Mobile game maker keeps big spenders engaged

Use Case

Churn, upsell, lifetime value

A mobile game maker wanted to increase player engagement. Using Pecan, they were able to cut the player churn rate in half with models built in a matter of days.

Industry: Mobile games

The Game: In the 20 top-grossing games in category with over 5M downloads

Solution: Identify potential VIP, high-value players to increase retention and in-game spend

Platform Use Case: Churn, upsell, lifetime value

Data Stack: BigQuery

3x

prospected VIPs

2x

lower churn rate

3.5x

uplift of spend per user

Challenge

Keeping and enticing gamers

A mobile game maker wanted to start using predictive analytics for in-game management and to assess its business performance and bottom-line impact. They focused on one of their games, ranked in the 20 top-grossing games in its category with over 5M downloads, to see if results could be further boosted by applying AI.

Like any online company, this game maker wanted to identify its customers, build distinct profiles for each, and match them with appropriate offers and content. Prior to using Pecan, the company managed only to partially leverage its data to improve business results, despite boasting many unique active players a week and engaging in hundreds of thousands of weekly transactions.

This game maker also faced the common problem of player churn. Finally, they also had difficulty in adapting their offerings to different player profiles and found it hard to identify high-value players early enough in their relationship with the game.

Solution

A predictive approach

Before turning to Pecan, this gaming company used manual statistical methods to build distinct profiles and match each of the profiles with appropriate marketing offers, content, and products.

The results were less than optimal, so the company used Pecan to build 3 predictive analytics models, each focused on a specific prediction task:

  • Identify potential high rollers
  • Predict which of these customers is receptive to marketing activities
  • Predict which of these customers will churn

This type of data-driven segmentation ensures that players are continuously getting value from the game. Informing personalized marketing outreach with these predictions can increase revenues, reduce churn, and help build successful future products.

This company built the three models with Pecan in 12 days. That’s a dramatic improvement on the 4-6 months typically required for traditional, manually executed solutions to these challenges.

It’s also important to note that one of the customer’s BI managers built these AI models using the Pecan platform. Using Pecan avoided the need to involve data scientists, who are often overloaded with demands from various teams and can be difficult to access for projects like these.

Because the BI manager was able to use Pecan and achieve solid predictive models in such a short amount of time without data science resources, the project cost was estimated to be just 25% the cost of alternative solutions.

Results

Owning the future

The Pecan model identified more than 200% additional potential “high rollers” or VIP players. By treating those potential VIPs with special care early in their lifecycle, the company converted the majority of them into highly valuable customers. Each segment was tested separately, and the average segment saw 3X uplift of spend per user.

With the users who were predicted to churn, similar tests showed equally strong results:

  • Users who weren’t provided customized offers did actually churn
  • Pecan’s identification of those likely to churn helped cut churn in half
  • Users who received customized offers spent more than 4X more than those who didn’t

This mobile gaming company determined the results of this campaign: less churn with higher spent per user and, most critically, a small investment of time and resources for a fast ROI.

The company quickly reaped the rewards of empowering its BI team with the capability of building state-of-the-art AI predictive analytics models. The results reveal the powerful benefits of adopting a predictive approach to users’ future behavior and taking action based on that foresight.

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