eCommerce Analytics

Many companies use surveys to get a handle on how customers perceive them and to find areas in which they can improve. Oftentimes, though, these surveys produce a lot of data but not a lot of insight. After all, if you ask your customers to rate you in 30 different product or service areas, how do you know which are critical and which are just nice-to-haves?

The key to analyzing survey data to find which areas are important is to be sure the survey has at least one all-encompassing “outcome” question that identifies whether you are successful in meeting the customer’s needs. This is usually something like “How would you rate our product/service overall?” or “Would you recommend us to a friend?”. We then use the responses to the other questions to find which individual product or service areas most directly affect the outcome.

This is most easily done using common data mining techniques. Using logistical regression or regression trees, it becomes easy to find the two or three individual areas that drive the overall customer perception of the company. For example, we might find that of the 100 different attributes available, a restaurant’s overall rating is driven primarily by speed of service, staff friendliness, and location.

Armed with information about the few attributes of your business which define your customers perception, you can better focus your resources to drastically improve the customer experience. Otherwise, it’s too easy to find yourself sweating over the unimportant details.