Customer retention plays a critical role in a firm's long-term sustainability. Until recently, however, firms have tended to rely on one-dimensional survey techniques to measure their customers' loyalty. This white paper shows how machine learning can help overcome these limitations.
We know from previous research that relying on satisfaction ratings to measure customer loyalty is problematic. It often masks underlying dissatisfaction with a company's products and services and that a significant proportion of its customers are at risk of churn. With better insights, these customers could be prevented from seeking alternative providers.
This research applied a more nuanced approach to understanding customer loyalty, looking at what customers think, feel and do in order to arrive at a more accurate prediction of their future behaviour. By combining this framework with advanced machine learning techniques and applying them to attitudinal,
emotional and transactional customer datasets in a B2B context, we have developed an approach which was 93% reliable in its prediction of churn.
Read the white paper Unlocking the secrets of customer loyalty.