How Machine Learning can solve               Customer Churn 

What is it 

Customer churn, simply put, is a metric that tells you how many customers have stopped using your product or service. In a lot of cases this is associated with actually switching providers in favour of competitors, and can represent a costly set of mistakes committed by a company. 

Knowing your company’s churn factor is helpful in understanding if lucrative accounts are being lost, but can also serve as an overall indicator of your current customer experience, which can help you tailor your product offerings to optimize your customer retention efforts. 

The possibilities 

Having a good understanding of your customer churn rates can help significantly in developing coherent marketing strategies for customer retention. 

Most companies have a particular set of customers that signify a large portion of their business, and maximizing their experience is of paramount importance. It can be tempting for companies to spend lots of time on acquiring new customers, but this can often be quite a costly process and in the worst cases can lead organizations to neglect key segments that are important to their current business. 

Customer churn mistakes solved by Machine Learning Consultants

How can Machine Learning help: 

Machine Learning can be extremely useful in simplifying what can otherwise be a quite overwhelming process.

This is based on typical behavioral patterns by customers. More specifically, there are particular indicators that can strongly determine whether a customer is on the brink of churning. 

There are overt examples of this, such as spending less time on your website, or reducing the amount of click-throughs on email campaigns, but there are more subtle examples too, like leaving carts without purchasing, displaying lower levels of engagement or increasing times between website or store visits. 

Machine Learning can actually help in predicting when a customer is about to churn. 

The process consists of feeding the machine with historic data of previous examples where customers have churned. It can assess the different factors that have caused this, the key indicators and purchasing habits that have preceded customer churn in the past, and use this information to develop accurate forecasts that can help you predict when particular customers are about to churn. 

The idea is that this will help you target your marketing efforts better, through developing strategies to keep customers that are verging on churning, such as offering special deals or improving their purchasing experience.

This can also allow for a greater clarification of goals. A marketing team can focus on bringing new customers in without feeling guilty about neglecting pre-existing users through ensuring added value in their product offerings and an overall better customer experience, which ultimately is the aim of the game. 

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