data.

Alec shared a guidance with me after the contest was announced. He said, and I am paraphrasing, "award the scholarship to the person who says that they can't make any recommendations to fix the site they love because they don't have access to the data".

Really good point.

I had very much kept my question open ended because I really wanted to see if people got creative with how they arrived at the recommendations (beyond the "I think").

I am afraid no one provided data.

On the surface it is understandable. You are doing analysis, impromptu analysis, on a site that you don't own. Of course you don't have access to data to base your opinions on.

Unfortunately that is not quite true.

You ALWAYS have access to data. For ANY website.

If you want to understand the clickstream data for any website you could go to Compete (here's ESPN's data, or this blog's). If you want data for a international site use Google Trends for Websites (here's H M V's data, and here's data for people from Switzerland who read the French newspaper LeMonde).

Sure the data is not 100% accurate, but it is directionally accurate and it will take a few minutes on either Compete or Trends to dig a bit and find something interesting you could base your recommendations on. It should take you a few more minutes to compare data for one site to its direct competitor and identify something even more interesting.

If you want to understand the search engine ecosystem then use Insights for Search. Check out how much delightful data is available to you: Acne vs. Poison. [Look out, poison making a massive come back!!]

Spend time understanding the keyword market and consumer interest for the business you are analyzing. Find strengths and weaknesses. Find opportunities (by geographic region or in the cluster of top related searches or, my fav, fastest rising searches). There are so many sources, so many possibilities (many free!).

If you want to get demographic or psychographic segmentation data use the DoubleClick Ad Planner. In a few minutes you can understand the demographic make up of any site.

Male – female, age, education, household income, audience interest and more. In a few more minutes you can get down identifying the psychographic segments. Affluent 100k+? Brides-to-be? Gossip Gurus? Home Buyers? Moms? Technology Geeks? Who are we talking to? Who do we want to talk to?

And these are just the basics. Check out: The Definitive Guide To (8) Competitive Intelligence Data Sources.

You always have access to data. Regardless of if you own the site or not.

If you are put in a position where you have to offer impromptu analysis please use these (and other) data sources to add the kind of power to your recommendations that can only come from being backed up with data. Some data.

4. Always, always, always state what you think the Objectives are.

This is such a common mistake when we present our analysis. We make recommendations without saying what we are actually solving for.

Before you present your recommendations first tell me what you think the website's objectives are. What you think the purpose of the website is. What you think the site is solving for.

Often analysis is not valued very highly not because it is stinky, it is because the producer and the receiver disagree on what the objectives of the site are.

I might think the purpose is: Orders, Leads, Job Applications.

You might think the purpose is: Facebook followers, Brand Perception Lift, Product Reviews.

If you don't tell me what you assumed the objectives were you'll see very quickly why I might think you produced nothing of value.

So make it clear.

I might still think your analysis was poor (or awesome!), but at least I know what you were solving for.

I have context within which I can place your analysis.

You might think that it is obvious what the purpose of GoNomad or NBA.com or SFAF is. But I assure you that it is not obvious. So make it obvious, we'll both come to your analysis / recommendations from the same perspective.

In your daily jobs you should never present your analysis without having shared vision around the objectives. Otherwise the best result is no action will be taken on your recommendations. The worst result is… we'll I don't have to say it do I? :)

[Use this if it helps: Web Analytics Measurement Framework. Though for impromptu analysis you don't have to get that detailed. Just keep the framework at the back of your mind.]

5. Focus on the obvious, and the non-obvious.

Even if you spend only 30 mins on doing some analysis try to say something that I won't anticipate by spending 5 mins on the site's home page.

Surprise me [/ your boss / your audience / children / god].

Here is an example.

I can guess the Macro Conversion on site in two seconds. So tell me about the three Micro Conversions that are not obvious but of great value to the site.

Say you looked at Williams-Sonoma. Points for telling me about ecommerce. Bonus points for grasping and telling me how to improve qualified sign-ups for the Williams-Sonoma Catalog (which brings a lot more revenue in the long term than a quickie online order). Or how to improve number of brides creating Wedding Registries (huge money there). Or memberships to the Wine Club. Or Gift Cards (which are essentially customers making interest free loans to Williams-Sonoma!).

