Predictive Retention

Identify customers at risk of churn before they leave you

Predictive Retention is a combination of sophisticated analytical, modelling and deployment software, customised professional services with bespoke training and user support resources, based around market-leading predictive analytical software IBM SPSS Modeler. Predictive Retention is designed for any organisation that has retention of its customers and users of its services as a central element of its operating model.

The implementation of Predictive Retention will enable your organisation to identify the key drivers of customer churn, student drop out or subscription cancellation (to name but three popular applications). You’ll be able to identify people at most risk of leaving you and prioritise which ones you should concentrate on trying to retain, with sufficient foresight to allow for effective intervention that will have the best chance of altering the predicted outcome.

Predictive Retention also enables you to determine where you should best invest resources in order to alter outcomes e.g. to target students at greatest risk of dropping out in their first semester and to prioritise those that come from more deprived backgrounds or, in a commercial context, to target resources aimed at retaining only those customers who are likely to be of the highest value to your organisation over their lifetime with you.

What exactly is Predictive Retention and what can it do?

Predictive Retention is a software product, supported by tailored services and training delivery, designed to enhance customer loyalty initiatives through more sophisticated analytics, the development of predictive models and through the use of those models to enable optimised decision-making.

Predictive Retention will allow you to:

  • Understand how patterns in operational and transactional data sources correlate with and explain the increased risk of a customer / student / subscriber leaving you or becoming dormant
  • Generate a hierarchy of the key early indicators that identify heightened risk of customer churn
  • Build predictive models that will allow you to predict the churn risk / risk of dormancy at the level of the individual
  • Exploit hidden patterns in unstructured (text) data to enhance model accuracy and uncover the subtle but critical details that make the difference between loyal, committed customers as compared to those who exhibit a heightened risk of leaving for a competitor
  • Balance risk of churn with wider customer / member / subscriber value, allowing you to target your retention efforts to your most valuable customers
  • Develop the basis of a real time ‘Red Alert’ system that will identify high value customers at risk of becoming defectors due to changes in circumstances or experiences and allowing you time to intervene and take remedial action

Who should use Predictive Retention?

Predictive Retention has relevance to a wide range of organisations, including (but not limited to):

  • Companies that rely on repeat business with clients to build long-term profitability
  • Organisations with products or services based around a subscription or renewable contract
  • Educational establishments that rely on student fees for revenue where premature student departure has multiyear negative impact on funding
  • Companies already using business intelligence and key performance indicators based on customer data and activity to provide a summary of retrospective performance
  • Organisations that sell their products and services to individual consumers and those engaged in business to business activities
  • Organisations with large amounts of untapped, structured and unstructured data (verbatim text) that wish to gain a deeper understanding of satisfaction and loyalty

Our Predictive Retention solution helps organisations drive greater profitability through improved customer retention by developing a detailed understanding of why customers stop doing business with you, in order that they can actually predict individual customers’ likelihood to leave, before this happens and with enough time to take positive action.

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