Customer lifetime value (CLV) is the cumulative revenue a customer generates over their lifetime. Calculating CLV is important for evaluating user acquisition channels, growth strategy, and marketing budget. There are several methods for calculating CLV using cohort data and projecting future revenue based on historical revenue curves. The timeframe and monetization model affect which method is most accurate. Marketing budgets can be based on CLV to ensure user acquisition costs are recouped over time through customer spending.

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The Importance of Customer Lifetime
Value (CLV) by fmr WeChat PM

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7. What is LTV?
LTV is the cumulative revenue that a customer
generates during their lifetime

8. Why calculate LTV?
● To evaluate User Acquisition Channels
■ TV vs ad-networks vs channels
■ ROI of UA channels
● Growth Strategy
■ MAU targets
■ Organic vs paid mix
■ AppStore ranking
● Estimate marketing budget, CPI bid rates

9. Why LTV?
Old school approach: Driving installs at lower CPI is a flawed strategy, esp.
Incent channels
Spend by Ad-network Installs by Ad-network

10. Cohort data inputs for LTV :

11. Methods of calculating LTV
Method Details Pros Cons
1
Similar game
curve
Project cum. Rev forward
to 180 days using similar
game comp curve
● Used only for new
games
● Accurate for
similar games
● High
variance
● Over-
estimate
2
Same Game
curve
Project cum. Rev forward
using historial same game
curve with Cohort data
● More accurate
● Switch method 1 >
2 in 30+ days
● Initial curve
Manual (iAP,
ads, paid
SKU)
3
Automated
Inverse Curve
● Scalable
● Works well with
established games
● Scalable
● Automated
reporting
● Ad-source
LTV is tough

12. Method 1: Similar game curve
1) Use as many days of actual cumulative revenue data for cohort as available.
2) Project cumulative revenue forward to 180 days using comp game curve (eg: Poker)
3) For illustration purpose, in below table, only one paid channel used per day

13. Method 2: Same game curve
1) Use as many days of actual cumulative rev data for cohort as available
2) Project cumulative revenue forward using game’s own cumulative revenue curve
3) Calculation in example is for value from ad install iAP only
4) For illustration purpose, multiple paid channel used on single day, LTV using 3 days
of cohort data

14. Method 3: Inverse Root
1) Aggregate cumulative rev for IAP and Ad rev for as many days as possible
1) Translate cumulative revenue onto a log-log plot
1) Algorithm estimates slope and intercept for the cohort and extends line to 180+ days
Cumulative Revenue Cumulative Revenue on log-log plot

15. Method 3: Inverse Root
Cumulative Revenue Cumulative Revenue on log-log plot

16. What timeframe to consider?
● Depends on the app retention
■ Eg: Poker - you might want to do 1+ yrs LTV
● Monetisation - iAP, ads, paid
● App mechanics - eg. game levels
Game 1 Game 2

17. How much to spend on User Acquisition?
Recommended: CPI = 180 days LTV
Agressive: CPI = 1 Year LTV

18. Ad Revenue Calculation : 180 days LTV
❖ Project “Sum of DAU of cohort” over 180 days
➢ Use actuals + game’s retention curve)
❖ Get average (Ad Rev / DAU) for a period of 1 year
➢ Assumption, you have by months or years of DAU & Ad Rev data
❖ Calculate Cohort Cumulative Ad Rev
➢( Sum of DAU of cohort ) * (Ad Rev / DAU)
❖ Find Ad Rev Value per Cohort Member
➢( Cohort Cumulative Ad Rev ) / ( Cohort Installs)

19. Paid App Calculation
❖ Find Historical % of Paid SKU downloads to Free SKU downloads
❖ Calculate Paid APP LTV per Install
➢ Historical % of Cohort purchasing paid App * ( $$ per App)
❖ Assumption: conversion on free> paid for UA channel is same as organic free> paid
conversion
❖ Example:

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