Retaining existing customers costs less than acquiring new ones. However, it’s very hard to maintain good relationships with customers in the modern world of saturated markets. By maximising retention, companies can grow revenues and profits as their customer and user base grows. Peak gives companies a much stronger understanding of their customers through data and help businesses to predict churn propensity as early as possible, enabling intervention and improvements to customer care. In short, Peak helps businesses to keep their customers for longer and increase their profitability by recommending the most cost-efficient retention actions.  


Examples of data used in predicting churn include: transactional data, customer profile, purchase history, client feedback, social media activity and web tracking data. Peak’s churn detection methods identify customer segments with increasing churn rate, create profiles of churn risk groups, predict likelihood of attrition.




By combining heterogeneous customer behaviour data and using advanced Machine Learning techniques, Peak is able to extract insight from customer attrition and retention data. Time-series analysis is used to describe the customer churn trend. Decision tree analysis is employed for customer churn profiling to explore reasons why customers are leaving. As customer churn can be a “low ratio event”, artificial neural network is utilised for customer churn scoring.


Peak creates web-based churn analytics that represents up-to-date trends and churn scoring. In addition to this, a dedicated data science team supports its clients. Our Data Analytics solutions help clients to improve their customer retention rates and increases profitability, and also help to improve their marketing campaign efficiency. Thanks to Peak’s recommendations, businesses can implement proactive retention marketing strategy and delight their customers!

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