Customer Retention Program
Aligning brand expectations with brand experiences to preserve the loyalty of your customer
In most cases churn ‘saves’ are handled on a reactive basis, with desperate efforts to retain customers being made at the point where a customer calls to terminate a contract. While some of these ‘saves’ will be effective, all too often the churn driving damage has been done and the likelihood is the customer will leave, often never to return.
Churn Prediction and Management
The Praxidia Customer Retention Program tackles the problem ‘upstream’ of the save scenario, identifying potential churn candidates ahead of the final ‘trigger’ that may result in their departure.
Potential churn casualties have a recognisable ‘signature’, the evidence of which can be found through analysis of their profile combined with their interaction history. For every organization this ‘signature’ will be unique, as different demographics, markets and service offerings reshape the nature of every brand offering.
Praxidia’s predictive churn analytics solution enables the processing of hundreds of data points to identify and isolate the key variables (predictors) that may be causing your customers to churn.
Upon completion of the prediction ‘algorithm’ the customer base can be profiled to identify the % propensity to churn on a customer-by-customer basis. However, prediction only provides one piece of the puzzle; the success of the save will require a careful understanding of how to approach the customer, with what message / offer and through which mechanism.
The Praxidia Customer Retention Program provides this additional level of prescribed detail, segmenting customers based upon their primary churn driver and identifying the most appropriate action based upon their segment / scenario.
Churn Prevention at Source
It is important to consider that while Churn prediction can help you to intervene and save a significant number of potential churn casualties, it does not immediately address the root causes that nurtured their disloyalty in the first place.
Once churn prediction and intervention has been successfully implemented, efforts are re-focussed to look upstream of the churn event to identify improvements to products or services that will mitigate churn drivers at source.