Surprise me.

Visit the website of the site's biggest competitor and tell me two things they do well that you think your site should.

Dig out industry standard scores for Customer Satisfaction & Task Completion Rates and use that to tell me areas of opportunities.

Give me three specific ideas for A/B or Multivariate tests and state your hypothesis for what will change.

Present your analysis / recommendations in a different format.

Shock me by including a framework you use for your recommendations (which one person did, it looked like a house! so amazing!).

Postulate a good enough reason to use Social Media (not just because everyone is doing it).

Tell me about how the inevitable demographic shifts in the US population will destroy the current business that this company has.

Surprise me.

If Scott or Brett or Dai or Trevor or someone else can spend a few minutes on the website and come to the exact same conclusions as you then it is unlikely that your analysis will be as impressive as you think it should be.

So… focus on the things that will be obvious to many and then include at least one non-obvious thing that almost no one will focus on because only you, the unique awesome genius person that you are, will see it.

Summary: Don't just offer opinions, think things through, offer data, clarify what you are solving for and finally do at least one thing that falls in the non-obvious category.

Amongst the submissions that was presented there were some common themes in the I was quite delighted by.

Here are a few of them, you should do these too when you do analysis…

1. "Why before the how"

Almost everyone focused on redesigning the home page, with one holy goal in mind: Make the value proposition of the company really clear really fast.

I love that!

One person framed it so well: "Address the why before the how."

Brilliantly put.

Use that mantra every day.

Some things were common in many submissions, and these I really really liked:

2. Obsess about SEO.

Some folks diligently focused on SEO, and I LOVE SEO!

From garbled urls to missing title tags to poorly linked internal pages to missing site maps. I am so happy people found these things (and EVERYONE of you can too with basic knowledge of SEO!).

It is "free" traffic, but more than that it is investing in the long term success. It is pretty attractive to jump to Paid Search recommendations or doing more Email Campaigns. You should do that, but if you come to me with that and not mention SEO you are going to break my heart.

[Even if you are an Analyst I expect you to have the knowledge described here: Official Google Search Engine Optimization (SEO) Starter Guide.]

3. Be different.

I covered this a bit in #5 above. But wanted to share more context with you.

In their analysis some people tried to be different. That is always a good thing.

Instead of sharing a site and three things one person shared three things they would change about the state of Texas!

Made me smile (and I sent him a free copy of Web Analytics 2.0 :)).

On a serious note… you know the obvious things people will say in these situations, and so do the HiPPO's (they have heard it all before). Try to be different (though not Texas different!).

4. Be sweet.

Without exception everyone was very sweet. Most people tried really hard to send me the best submission they could. I got special graphs, images, wonderfully formatted word documents… so much.

It was so nice. I feel profoundly grateful.

Life is short. Be sweet to those around you. They'll reflect it back. One person at a time we can make the world a better and less bitter place.

Closing Thoughts.

I recognize that you won't do all of the above for an "impromptu analysis", else there would be nothing impromptu about it.

I hope that you'll take the principles outlined in this blog post and make them a part of your DNA. When you are asked to do some quick analysis that you'll activate these principles, even without thinking about them too much.

When I have to analyze a site I quickly make a note of the two or three objectives of the site (and one of those falls in the non-obvious category). I log into Compete and Trends and get some data about clickstream. I see if there are clues in Insights for Search and Ad Planner about the site's business. Then I write down two of three things recommendations / fixes that I can back up with data, or in case of no data formulate and preset a couple hypotheses for testing.

It takes me between 30 mins to an hour. I won't change the website's trajectory in a massive way, but I'll definitely give them some concrete things that will have a short term noticeable positive impact.

And you can too!

Ok now it's your turn.

What is your approach when put on the spot and asked for some analysis of a site you don't own? What are one or two techniques that work for you? Thoughts on the above nine principles?

Please share your critique / approaches / feedback in comments below.

Thank you.

Analyze This: 5 Rules For Awesome Impromptu Web Analysis is a post from: Occam's Razor by Avinash Kaushik