Calculating Cohort Lifetime Value With Excel And R

Eleni Markou shows how to calculate the lifetime value of a group of customers using two techniques:

A lot of ink has been spilled in developing various descriptions of the LTV, the majority of which ends up with mathematical formulas that are based on margin (m), retention rate (r) and discount rate (d) like the following (here):

However, this model appears to be not that realistic as it is based on a few quite restrictive assumptions:

  • Retention is assumed to be constant during the lifetime of a customer, i.e. the probability r of remaining retained remains the same across all months.
  • An infinite time horizon is assumed when calculating the present value of future cash flows.
  • The unit economics are supposed to be constant throughout lifetime which leads to a constant contribution margin.

Yet when dealing with an actual company, it easily becomes evident that none of the aforementioned conditions actually hold. Especially in early-stage businesses the size of the time periods across which you would like to calculate the LTV is month – or week – sized while at the same time the retention rate across them can vary significantly as the company’s products evolve quickly.

There’s a lot packed into that article, so give it a read.

Related Posts

MAPE and Its Flaws

Jan Fischer takes us through Mean Absolute Percentage Error as a measure of forecast quality: Particular small actual values bias the MAPE.If any true values are very close to zero, the corresponding absolute percentage errors will be extremely high and therefore bias the informativity of the MAPE (Hyndman & Koehler 2006). The following graph clarifies this […]

Read More

From Excel to R: Three Examples

Abdul Majed Raja has a few examples of things which are easy to do in Excel and how you can do them in R: Create a difference variable between the current value and the next valueThis is also known as lead and lag – especially in a time series dataset this varaible becomes very important in feature engineering. In […]

Read More


August 2018
« Jul Sep »