Measuring loyalty performance
Success in loyalty is about driving incremental improvements in customer spend, visits, and engagement. You have heard me talk a lot about driving customer lifetime value, but LTV can be difficult to measure. What’s more, customer lifetime value is a lagging indicator of the impact of your program and, for most moderately sized restaurants without tons of loyalty data, has “statistical relevance so wide you could drive a truck through the error bars,” as our Chief Product Officer, Aaron Newton joked when we were discussing the topic.
The good news is that there is another metric that’s much easier to measure: Average Revenue per Customer. I love this metric because it’s so easy to calculate and it shows the impact of marketing activities almost immediately.
The formula is simple:
Average Revenue per Customer = Total Revenue / # of Customers
In other words, how much revenue, on average, does a single customer generate during a specific period of time. I’d suggest identifying standard periods of measurement, for example, 7 days, 14 days, 30 days, 60 days, 180 days, and all time. The actual periods don’t matter. The consistency does.
This approach has a few benefits:
- By defining static periods, you can easily see improvements over time and plot those on a graph to see how you are trending.
- This metric can be calculated almost immediately after launching a new loyalty program and shows incremental improvements (or declines) right away.
- You can see how marketing activities drive spending and when you find initiatives that work, you can automate them to maintain that high performance.
It’s important to note that, when calculating this metric, you must exclude those customers who were acquired during the time period being measured.