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.
