A vanity metric is a unit of measurement that looks positive but fails to provide the viewer with any information that can be used to drive future actions. Vanity metrics can be contrasted with actionable metrics, which allow the viewer to identify specific actions that can be repeated and have already demonstrated an ability to affect a desired outcome.
Because vanity metrics are often used to show growth, they typically quantify data that will naturally increase over time. Examples of popular vanity metrics include:
- Total customers -- without supporting metrics to show how much each customer spends, this metric is simply a vanity metric.
- Downloads – without supporting metrics that show how many subscribers are actively using the app, this metric should be considered a vanity metric.
- Twitter followers – without supporting metrics that show the number of times an account’s followers commented upon, retweeted or liked individual posts, this metric qualifies as a vanity metric.
Commercial off-the-shelf (COTS) analytics programs often feature vanity metrics in their executive dashboards, simply because developers tend to select metrics that are easy to acquire and visualize with charts and graphs. Such dashboards are popular because of the convenience they offer, but the data can be misleading when the numbers lack context. Take, for example, a bar graph on an executive dashboard that shows an impressive 100% increase in sales year over year. That same bar graph may not look quite so impressive if the viewer should learn later on that there were only 5 sales the first year and 10 sales the second year.
In a business-to-business (B2B) setting, any metric that doesn’t directly relate to customer acquisition, customer retention or revenue may legitimately be referred to as a vanity metric. The challenge of working with vanity metrics is figuring out how to use the data from a vanity metric as a starting point for future examination. Strategies for turning vanity metrics into actionable metrics include the use of A/B tests and per-customer metrics such as customer average order value (AOV) or time on page.
Analyst Jim Nail discusses vanity metrics and the danger of confusing data points with data metrics.