Addressing customer churn through Predictive Analytics

For today's companies, a customer centric strategy is quite relevant for achieving success. To gain new customers is only halfway of the concern to achieve a long-term and sustainable business growth; it is also about retaining and engaging them. 

Companies are getting more aware of customers’ satisfaction. They need to differentiate their business and build engagement with their customers by paying special attention to churn analysis and manage it from a profitability perspective. Churn could happen due to many different reasons and its analysis helps to build effective retention strategies. 

Traditionally, companies focused their resources to make up for the lost revenue by following a strategy that aims to acquire new customers instead of diminishing their customer churn rates. This is an even more expensive strategy. As stated by the Harvard Business Review, “Acquiring a new customer typically costs anywhere between 5 and 25 times as much as it does to retain an existing customer”, making it crucial for any business to lower their customer churn before following any other strategy. 

 Reducing churn is more important than ever. According to the experts, a high percentage of businesses’ revenues is lost due to customer churn; therefore, there is a high weight and value attributed to the process of understanding the behaviour of those customers and taking the right steps to address it. 

In the telecommunications industry, where churn is crucial and multiple providers deliver similar solutions, companies need to differentiate their business and offer the best quality of service to retain customers. Telecom operators that believe in the potential of predictive models and driven analytics are more likely to reduce their churn rates that the ones that do not. An analytics-driven approach can help telecom companies reduce churn by as much as 15%. Predictive analytics has become a promising backbone in translating millions of data patterns into actionable insights, which help companies prevent and reduce future customer churn. 

By modelling customer churn, businesses could identify customers that are likely to stop engaging with the brand or the ones who are varying in their behaviours and preferences by obtaining an algorithm that turns customers' historical data into a probability of churn for each customer over a period of time. 

Improve your customer’s journey 

The first step to address customer churn is to optimize the Customers’ Experience by using data sets to obtain a global view of the customer decision journey. New technologies have given consumers the power to be informed and to compare prices, complain loudly and decide. 

In this regard, companies should understand customers' expectations from the very beginning to gain competitive advantage and reduce the probabilities of cancellation. 

Analyse, diagnose and predict data 

Advanced algorithms and Machine Learning techniques are necessary to understand and identify which factors make customers to leave. This analysis allows to identify variables which define customers' behaviour and preferences and predict churn probabilities, among other insights. 

These techniques are used because of their ability to handle complex relationships in data which make companies to easily analyse the reasons behind those behaviours to come up with proper solutions. 

Taking a proactive vs. reactive approach 

After having analysed the data and predicted the path of customers’ behaviour, acting accordingly is the next step to follow. Customer churn is an issue that must be taken care of before it is clearly apparent. Artificial Intelligence (AI) also plays an important role by applying the resolution from the data analysed and allowing companies to target proactively the segment that will be more likely to churn. 

Therefore, the leverage of Machine Learning and predictive analysis techniques to generate a predictive and data-driven strategy, will allow companies to focus their resources on a proactive approach that will target the right segments. 

Customers are the key for any business, but churn management is essential. MedUX, through predictive analytics techniques, builds predictive models that enable to poll millions of customer observations and variables to identify this kind of realities. By acting upon the right identified recommendations, telecom companies can have a global view of their customers to predict churn, ensure customers loyalty, reduce costs and remain competitive.

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