People are full of surprises. Here’s how to find the truth.

Paul Tiedt | Feb 1, 2018 Paul Tiedt 02/01/18

It's easy to make assumptions about why people do the things they do. Behavior tells us a lot, but the only way to fully understand what makes people tick is to ask the right questions. Combine behavioral data with survey data, and you'll have a clear picture of what motivates your customers. Behavioral data might show you who walked out the door after months of regular visits. Survey data tells you why.

Fresh is best

Timely consumer surveys provide information about the attitudes and intentions behind actions. The data is best when you get it during or immediately after a visit, while the customer's experience is still fresh. And yes, there's an app for that.

SurveyMini is a location-based consumer research app that uses precise mapping technology to trigger visit-detected surveys to users' smartphones. By combining behavioral data with near real-time feedback, brands are able to see how they compare to specific competitors on things like competitive visit share, purchase conversion rates, and trip motivation.

Two ways of looking at CX data

Let's look at an example where three retail customers exhibit three different patterns of visits to a favored Orange retailer and an alternative Blue retailer. Pictured below are the 12 visits to brand Orange and brand Blue over time. On average, these three customers visit Orange retailer 8 times before switching to Blue. This behavior data alone is powerful. But it's only half the story.



This is where survey data comes into play. It helps put context around the behavioral data. Look again at the pattern of customer behavior, as it appears below. Visits to brand Orange circled in red indicate a customer-reported negative experience. Visits to brand Blue circled in green indicate visits when customers reported their reason for visit. Now we start to see the pattern. We know that customers don't defect after 8 visits in general; they defect after 3 consecutive negative experiences with a brand.


Understanding unexpected outcomes

Customer feedback can also help us understand the exceptions to our predictive algorithms. In this example, Customer 3 didn't leave because of a poor experience with brand Orange, but instead reported visiting brand Blue because friends recommended he try it.


As you can see, behavioral data alone lets you predict customer churn or loyalty by looking at visit patterns, but survey data can tell you why churn is more likely with some customers than others. And that’s a key factor in devising your customer retention strategy.

For more best practices, check out the Ultimate Guide to Omnichannel Experiences.

Paul Tiedt

VP, Client Insights
Customer Experience Update