If someone with no knowledge of carpentry were asked to build a shed, his or her lack of experience and skill would quickly become noticeable. Unfortunately, it’s not always quite as obvious when people work with data. It’s easy to draw incorrect conclusions from numbers, which sometimes can be more harmful than not having data at all.
Know your source.
Knowing the makeup of data (e.g., knowing what is included, what is not included, and how it was obtained) is essential when considering how to interpret information. A great example of this comes from a retail client that I worked with many years ago.
In an effort to gauge customer satisfaction at its stores, the client invited customers to take an online survey after each visit. The incentive to take the survey was a high discount coupon that could be used on their next visit.
The market research company that handled the reporting noted that more than half of the respondents used a high discount coupon on their subsequent visit. Without taking all of the factors into account, some within the client’s organization drew the conclusion that customers were too reliant on coupons – and that revenue was being lost unnecessarily.
However, a closer look at the source of the data led to a different conclusion.
The incentive offered for completing the survey helped to provide context for the coupon usage behavior. People who were more apt to take the survey because they were being offered a coupon were, in turn, more likely to use a coupon. As a whole, these customers were more price-sensitive and were prone to go elsewhere without a discount. It may sound obvious, but things like that often are overlooked, as initially happened in this case.
When the sales data for all customers, regardless of whether or not they completed the survey, was factored in, the results were much more accurate. The overall rate of high discount coupon usage was much smaller for the general population than it was for just the group of individuals who took the survey.
Thinking critically about the source of the data helped properly interpret the numbers. Before putting the survey data in context, the client questioned if its marketing approach was “giving away too much.” Taking the source of data into consideration and reviewing broader sales data helped remove those questions, thus keeping a well-established and profitable couponing program in place.
This is just one example of how important it is to judiciously think about the data you receive, view, and analyze. Consider the source of the data, how it was gathered, how complete it is, and how it is being presented. Most importantly, use your experience to determine if it makes logical sense. Critical thinking about data should lead to a deeper understanding of the information and how to apply it to your business.
Remember, data is there to help make decisions. In the end, your own experience and judgment will help you properly apply it.