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# How much data is needed to calculate LTV?

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This presentation was made at a data science meetup hosted at N3TWORK's offices during GDC 2018. The presentation is a follow-up to a post with the same title published on Mobile Dev Memo in early 2017.

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### How much data is needed to calculate LTV?

1. 1. How much data is needed to calculate LTV? Eric Benjamin Seufert GDC Data Science Meetup @ N3TWORK March 20, 2018
2. 2. Who I am N3TWORK Mobile Dev Memo Freemium Economics
3. 3. Who I am
4. 4. Review: How do we build an LTV estimate? - Get clarity on model; - Get clarity on early stage signal
5. 5. Review: How do we build an LTV estimate? - If we’re confident in the model, we can use early-stage monetization data to project out to some goal LTV metric (“Day X LTV”); - Eg. if we want to recoup our money in 90 days, we should project a Day 90 LTV; if we are confident in our model, perhaps we can estimate a Day 90 LTV with just 7 days of data.
6. 6. Review: How do we build an LTV estimate? - 7 days isn’t a long time. But how much data do we need to even get a comfortable, robust estimate of 7-day LTV?
7. 7. The Approach - Simulate the collection of monetization data to understand how many users it takes to get a good estimate on Day 7 LTV;
8. 8. The Funnel - As we acquire users into our app, we accumulate data over time; - The amount of data we have at Day X is a function of the input new users + retention + days that new users have been acquired; - With 100% user retention, with 100 DNU running for 4 days, we have 400 data points for Day 1, 300 data points for Day 2, 200 data points for Day 3, and 100 data points for Day 4 (only one cohort has reached Day 4 in our app); - Adding in a typical retention curve makes this degradation in data size even more pronounced.
9. 9. The Approach - Simulate the collection of monetization data to understand how many users it takes to get a good estimate on Day 7 LTV; - Build some reasonable assumptions of how our app / marketing performs:
10. 10. The Approach - Generate a retention curve:
11. 11. The Approach - Generate a retention curve:
12. 12. The Approach - Simulate cohorts joining the app by using a pareto distribution to model monetization
13. 13. The Approach - Stack up the monetization by Day to come up with LTVs
14. 14. The Approach - Calculate confidence intervals for each Day X LTV
15. 15. The Approach - Calculate confidence intervals for each Day X LTV - 5 Days of cohorts, 500 users per day - Day 4 LTV CI 95% is \$0.38 -> \$0.62 - \$0.24 difference, nearly 50%! - And that’s just Day 4!
16. 16. The Approach - What about 90 days of traffic buying? - Day 5 has a tight CI - But Day 90 spread is large
17. 17. Thanks! @eric_seufert eric@mobiledevmemo.com