Maximize User Acquisition & Player Lifetime Value with AI

Maximize User Acquisition & Player Lifetime Value with AI

Table of Contents

1. Introduction
2. The Challenges of User Acquisition
   • Macro Conditions Post-Pandemic
   • Changes in Markets
   • Decrease in Revenue from In-App Purchases
   • Privacy Age and Its Challenges
3. The Need for Deep Understanding of Data
4. The Ask Predict Act Framework
5. Predicting Campaign Return on Investment
6. Managing iOS User Visibility using AI
7. Overcoming Apple's Privacy Restrictions
8. The Magic of Predictive Analytics
9. Integrating Predictive Signals into Campaign Management
10. Summary and Conclusion

The Challenges of User Acquisition and Achieving Higher Player Lifetime Value Using AI

In today's competitive gaming industry, user acquisition managers face numerous challenges in acquiring and retaining players. The macro conditions post-pandemic have led to a decrease in consumer spending, which has slowed down the growth of the gaming industry. Additionally, market competition has increased, making it more expensive to acquire new users.

Furthermore, changes in monetization strategies have resulted in a decrease in revenue generated from in-app purchases. On top of these challenges, the privacy age led by Apple has introduced complex difficulties in optimizing and measuring marketing activities.

To overcome these challenges, a deep understanding of data and players' lifetime value is crucial. By utilizing predictive analytics and AI-based predictions, user acquisition managers can achieve better marketing performance. This article explores the strategies and frameworks that can be employed to boost user acquisition and achieve higher player lifetime value.

Introduction

In the gaming industry, user acquisition plays a crucial role in the success of mobile gaming companies. As an essential part of the marketing strategy, user acquisition managers and marketing teams constantly strive to improve their campaigns and maximize their return on investment (ROI). However, in recent times, several challenges have made it increasingly difficult to achieve positive ROI.

The Challenges of User Acquisition

1. Macro Conditions Post-Pandemic Since the COVID-19 pandemic, consumer spending patterns have changed significantly. This shift has impacted the gaming industry, leading to a slowdown in its previous rapid growth. User acquisition managers now face the challenge of acquiring new players in a market where consumer spending is more conservative.

2. Changes in Markets While the United States remains a top market for mobile games, it has become increasingly competitive. Acquiring users in such a saturated market has become more expensive, putting a strain on user acquisition budgets.

3. Decrease in Revenue from In-App Purchases There has been an overall decrease in the revenue generated by in-app purchases in the gaming vertical. This decline poses a challenge for user acquisition managers, as they need to find alternative ways to monetize their games and increase their players' lifetime value.

4. Privacy Age and Its Challenges The introduction of privacy restrictions, particularly by Apple, has added complexity to marketing activities. With limited access to user data and reduced attribution abilities, user acquisition managers face difficulties in optimizing and measuring their campaigns effectively. The uncertainty caused by these privacy restrictions has led to a shift in user acquisition from iOS to Android platforms.

The Need for Deep Understanding of Data

To overcome these challenges, user acquisition managers must focus on a deep understanding of their data and players' lifetime value. By utilizing AI-powered predictive analytics, they can gain insights into user behavior and make informed decisions that maximize ROI.

The Ask Predict Act Framework

At Pecan AI, we have developed the Ask Predict Act framework to help user acquisition managers navigate the complexities of user acquisition and achieve higher player lifetime value. This framework consists of three main steps:

1. Ask: Define predictive questions that reflect specific business needs. For example, user acquisition managers can ask which users are likely to convert in the following week. These predictive questions serve as the foundation for the subsequent steps.

2. Predict: Utilize first-party internal data of gaming companies to generate ongoing predictions. This involves data preparation, training AI models, and generating accurate predictions. The predictions encompass various metrics, such as conversion rates, high-value users, and lifetime value.

3. Act: Integrate the predictions into the systems of action, such as DMMP (Data Management and Marketing Platforms), BI tools, Facebook, or Google. This ensures the predictions are actionable and can be used to optimize marketing campaigns effectively.

By following this framework, user acquisition managers can make data-driven decisions based on predictive insights, enabling them to maximize their marketing performance and achieve higher player lifetime value.

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