The Challenge of Behavioral Metrics
When dealing with customer experience, there are many pieces of behavioral data that can be captured. Each one provides some value by helping us understand what the customer has done and how he or she interacted with your company.
But because such behavioral metrics are backward looking, it is often hard to tell what is really going on with the customer’s interaction. When looking at broad aggregated scores it is easy to assume there is a relationship between data points, but when properly analyzing the data and looking at the right segments, the relationship between those points may not consistently exist or even exist at all. Making assumptions without the analysis to prove or disprove them can get you into trouble by making wrong or unwarranted decisions that affect your company’s bottom line.
Bounce rate is a good example of a useful, but generally misapplied, behavioral metric.
It calculates the percentage of visitors who view one page and then “bounce” off that page to a different site instead of viewing additional pages on the original site. Exit rate, sometimes called page exit ratio, is a similar metric that measures the percentage of visitors who leave a site from individual pages. (By definition, all visitors eventually exit, but not all visitors bounce.)
Bounce rate is calculated by dividing the total number of visits by the total number of visitors who view one page. If 1,000 people visit and 200 leave after viewing one page, the bounce rate is 20%.
Exit rate is calculated by dividing the total number of visitors to a page by the total number of visitors who exit the site after viewing that page. If 1,000 people view a page and 400 leave after viewing that page, the exit rate for that specific page is 40%.
The traditional use of these two metrics assumes the lower the bounce rate and exit rate, the better. Why? The basic assumption is that unsatisfied visitors bounce or exit.
That assumption is not always correct because it does not take into account the impact of visitor intent, especially in a multichannel world. Many companies assume that the pages on their sites with the highest exit rates need to be “fixed.” But if a customer visits a product page, finds the information he or she wants (say, a product price), and exits to drive to the store and purchase the item in person, should the page be “fixed”? Was the customer dissatisfied by his or her visit to the website? Hardly—the customer found exactly what he or she was looking for. He or she left after viewing one page because he or she was satisfied.
In many cases, the page with the highest exit rate is the page that best meets the visitor’s needs and expectations. To truly understand behavioral metrics such as bounce rate or exit rate, other factors, such as initial intent and subsequent actions, must be taken into account. Otherwise, CEOs and managers may fix what is not broken and ignore what may be, in fact, a real problem and a real opportunity for improvement.
The key to understanding your customers is to comprehensively measure and operate within what ForeSee calls the Measurement Ecosystem, a set of data and analysis that allows you to predict the future. The Ecosystem is based on using different types of metrics, methodologies, and tools to make the whole significantly stronger and more robust than its individual parts.