How to Integrate Customer Survey Data Into Your Analytics

Written by Guest Blogger on September 9, 2013

Posted in Analytics, Lead Generation

Most companies do two separate things: a) survey their customers and b) measure how customers are using their products/services. Survey data tells you the how and why, while activity data is more focused on the who, what and when. This “what do they say” and “what do they do” dichotomy are two very different, very important approaches to arrive at insights about customers.

Most companies analyze this data completely independently. Survey data gets collected every few months, the the results are analyzed, conclusions are drawn, and then it goes on the shelf. Customer activity data lives in a specialized tool that a few product managers and marketers pore over.

Instead of thinking about the two datasets independently, companies should be integrating them together into a unified view of the customer. Any analysis of customer satisfaction should incorporate both customer survey and usage data. If this data is never integrated, valuable insights are never surfaced.

If companies took data from product instrumentation tools (like Totango or Mixpanel) and integrated it with customer survey data collected from tools (like Formstack!), they could easily operationalize some pretty interesting insights. Here are three questions companies would be able to answer and act on by integrating survey and analytics data:

1.What product usage patterns can predict low net promoter scores?
Net Promoter Score is a commonly asked question on customer surveys. You’ve probably asked, or responded, to it before: “How likely are you to recommend our company/product/service to your friends and colleagues?” What if, instead of asking that question quarter after quarter and charting the results over time, you actually tied it back to product usage data?

It’s possible that customers with low NPSs purchase certain stock keeping units or interact with your product in particular ways. Figure out what SKUs or product features are correlated with low NPSs and you’ll know what to improve. And while you’re at it, design marketing campaigns to the low-NPS customers based on their custom order history!

2. Are customers loyal to a company because there isn’t any alternative?
What if you have a low Net Promoter Score but you also have a high repeat purchase rate? Customers are clearly coming back and purchasing over and over again, so why worry about the low NPS?

If your NPS is low and your repeat purchase rate is still doing fine, do a gut check. In today’s disruptive marketplace, unhappy customers who keep coming back are probably doing it because they don’t have any other good options. They’ll jump ship as soon as something better comes along. If you can see that writing on the wall ahead of time, you’ll have the opportunity to improve your customer experience before your customers walk out the door.

3. What products are most likely to create customer evangelists?
Customer evangelists are incredibly valuable for building your brand and acquiring new customers. What if you could have more of them? If you integrate your survey and activity data, try uncovering what SKUs or product features are correlated to high NPSs. Then funnel more customers to those high-satisfaction items with email campaigns or website features.

Additionally, try to make a good first impression! Examine the very first interaction that a customer has with your brand and whether it resulted in a high or low NPS. Make sure your customer acquisition process is streamlined to push users towards high-NPS activities.

Once a company starts to uncover these insights, it becomes easy to see plenty more. For example, how do location, time of year, or even day of the week have an affect on what people say vs what people do? How many people say they will become repeat purchasers, compared to the number of people that actually are repeat purchasers, when a company changes the price of a product?

Have you read survey results in the past and thought you’d like to compare it to actual customer behaviour? How do you think your company would benefit from this?

logo-blogpostAbout the Author
Sarah Lang (@_sarahlang) runs inbound marketing at RJMetrics. RJMetrics provides business intelligence for online ecommerce and SaaS businesses. If you’re interested in this type of analysis for your data, please feel free to get in touch.