Conceptually, the Core Ratio is similar to the well-known LTV:CAC ratio, which measures the return (LTV) on investment (CAC). We moved away from LTV:CAC ratios because they are based on guesswork to estimate customer lifetimes and because they involve complicated math with differing opinions on formulas, definitions and time frames. No matter how you calculate your LTV:CAC ratio, someone will point out an aspect of the data the LTV:CAC ratio fails to capture and they’ll be right.
The LTV:CAC ratio is expressed as a number, which is an average over time and over many cohorts. Averages hide information because they obscure trends over time and hide details by aggregating different customer groups.
Averages are easier to grasp and remember, but end up being a cognitive shortcut that is more costly than most people realize.
One aspect of data that averages can hide is extremes: A statistician drowns in a river that is, on average, three feet deep. In this example the relevant metric for the statistician is not the average depth but the maximum depth. This is why we look at distributions instead of averages.
Averages also hide trends. For example, a LTV:CAC ratio of 3 that has been steadily improving over the last 2 years is better than the same ratio of 3 that has been steadily declining. Reducing LTV:CAC down to a single number erases this critical information.
Lastly, averages hide the fact that not all improvements over time are created equal. A LTV:CAC ratio of 3 that has been steadily improving because the sales and marketing team has figured out how to scalably grow the business is better than the same ratio of 3 that is improving because a small group of loyal customers have started spending more. This is why we look at all metrics, including the Core Ratio, over time and on a cohort-by-cohort basis.
Written by: Thomas Gieselmann and Jonas Nelle