3 Challenges with Tracking Metrics and How to Get it Right
Metrics are everywhere. There are plenty of articles on how to define metrics, track them, align the team, etc. Each business has their own set of key metrics they use to track their business and show trends over time. With metrics everywhere, people are always asking for benchmarks. They want to know how they compare to average, good, or great. One challenge with metrics is to make the right comparisons to drive to meaningful action. Three things to be careful around comparing metrics are business model, averages, and leading vs. lagging metrics.
Comparing key metrics across business models can be dangerous. Certain industries are fundamentally different in terms of revenue growth, cost to acquire customers, margins, etc. For example, comparing a data company’s higher margins to a grocery chain’s lower margins is not very helpful. Even within an industry, a freemium business model driven by viral/word-of-mouth growth can have very different metrics than a model focused on large enterprise sales with a direct salesforce. Furthermore, comparing startups at different stages (i.e. $500,000 ARR vs. $5,000,000 ARR) can also distort metrics comparisons.
It is easy to ask the question - how does my [insert metric] compare to what is average/normal in the industry. First, even within a company, this can be very dangerous. Let’s say you have small, medium, and enterprise customers that each have very different annual contract values, churn, and cost to acquire. Lumping all those metrics into one company average can be very misleading. It would not tell you how you are doing in each segment, and if the segment/overall strategy needs to shift. Second, you have the simple statistics problem of distributions. Two companies could have the same average, but one could have a wildly skewed distribution while the other could cluster all data points around the average. Just looking at the average could hide this fact and the resulting implications to action.
Some metrics foretell the future, and others are a result of past actions. For example, churn is expressed after the fact, while leading indicators such as customer product usage and update meeting attendance could actually predict if a customer is more likely to churn. The same goes for sales being the lagging indicator, but metrics such as number of sales meetings and number of demos can likely predict future sales. As you are looking at optimizing certain metrics, make sure you know how they relate to each other and look for ways to improve leading indicators to drive the right actions to improve the ultimate metric.
While metrics are extremely important, be very careful of blindly comparing metrics to benchmarks without stepping back and making sure the comparisons are “apples-to-apples.”