Using Intuceo’s proprietary analytic and predictive technology, a solution was developed for a leading child education toy company to predict customer lifetime value (CLV) and gain accurate insights to enhance customer satisfaction, engagement and retention through targeted marketing.
In order to predict customer lifetime value, the client needed:
- To know how much revenue the customer is likely to give in the next couple of years based on early engagement with their games.
- To rank order the customers based on the predicted class they belong to so as to understand their contributions to the profits rather than treating all customers alike.
- To empower its decision makers by giving them the ability to extract and analyze customer data for informed decision making.
At Intuceo’s core are five patent-pending analytic and predictive engines:
- Data SharpTM pre-processing wizard – the total revenues generated by customers was binned into 4 classes
- Quick InsightsTM preliminary insight validator – created initial variables – like does the family have a preferential game or do they play several games, what is the age differential between eldest and youngest kid in the family
- Hidden InsightsTM deep insight extractor – extracted about 82 attributes including the class attribute and these were used to build the models
- Bold VistasTM predictive modeler – predictive models were delivered to the business users via comprehensive self-explanatory graphs and charts on a web dashboard.
- Accurate decision making with customer micro-segmentation – People who play a single game in the first week are likely to give 1/3rd as much revenue as people who play multiple games.
- Targeted marketing – Families with more kids means more revenue – giving $11 in revenue per year per kid.
- Improved customer retention – Customers who played multiple games are sticky AND give more revenue
The client implemented iCube’s predictive analytics solution to predict the lifetime value of a customer based on his or her early engagement. Data set consisted of 6,093,598 customer transactions of usage data collected over 5 years for 10,000 unique customers.