Identify customers at risk and gain insight into the drivers of churn, to more effectively target your retention efforts.
New customer acquisition costs much more than retaining existing customers - so retention should be a top priority. A churn model can provide insight into the reasons customers leave so you can design a data-driven retention strategy.
Customer Churn Analysis looks at the data of those who have churned and identifies the risk triggers and characteristics that are common to those who have left. This data shapes a predictive churn model. By overlaying data from current customers with these trigger points, you can identify those likely to leave – and take action.Get in touch
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 a benchmark and insights into your churn rate and churn drivers is established, we can look to predictive churn modelling to measure the immediate or future risk of customer attrition. Using behavioural and transactional data, we apply statistical and machine learning techniques to predict the probability that a customer will churn.
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Find out how Qrious can help your organisation tackle churn and drive retention.