Shift Your Web Analytics Focus to Include the “Why”

November 11, 2015 Kara Frazier

a man presenting at a conference with a microphone in hand

For a millenia, keepers of data have struggled to express the why behind the what, the story behind the numbers. The theme of the San Francisco chapter of the Analytics Labs was the why of web analytics. After all, understanding why your analytics data is looking like it does is just as important as the data itself.

Quantitative and Qualitative

How many times have you been in a meeting that unfolds in the following way?

Your team is called in to review the web analytics data in your monthly marketing meeting and the person in charge of analytics reports proudly declares: “345,123 visitors to our homepage this month.” Someone may make an effort to tie these metrics to your business targets, but no real depth of analysis is provided. The meeting moves on and and your team returns to work until next month, when you will offer up a similar report that, again, likely doesn’t generate any real action-items.

When your team only focuses on quantitative data and not the qualitative reasons behind the numbers, you are glossing over important signals that indicate both risks and opportunities in your business.

As the San Francisco Analytics Lab opened, Michele Kiss, Senior Partner at Analytics Demystified, started this discussion, describing the why as qualitative data and the what as quantitative data. Kiss explained that while it is important to see exactly what prospective and current customers are doing on your web properties, it is also crucial to know “what lies beneath the numbers.”

Shifting your web analytics focus to include the why does not mean that the qualitative data is more important than the quantitative, but by viewing your numbers more comprehensively, you can form a more complete picture of your audience.

It’s an idea Kiss believes in so strongly that she encourages analytics practitioners to become more involved with qualitative research, whether this means joining other teams in their research efforts or performing their own research within the analytics team.

Types of qualitative research include surveys, user profiles or personas, case studies, observations, focus groups, social listening, help centers, eye tracking, task completion (user testing), card sorting, user-initiated feedback, testimonials, etc.

Kiss suggested aiming to answer Avinash’s “Three Questions” with your research:

  1. What was the purpose of your visit?
  2. Did you accomplish what you came for?
  3. (If not) Why not?

Performing and analyzing this research may be a daunting task, but in the long run the results will save you a world of confusion about your current and potential customers and how to better reach and retain their business and loyalty.

The bottom line is – the why informs analytic improvements.

“The more data you can use to understand the bigger picture, and not be limited just by one tool, the better your analysis will be at uncovering the motives behind your user’s actions – and the better your marketing can be at addressing these motives and concerns.” – Michele Kiss

The “Why” Leads to Strategy

Dan Reno, expert developer and analytics professional, followed Kiss’ thoughts with a presentation detailing how to execute strategy-driven solutions for web analytics implementations, audits and adjustments.

Like Kiss, Reno argues for a greater shift in the vision of web analytics professionals. He quotes Seyi Fabode:

“The end of analysis will come about when we focus on ‘systems’ and combining constituent parts to truly understand/discover where problems lie and ultimately how to solve them.” – The End of Analysis?

However, Reno points out, the tendency to focus strictly on the numbers is so pervasive because most enterprise analytics teams spend so much of their time seeking to validate their data, leaving them with limited energies to put into understanding what that data actually means.

Reno argues that as well as developing more acute attention to detail, analysts also need to adopt a 10,000 foot view. As he addressed the audience, he emphasized:

“We need to stop burying our heads in minutia of never ending data that we now pay so much attention to. We need to assess whole systems to get business breakthroughs.”

Data collection is not the end-all for web analytics. The information collected opens the way to data-driven strategies that conform to overall business objectives, and ultimately, overall improvement.

Analytics in Operation

The final speaker at the San Francisco Labs was Patrick Hillery, a data quality and solutions expert who addressed the how—how to implement web analytics solutions that facilitate a broader business-impact perspective.

Hillery argues that digital analysts should strive to inform the organization of the more qualitative impact of web analytics numbers so decision makers can implement data-backed strategies. Analysts can work toward this goal through:

  1. Becoming a self-sufficient web analyst
  2. Setting up regular, automated reports
  3. Integrating API/ETL
  4. Creating custom dashboards to keep key business metrics visible

Staying on top of your web analytics processes and issues also helps to maintain functionality and to retain credibility within your organization while teaching others in the enterprise why customers are behaving the way they do.

Next up on the Analytics Labs Tour

The Analytics Labs are structured for discussion, bringing together the best minds in web analytics to address analytics issues and to discover possible solutions. The Labs tour is continuing this week in Boston, New York City, and Washington D.C.

For more information visit analyticslabs.org

 

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