Analytics and UX: Understanding the Story Behind the Data


When is the last time you told a lie? How often do you say one thing, but do another? Do you think your search history matches who you are and how you behave in real life?

If you’re feeling a little flushed right now, don’t worry — you aren’t alone. In reality, most of us behave very differently online than we do in the “real world,” even if we won’t admit it.

Just ask Seth Stephens-Davidowitz, a former Google data scientist who wrote the book, “Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are.” To write it, he spent years analyzing millions of anonymous Google searches to discover how wide the disconnect between our offline and online behavior really is.

Though the book is packed with plenty of groundbreaking insights about racism, gender bias and the power of online anonymity, one line in the book perfectly sums up his findings:

“Don’t trust what people tell you; trust what they do.”

So what does this all have to do with digital marketing?

It highlights how data can tell us much more about who our customers truly are than any interview or focus group can — but only if we know how to look for the story beyond the numbers.

“Data can tell us much more about who our customers truly are than any interview or focus group can — but only if we know how to look for the story beyond the numbers.”

Most marketers recognize the value in data, but few know how to analyze and leverage it in meaningful or actionable ways. In this post, we’ll explore how thinking bigger about analytics is the key to long-term marketing success.

How and why marketers get data wrong

Let’s face it — most marketers are not data experts. Though experienced marketers know how to spot trends, identify opportunities and gauge campaign success using data, they often don’t know how to get to the root of what users really want or need.

Consequently, many marketers focus on just a few key metrics that might tell you a lot about what users are doing, but very little about why they’re doing it. Even worse, some marketers use these findings to make sweeping decisions about their digital marketing tactics.

This is why marketing data analytics should always be approached with purpose, curiosity and patience. Doing so helps marketers see data more holistically — and empowers them to make more informed decisions about their digital efforts overall.

Let’s look at how this works in practice…

Imagine you recently launched a paid search campaign designed to generate brand awareness for your home security business. As part of this campaign, you decide to run a paid Google ad that points to a blog post with an infographic titled “5 Reasons You’re Vulnerable to Home Invasion.”

You select a target keyword that’s relevant to your product range — in this case, let’s say that keyword is “home security system” — and point traffic to your infographic. You hope this will prompt people to continue clicking through to other pages on your site — and in the long run, request a quote for a home security system installation.

One month after the campaign’s launch, you log into Google Analytics to see how your ad is performing. Unfortunately, you see this:

intro conversion rate

Yikes. Clearly, something about your campaign isn’t working, and it looks like you’re wasting ad spend without much quality traffic in return. You have two options here:

Option one certainly sounds like the smarter business decision, but option two is actually the better choice for long-term marketing success. To find out why, let’s look at how this could be approached with purpose, curiosity and patience


By treating a high bounce rate as the first clue in a fact-finding mission, you have a built-in purpose: To discover why the content isn’t meeting the point-in-time needs of your audience. This purpose is the foundation from which you can determine which direction you’ll pursue next.


From here, you’ll need to get curious — not just about what’s not working, but why it’s not working. You decide to start with a few hypotheses:

Hypo 1

Hypothesis #1: There’s something wrong with the ad itself.

Hypo 2

Hypothesis #2: There’s something wrong with the ad targeting.

Hypo 3

Hypothesis #3: There’s something wrong with the content.

By narrowing down your fact-finding mission to a few hypotheses, you have a clearer idea of what data to look at next. Based on your hypotheses, you decide to include a few other metrics:


With just a few other metrics to consider, you start to get a clearer picture of what’s going wrong with your ad campaign. You decide to apply these metrics to your three hypotheses:

Hypo 1

Hypothesis #1: There’s something wrong with the ad itself.

Based on the number of click-thrus on the ad itself, you know the language on the ad isn’t the issue, since users find it interesting or relevant enough to click on it.

Hypo 2

Hypothesis #2: There’s something wrong with the ad targeting.

Once users click on the ad and see the content, they don’t immediately leave the page. Instead, they appear to linger on the page before leaving your site. You also notice that the bounce rate for this piece of content is much lower when it’s not attached to this particular campaign.

Hypo 3

Hypothesis #3: There’s something wrong with the content.

You seem to be getting lots of impressions, so there are certainly enough people interested in the topic. You presume the issue likely occurs somewhere between clicking the ad and reading the content.

Based on your initial findings, you have a hunch the issue is misalignment between users’ search intent (i.e. the problem they’re trying to solve or the question they want to answer) and what the content actually delivers.


To find out if your hunch is correct, your next step is testing — a crucial step in data analysis that requires plenty of patience and planning. Based on your hypothesis, your strategy might look like this:

Test #1: Change the ad copy to better match what’s covered in the content itself, and compare ad performance to the previous ad.


Based on new data, you might find that while bounce rate decreased, the click-through-rate plummeted. You try a new approach…

Test #2: You keep the new ad copy, but change the target keyword to something focused on top-of-funnel needs. In this case, you change it to “home burglary statistics in my state” and relaunch.


Voila! After comparing your previous data with new ad data, you see that bounce rate significantly decreased while click-through-rate increased. Your new ad campaign is finally working as intended.

But here’s the best part… based on these findings, you know more about what your audience is looking for, when they’re looking for it, and how they prefer to consume it online. Over time, applying this same process to other campaigns will paint an even clearer picture of who your audience is and how you can best serve them.

Reframing the data conversation

The goal with any content marketing tactic is to give your prospective customers what they want, when they want it, and in a format they’ll love. Only then can you successfully turn your target audience into your owned audience and start turning traffic into revenue.

By approaching data analysis with purpose, curiosity and patience, you can better understand how your marketing tactics influence the user experience — on your website, your social media channels, and even Google.

So, if data really does have so much potential, why aren’t more marketers leveraging it in this way?

The missing link is almost always a digital marketing strategy. Unlike digital marketing tactics, a digital marketing strategy focuses much more on planning and prioritization than it does on execution.

A robust and well-documented digital strategy is important because it:

  • Aligns CRO experts, SEO strategists and other digital marketing specialists
  • Provides a comprehensive look at how marketing tactics will influence success
  • Illustrates how different tactics work together to attract, convert and retain customers
  • Creates a framework for data analysis, testing and continuous improvement

Yet for many marketing teams, tactics are executed without any documented strategy to guide them. Why? Because plenty of marketers still believe doing is inherently more valuable than planning.

To that, we say:

“Execution without strategy is expensive, strategy without execution is demoralizing.”

Do you need a digital marketing strategy? If you’re creating content, running ads, posting to social media or trying to rank on Google, the answer to that question is always “yes.” Without one, you simply can’t begin uncovering the story behind your web analytics.

Content Marketing Strategy Template

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The post Analytics and UX: Understanding the Story Behind the Data appeared first on Vertical Measures, An Investis Digital Company.

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