Guidance for e-commerce, marketing and SaaS web analytics/data managers, analysts, strategists and specialists.

  • Analytics tools only capture data; it’s your job to figure out what the data say that’s of value to your company.

  • Real use of analytics data requires a deep quantitative analysis and qualitative validation of the relationships between data sets.

  • It’s helpful if your analyst understands the commonalities of data inconsistencies across your industry and/or sub area of your industry – as ours do.

Normally we’re left unsatisfied by definitions found on Wikipedia or similar online sources, but for “web analytics,” Wikipedia actually has it right:

 

The measurement, collection, analysis and reporting of web data for the purposes of understanding and optimizing web usage.

 

This is an extremely accurate definition if you are looking for an answer that defines web analytics from a tactical perspective. It’s as vanilla as can be, but it is spot on.

 

But when people search with a term like “web analytics,” what exactly is driving them? What is the search intent associated with the information they are looking for?

 

Web analytics is so foundational to every campaign, every platform, basically everything we do in conversion optimization. Without it, we are flying completely blind and without a purpose. It is imperative to understand its purpose and usage, and how to exploit its capabilities to the fullest.


 

Your Reticent Life Partner: Web Analytics

If you are in charge of developing and/or interpreting analytics for your e-commerce store or SaaS program, or trying to develop custom reports of user data from your travel website, then you know one of the main issues is that no one has a clue how you do what you do. They think it’s something the computer does for you, and they just want more.

 

We continually talk to very technical folks with titles like data scientist, web analytics manager or digital marketing analyst in e-commerce, SaaS solutions and the travel industry. A common theme throughout each conversation is that they can’t squeeze any more blood from the turnip. At some point, all of the data eventually look the same and it’s tough to see where some of their revenue leaks may be.

 

On the other hand, sometimes there’s a great benefit in having another set of eyes look at something. Even better if they’re the eyes of someone who happens to know what the commonalities of data inconsistencies are across a specific industry and/or sub area of an industry.

 

Unlock the Answers in Your Analytics Data

All your analytics tool does is capture data. This is true for all types of web analytics platforms. Google Analytics, Omniture, Adobe Analytics, Web Analytics from IBM (formerly CoreMetrics) are very common in everyday conversations with technical web marketers.

 

The real job of a technical marketer using web analytics is to wrangle the data into shape. It’s taking what is default, and applying a very deep quantitative and qualitative analysis to it. Choking the data with your bare hands. Stepping on it and squeezing it for every last drop of information. Torturing the data to the point where it gives up all of the personas for a given site and the customer journeys that show all the problems where you are losing money.

 

This can be done without breaking any laws or international treaties. A deeper look at the sophisticated relationships between data sets allows us to make valid inferences about facts behind those numbers, which relate to user experience on your site.

 

Case Study: A Common SaaS Business Problem

For the most part, E-commerce checkouts are starting to stabilize in terms of how many steps there are and/or the information they require. There is still variation, but some of the retail giants out there are effectively training users as to what a common checkout should look like.

 

However, if you are not selling a tangible product online that’s shipped to the buyer, your checkout process is not always as linear. This scenario, which we see quite often, calls for a bit of an out-of-the-box approach when dealing with the web analytics. (Keep in mind that this is a high-level analysis that doesn’t require combining data sets from various systems, which need validation and integrity checks.)

 

When we can identify the SaaS conversion point, let’s say a sign-up for a subscription, we can do an inversion analysis, which requires starting at the very end and working our way back up the funnel to show failures or dropoffs where they occur. In this specific scenario, our hypothesis is that a poorly designed registration area is the problem. We can quantitatively identify users getting confused by guest checkout vs. new account, because of friction in the design, lack of clarity in the form fields or what’s being asked of the user. This analysis gives us specific variants of the accomplished goal (a sign-up), which might otherwise provide a false positive.

 

The same feature can be used in a cohort analysis of items within a product type that share commonalities in terms of sizes, colors, etc. We may want to identify the percentage of people who try to add to a shopping cart but are unsuccessful if they forget to select the variables aforementioned. What we want to identify here is the percentage of users who abandon the cart process entirely because they didn’t see the error message reminding them to select these product features. A bonus here is that, by looking at average order value over a certain time period, we can arrive relatively quickly at an estimated revenue loss based on the number of people encountering that specific problem.

 

Anytime we can tie revenue to a specific user issue encountered in the middle of the conversion funnel, the Gods of ROI float down from the clouds and enjoy rubbing elbows at happy hour because they didn’t have to spend the day devising a test plan to prove what needed to be tested.

Common Misconceptions About Web Analytics

By far, the biggest myth in analytics is that all you need is the Google Analytics dashboard numbers and that is enough to make all your marketing decisions. There are other misconceptions, as well. Here are a few:

 

Myth: The default dashboard in Google Analytics shows averages and that’s a good place to start.

Fact: Averages lie all the time. Instead of looking at the default numbers, make sure there is a question you are trying to answer, then always compare numbers from a minimum of two segments.

 

Myth: Free analytics software just isn’t enough for our HUGE organization.

Fact: The data are secondary to the insights they yield. Tools don’t automatically create lifts in conversions or revenue. The insights pulled from the data and what you do with them at that point is what really matters.

 

Myth: Information from different web analytics programs should all match.

Fact: In reality, no cumulative data points ever match. This is because of the variations in how the information is gathered. Google Analytics uses client-side code to gather information. Other tools may use server-side information. There may be be delays in the data being reported. Other issues involve actual tracking methods, JavaScript, cookies, etc.

 

Myth: Bounce rates should be the first agenda item in every meeting.

Fact: While this may be true in some scenarios, the motivation behind it is generally incorrect. Since Google Analytics defaults to a bounce rate for the entire site, people tend to look at that immediately. The reality is, bounce rates should be looked at on an individual-page basis, typically at the end of a conversion funnel.

 

Many customers high in the funnel come and go as they research and compare products, services, features, prices, color availability, and countless other factors. For some pages, you should expect a relatively high bounce rate. But along with bounce rate, always look at numbers for new and returning users, session duration, and page views.

 

When Foundational Web Analytics Isn’t Enough, What’s Next?

If you are responsible for conversion optimization for an e-commerce site or a SaaS program, analytics are the foundation to your ability to succeed. Conversion optimization often requires you to combine multiple data points to arrive at an understanding of what the numbers really show. In addition to quantitative analysis, you need to make sure you conduct qualitative (observational) analyses to pair the quantifiable “what” of user experience with an actionable “why.”

 

How deep you or your analyst goes into your web analytics data will depend on your need and on your or your analyst’s understanding of statistical analysis and the analytics tool. Unfortunately, while the former is much more important, the latter will have a far greater impact on the results you get.

 

What we’ve said here about web analytics only reaches the tipping point.

 

Some other areas of interest to you might be:

  1. Conversion Optimization: Tactics for conversion optimization must reach down to a funnel level or to individual pages to adequately focus on fulfilling your site users’ needs.
  2. Landing Page Optimization: The need for landing page optimization never ends as you seek to understand changing customer personas and how to inform, assure and guide site users toward conversions.
  3. A/B Testing: Proper testing can yield a lot of actionable information about your site’s users, but A/B testing is far more complex than the simple either-or proposition its name implies.

Interested in working with us?

Jeremy Smith


Conversion Expert

Jeremy writes about conversion optimization, web psychology, and what makes users click in the digital world. He is also a Google Certified trainer and avid online marketer.

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