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Use ML to Predict and prevent customer churn and help businesses with a huge additional potential profit source
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Master Customer Churn Prediction and Prevention using ML
Customer Churn Prediction and PreventionPredicting and preventing customer churn represents a huge additional potential revenue source for every business. What is Customer Churn?Customer churn (also known as customer attrition) refers to when a customer (player, subscriber, user, etc.) ceases his or her relationship with a company. Online businesses typically treat a customer as churned once a particular amount of time has elapsed since the customers last interaction with the site or service. The full cost of churn includes both lost revenue and the marketing costs involved with replacing those customers with new ones. Reducing churn is a key business goal of every online business. The Importance of Predicting Customer ChurnThe ability to predict that a particular customer is at a high risk of churning, while there is still time to do something about it, represents a huge additional potential revenue source for every online business. Besides the direct loss of revenue that results from a customer abandoning the business, the costs of initially acquiring that customer may not have already been covered by the customers spending to date. (In other words, acquiring that customer may have actually been a losing investment.) Furthermore, it is always more difficult and expensive to acquire a new customer than it is to retain a current paying customer. Reducing Customer Churn with Targeted Proactive RetentionIn order to succeed at retaining customers who would otherwise abandon the business, marketers and retention experts must be able to (a) predict in advance which customers are going to churn through churn analysis and (b) know which marketing actions will have the greatest retention impact on each particular customer. Armed with this knowledge, a large proportion of customer churn can be eliminated. While simple in theory, the realities involved with achieving this proactive retention goal are extremely challenging. The Difficulty of Predicting ChurnChurn prediction modeling techniques attempt to understand the precise customer behaviours and attributes which signal the risk and timing of customer churn. The accuracy of the technique used is obviously critical to the success of any proactive retention efforts. After all, if the marketer is unaware of a customer about to churn, no action will be taken for that customer. Additionally, special retention-focused offers or incentives may be inadvertently provided to happy, active customers, resulting in reduced revenues for no good reason. Unfortunately, most of the churn prediction modeling methods rely on quantifying risk based on static data and metrics, i.e, information about the customer as he or she exists right now. The most common churn prediction models are based on older statistical and data-mining methods, such as logistic regression and other binary modeling techniques. These approaches offer some value and can identify a certain percentage of at-risk customers, but they are relatively inaccurate and end up leaving money on the table.A Better Churn Prediction ModelOptimove uses a newer and far more accurate approach to customer churn prediction: at the core of Optimoves ability to accurately predict which customers will churn is a unique method of calculating customer lifetime value (LTV) for each and every customer. The LTV forecasting technology built into Optimove is based on advanced academic research and was further developed and improved over a number of years by a team of first-rate PhDs and software developers. This method is battle-tested and proven as an accurate and effective approach in a wide range of industries and customer scenarios. Without revealing too much about the secret sauce of Optimoves customer churn prediction technology, the approach combines continual dynamic micro-segmentation and a unique, mathematically intensive predictive behaviour modeling system. The former intelligently and automatically segments the entire customer base into a hierarchical structure of ever-smaller behavioural-demographic segments. This segmentation is dynamic and updated continually based on changes in the data. The latter is based on the fact that the behaviour patterns of individual customers frequently change over time. In other words, the segment route history of each customer is an extremely important factor determining when and why the customer may churn. By merging the most exacting micro-segmentation available anywhere with a deep understanding of how customers move from one micro-segment to another over time including the ability to predict those moves before they occur an unprecedented degree of churn analysis accuracy is attainable. Beyond Customer Churn Analysis: Preventing Customer Value AttritionOptimove goes beyond simply predicting which customers will abandon the business by providing early warnings regarding customers whose lifetime value prediction has declined substantially during the recent period, even though they are still active and may not abandon the business entirely in the near future. Optimo
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