- Analytics
- Churn / Attrition and Retention / Loyalty Models
- Clustering
- Customer Analytics
- Customer Life-Cycle Analysis
- Customer Segmentation
- Customer Value Models
- Data Analysis
- Data Mining
- Data Segmentation
- Decision Trees
- Market Analysis
- Market Basket Analysis
- Predictive Modelling
- Profiling
- Recency, Frequency, Value (RFV) Analysis
- Response Analysis and Models
Predictive Modelling
Predictive modelling can help enhance customer communications, increase retention and improve returns for virtually any business with a customer database. They can be applied to both B2C and B2B verticals (dependant on data availability) across travel, leisure, retail, IT, automotive, finance and any industry that can benefit from a greater understanding of its customers’ behaviour.
Predictive Modelling is used to help clients understand how their customers are likely to behave in the future, thus giving them the chance to act up on this information in a timely manner. If a customer is likely to lapse then it is much more useful to be aware of that before it happens than after the event. Likewise if a customer has a strong likelihood of becoming a high spender or to reach top tier status, then it is useful to be aware of that as soon as possible (even before they have started transacting) so that action to ‘lock them in’ can be taken.