# Calculating Cohort Lifetime Value With Excel And R

2018-08-07

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.

## Dealing With Multicollinearity With R

2018-08-21

Chaitanya Sagar explains the concept of multicollinearity in linear regressions and how we can mitigate this issue in R: Perfect multicollinearity occurs when one independent variable is an exact linear combination of other variables. For example, you already have X and Y as independent variables and you add another variable, Z = a*X + b*Y, […]

## Including R Visuals In Power BI Dashboards

2018-08-21

Parker Stevens shows how to include R visuals in a Power BI dashboard: Let’s finish up this post with a quick example of how to code the elusive line chart with two y-axes. This always seems to be asked in the forums and it’s pretty easy to implement. Follow the same steps as shown above […]

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August 2018
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