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LTV Predictions: How do real-life companies use them & what can you learn from it?

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A comprehensive summary about the lifetime value predictions that Martin, Head of Marketing at AppAgent, has learned by building analytics platforms for clients and consulting with the best of the best in the mobile industry.

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LTV Predictions: How do real-life companies use them & what can you learn from it?

  1. 1. LTV Predictions How do Real-Life Companies use Them & What can you Learn From It? Martin Jelinek Head of Marketing AppAgent
  2. 2. Introduction Martin Jelinek • University of Economics • 2007 – 2016: Independent Game Developer • 2017 – 2018: AppAgent • 2019 – Head of Marketing at AppAgent • Published / launched / consulted 40+ mobile games and apps • Designed and built AppAgent‘s in-house Marketing Analytics • Speaker at App Promotion Summit, GIC and other events
  3. 3. Introduction
  4. 4. LTV Calculator I could calculate LTV and ROI myself. Mastermind My company uses a custom LTV predictions model. LTV Predictor My company uses some form of LTV Predictions. ROI Primate I understand the concept of LTV and ROI and could explain it to this audience. Audience Level Check What is the level of YOUR understanding?
  5. 5. 20 minutes / 5 mins questions 3 Sections: - Predictions 101 - 3 most common modes - Insights & Learnings Today‘s talk:
  6. 6. PART 1/3 PREDICTIONS 101
  7. 7. Basics 1/4 - What is a prediction? • What‘s a prediction? • Using past experience to estimate what‘s going to happen. • Using historical data to understand what will happen with % certainty. •LTV vs. ROI predictions • ROI = LTV / CPA
  8. 8. Basics 2/4 – What are predictions for? • User Acquisition: • How much can we spend (and scale)? • When and how to adjust bids, creatives, targeting etc. • Confidence when scaling • Evaluation of new channels • (Re)-Engagement: • Re-engagement efforts focus • Identification of at-risk users • Special offers • Ads vs. in-apps Many benefits, two key areas:
  9. 9. Basics 3/4 – How do they work? • Using past to estimate future • Explore past data to find „rules“ that can be leveraged in calculations • Build a MODEL – set of rules and calculations on how to transfer input into output • Collect inputs (retention and ARPDAU) to • Point vs. Interval • The LTV for (segment) is $2,5. • Prediction: „There‘s a 95% chance that the LTV will be between $1.5 and $3.“
  10. 10. Basics 4/4 – No data, no predictions. • Characteristics of data • Variability • Level of spend, first purchase timing, behavior differences between segments • Amount of data • What is the minimum amount of data points for reliable predictions? • Userbase vs. refined segments • App versions, seasonality.. To predict, we need past observations (data).
  11. 11. PART 2/3 PREDICTION CONCEPTS
  12. 12. 3 Most common LTV prediction models: - Retention x ARPDAU - „Ratio“ model - „Behavioral“ model Concepts of LTV Models
  13. 13. „Getting a couple of retention datapoints for a cohort to model the shape of the retention curve.“ pLTV = pRetention x ARPDAU Concept 1 – „Retention x ARPDAU“
  14. 14. „If a cohort spent 100 bucks during the first 7 days, we know from past experience they will spend in average 4x more by the time they mature to day180.“ Revenue D180 / Revenue D7 Concept 2 – „Ratio“
  15. 15. User behavior > > LTV „If user xyz had 10 sessions during the first three days, at least three of them in the morning, and visited a promotional package page two times, he has a probability of becoming a payer of 35%. His pLTV is 35 USD.“ Concept 3 – „Behavioral“ Revenue D180 / Revenue D7
  16. 16. „Let‘s get a couple of retention datapoints to model to see how sticky is the game and model their retention curve. The more days they stay, the more they will pay.“ Retention + ARPDAU >>> pLTV Subscription App
  17. 17. „Let‘s get a couple of retention datapoints to model to see how sticky is the game and model their retention curve. The more days they stay, the more they will pay.“ Retention + ARPDAU >>> pLTV Ad-monetized app
  18. 18. „Let‘s get a couple of retention datapoints to model to see how sticky is the game and model their retention curve. The more days they stay, the more they will pay.“ Retention + ARPDAU >>> pLTV Casual Game
  19. 19. „Let‘s get a couple of retention datapoints to model to see how sticky is the game and model their retention curve. The more days they stay, the more they will pay.“ Retention + ARPDAU >>> pLTV Hardcore Game
  20. 20. „Let‘s get a couple of retention datapoints to model to see how sticky is the game and model their retention curve. The more days they stay, the more they will pay.“ Retention + ARPDAU >>> pLTV Airline Ticket Reseller
  21. 21. „Let‘s get a couple of retention datapoints to model to see how sticky is the game and model their retention curve. The more days they stay, the more they will pay.“ Retention + ARPDAU >>> pLTV E-Commerce
  22. 22. Who is responsible for driving the process?
  23. 23. PART 3/3 LEARNINGS & TAKEAWAYS
  24. 24. A model needs to be assigned depending on the app vertical (and type), monetization type, user behavior, business model and other factors. Which model is the best? NONE.
  25. 25. In case there‘s a huge variance in the data, predicting LTV with a sensible level of certainty could be almost impossible. Mission Impossible (SOMETIMES) MISSION IMPOSSIBLE
  26. 26. From what we‘ve seen and heard, the „ratio“ model is the most common. Even for the „big players“ – still, this conclusion could be biased, but still – even big companies often favour this. The question remains So what are companies using in real-life?
  27. 27. Even if there‘s added value of higher precision, the increased hassle is not worth it - Lots of engineering time to create / maintain - Apps keeps changing ... And complex is hard to change - UA team understands ... And btw, do you REALLY need a prediction model? Simple vs. Complex SIMPLE vs. COMPLEX
  28. 28. 3 main approaches – retention-based, ratio based, behavior-based Ratio model most frequent – probably as it‘s the best bang for the buck No one-size-fits all – each app is different and so is the optimal prediction model Not always possible – for some apps, predicting can be close to impossible Simplicity is favored – most companies we talked to prefer simplicity Marketers are responsible – most often, marketers drive the whole process Key Takeaways
  29. 29. martinjelinek@appagent.co Contact Thank you!
  30. 30. Thank You!

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