Strategies and case studies to grow LTV in 2023 / Julia Iljuk (Balancy)
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The document outlines strategies and case studies focused on enhancing player lifetime value (LTV) in mobile gaming for 2023, emphasizing the importance of liveops for monetization and user retention. It discusses player segmentation and personalized offers to improve conversions and includes a case study demonstrating successful tactics that increased conversion and average revenue per paying user. The document also highlights the state of the gaming market and suggests practical tips for designing compelling special offers.
Strategies and case studies to grow LTV in 2023 / Julia Iljuk (Balancy)
- 1. Strategies and case studies to grow LTV in 2023
- 2. Let’s get to know each other /julia-iljuk ■ 7 years in the game industry ■ LiveOps enthusiast and Head of Growth at Balancy ■ The host of online courses about game analytics on Edvice ■ Wargaming Forge LiveOps Alumni Follow for LiveOps tips and best practices
- 3. Agenda 1. Why LiveOps, why today? The state of mobile games market 2023 2. The power of player segmentation and personalized value proposition 3. Success stories: how Hot Siberians use LiveOps tactics and Balancy tools to unlock games’ LTV growth 4. More tips on designing special offers and LiveOps.
- 4. Gaming market competition forces studios to evolve monetization tactics Gaming market is saturated and polarized UA attribution is broken Studios hit the LTV ceiling How to retain users better, convert them to paying and sell them more? LiveOps is the best tool to increase conversion to paying users and to grow LTV. Paid games - the explosion of mobile gaming, $1-5 per install Free-to-play games, in-app and ads monetization, LTV is $1-100 per paying user Player-tailored games with LiveOps give 2-3x LTV growth compared to f2p games
- 5. What is LiveOps? Personalized data-driven events/updates To maximize the value of the game without involving the dev team
- 6. The state of mobile gaming 2023 Sensor Tower report 💡LiveOps is a key monetization and retention structure for top grossing games attempting to maintain long-term player engagement and spending.
- 7. The state of mobile gaming 2023 Sensor Tower report 💡 On average, an Action game with LiveOps can generate 30 times more in revenue than a game without it.
- 8. Player segmentation Target right players at the right time Segmentation type Segment examples Strategy example Life cycle stage Newbies Convert with simple starter packs with a low price point and first-purchase bonuses Veterans Entertain and upsell with more diverse and unique content Activity Active Convert or upsell taking into account other segmentation types a player belongs to Churning Retain with monetary (discounts) and engagement (goal setting, events) tactics Lapsed Return by communicating via outside of the game means Paying status Non-paying Convert with first-time conversion packs with great value; or turn them into your promoters Paying Upsell based on previous paying activity of players, offer items similar to what they like Paying prediction Likely to pay Convert or upsell with relevant offers based on their previous activity in the game Not likely to pay Turn them into your promoters Size of payments Minnows / dolphins / whales Adjust pricing and discounts according to their paying capacity
- 9. Personalized value proposition Offer combination, messaging, pricing and highlights Mike 32 years Levels completed: 7 Daily playtime: 1.16 hours Time to convert: 4.33 days Max pay: $19.99 Probability to churn: 15% Monika 24 years Levels completed: 24 Daily playtime: 0.83 hours Time to convert: 7.33 days Max pay: $4.99 Probability to churn: 37%
- 10. Case study: idle city builder ● Expand base ● Train soldiers ● Provide allowance ● Complete Missions The Idle Forces: Army Tycoon About the game
- 11. Experiment 1 Special offers for ad watchers to convert them into their first purchase
- 12. Experiment 2 Dynamic special offer with personalized pricing according to players’ paying capacity
- 13. Experiment 2 Dynamic special offer with personalized pricing according to players’ paying capacity
- 14. Experiment 3 The matrix of offers that provides value propositions based on previous purchase history Duration of experiments: ⏱2 months Results: ⬆ Conversion 4% -> 5.5% ⬆ ARPPU +50%
- 15. Designing a special offer Relevancy Time limit Specified discount Clustering Anchor
- 16. Designing a special offer One-time only Specified discount Additional value Time limit Minimum text
- 17. Designing a special offer (bonus) Visit our blog (balancy.co/blog) for video tutorials on creating effective special offers that players can’t resist.