With new customer acquisition costing much more than retaining existing customers, customer retention is understandably a top priority for all organisations. A churn model can provide insight into the reasons customers leave so you can design a data-driven retention strategy.
By understanding your churn rate, you establish a benchmark against which to measure your retention efforts. Knowing how churn rate varies by month, product line or customer type can also help inform customer segments for more effective targeting and personalisation.
By analysing the data of customers that leave or cancel your product or service, we can discover trends in your data that indicate propensity to churn. Gaining deeper insight into the attributes or behaviours that lead to your customers leaving allows you to not only address any glaring issues, but also begin to develop a predictive churn model.
Once we've established a benchmark and clear understanding of your churn rate and churn drivers, we can then look to predictive churn modelling to measure the immediate or future risk of customer attrition. Using behavioural and transactional data, we then apply statistical and machine learning techniques to predict the probability that a customer will churn.
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