3 Steps to Stop Daily Active User (DAU) Bleeding Using Predictive
Keeping & enticing gamers
Gigantic wanted to start using predictive analytics for its in-game management and to assess its business performance and bottom-line impact.
The company, like any online company, is interested in identifying its customers, building distinct profiles and matching each of them with appropriate offers and content. Prior to using Pecan, the company only managed to partially leverage its data to improve business results despite boasting many unique active players a week and with hundreds of thousands of weekly transactions.
Like other online companies, it also suffered from player churn. Other challenges included difficulty to adapt the offering to different player profiles and inability to identify high-value players early enough in the process.
A predictive approach
Before turning to Pecan, Gigantic used other statistical and manual methods to build distinct profiles and match each of them with appropriate marketing offers, content and products.
The results were less than optimal so the company built with Pecan 3 deep learning-based predictive analytics models:
- To identify potential high rollers.
- To predict which of these customers is susceptive to marketing activities.
- To predict which of these customers will churn – and how to prevent that.
Gigantic used the Pecan platform to build the models in a total of 12 days, compared with 4-6 months required by alternative solutions, which also consume huge and expensive resources of data scientists.
Using Pecan instead of traditional methods meant that the cost of the project was estimated at 1⁄4X of the alternative solutions.
Furthermore, the AI models were built by one of the customer’s BI managers using the Pecan platform, avoiding the need for trained and expensive data scientists.