Since 2012 about 20% of clients in financial sector are leaving their service providers annually. On base of long term and recent history data the clients intending to leave are identified with up to 96% accuracy depending on available data. The identification serves as the basis for targeted communication and marketing in aim to prevent the individual clients to leave.
The cost of acquiring a new client is several times higher than the cost of retainment of existing clients.
Propensity to buy
Analysis of available data gives result for individual clients how likely they are buying a particular financial product. The model outcome directs the financial institution to right time offerings to individual clients or customer segments.
Optimal product pricing determines the optimal price for individual financial products in aim of different client segments or individual client's expectations.
Recommendation engines basically use algorithms and data to recommend the most relevant items to a particular individual client or customer segments.