Contactdata receives data regarding your customers and their related events, such as purchases, responses to digital messages and so on, then adds this information to the relevant customer profile. The data extrapolation is carried out using tailor-made algorithms. Depending on the data you have and the information you want to extract, you can activate the suitable algorithms, or request customized ones to be made available.
This guide includes an overview of:
- The available Contactdata algorithms.
- The required input data.
- The output that is added to the customer profile.
The algorithms enrich the profiles of individual customers. All information provided by Contactdata is available, for example, in Contactplan for segmentation activities.
Certain algorithms use the available information to create clusters. These are marked as ‘clustering’ in the descriptions provided here. Other algorithms use models to predict future customer behavior. These are marked as ‘predictive’.
The algorithms are available in a library, which includes:
- Customer purchase statistics.
- Customer time-based behavior statistics.
- Customer purchase preferences.
- RFM clustering.
- Customer Engagement.
- Dynamic Customer Engagement.
- Customer Lifetime Value (CLV).