The Qualitative and The Quantitative Faces of Customer Behavior Analysis

In my previous entry, I talked about the three phases of Tealeaf’s online customer experience optimization methodology: Visibility, Insight and Answers.  Visibility is how you become aware of customer experience issues; Insight is where you understand the root cause and business impact of each issue; and Answers is about taking action on your insights. The cornerstone of the critical Insight phase is what we call customer behavior analysis. I think it’s important for me to explain in more detail what customer behavior analysis is.

Regardless of how you become aware of potential customer experience issues, customer behavior analysis is how you answer the why questions about the web site: Why do more customers abandon the credit card application on the second step rather than the first step? Why are customers searching for products multiple times and still not adding items to the shopping cart?

To provide you with the most insight, customer behavior analysis must have two pillars: the qualitative and the quantitative. Because without qualitative data, the numbers from quantitative data are just numbers. How often does an anomaly in the numbers get written off as a data problem because it can’t be validated in the real world? And at the same time, qualitative data that can’t be quantified can cause you to focus on the wrong things. You might ignore a huge problem that’s not very visible and invest in site changes that really don’t translate into higher sales. It’s the connection between the qualitative and the quantitative aspects of customer behavior analysis that makes customer experience management both data-driven and actionable.

Customer behavior analysis is usually an iterative process, where both of its pillars work together in concert. Let me illustrate this with a real-world example. One of our customers, a leading communications company, noticed a rise in the number of customers going through the subscriber process multiple times before abandoning. They needed to know why, and traditional segmentations weren’t giving them the answer.

• The first step in their customer behavior analysis was to identify which customers abandoned the process. This information might come from a web analytics tool or from searching in a customer experience management tool like Tealeaf.
• Next, they looked for visual evidence  that something was not working as expected. With Tealeaf’s visual replay capabilities, they simply watched some of the sessions of abandoning subscribers. Before long, the company discovered an endless loop in the “new subscriber” process. Once new subscribers enter their payment information, the last step before order completion, they’re supposed to enter a date for equipment delivery. However, when new subscribers neglected to schedule a date, they ended up in an endless loop.
• Finally, they quantified the problem by looking for other sessions where the same thing went wrong. If you have found the right problem, the number of users affected will reflect the number of users who didn’t complete the process successfully. This analysis also gives you the information you need to determine the problem’s actual business impact. In this case, the company found that the endless loop was putting several potential new customers per day into a ‘no man’s land,’ representing more than $220K in lost revenue per year.

From this example, you can see that customer behavior analysis is a systematic way of uncovering and understanding customer experience issues from both a qualitative and quantitative perspective. While you can conduct customer behavior analysis without a customer experience management tool like Tealeaf, our customers have found that they save tremendous amounts of time and effort by not having to reproduce issues manually or search log files to determine who has been affected by an issue. And by having quantitative business impact at their fingertips, they find it much easier to set their priorities around making the biggest improvements to customer experience.

-- John Dawes, Vice President, Product Management

Comments

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From this example, you can see that customer behavior analysis is a systematic way of uncovering and understanding customer experience issues from both a qualitative and quantitative perspective.