CASES
CASES
CASES OF CIM7
Large theme park with lodging facilities
Almost every Dutch person has ever been to a theme park. But when do they return? And enjoyed they also an agreeable stay in one of the lodging facilities?
A sizable part of the guests are registered and gave an opt-in for information.
Frequently newsletters are send with great information about the park and with attractive deals. But how can they communicate more differentiated, so guests receive appropriate information and better deals?
In steps we worked on a more selective approach. First on the basis of segment classifications, later based on age and family phase and now we combine this with a predicted frequency of visits.
The more differentiated communication leads to greater interest by the customer and a higher frequency of visits or a visit in combination with one of the lodging options of the theme park.
A global player in consumer electronics
Based on transaction data and behavioural data, the active and the prospective customer value are assigned to all customers. Through ‘recommender algorithms’ for each customer the product categories with the highest ‘probability to buy’ are predicted including the probability-to-buy score within those categories.
This enables not only a more targeted communication with the customers is, but also the proposition and the depth of the offer can be differentiated at individual customer level. Resulting in conversions of 4 to 5 times higher than generic offers.
Major car manufacturer and dealer organization
The experience of the car as perceived by the customer includes both the intrinsic product and the service around. So the brand performance is a result of both the car and the dealer performance.
In the customer value model both the car and the main dealer services are taken into account. On car and on customer level. This enables the manufacturer to prioritise on the highest possible customer value and customer experience. And it also provides many opportunities of benchmarking.
In addition, a predictive model is developed that provides insight in which cars are likely to be sold in the next three months or which cars not likely to come back to the dealer workshop for services.
Currently, these models are enhanced with ‘machine to machine data’.