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Convincing a new customer to buy your product is harder than retaining the old ones. However, how would you know who is going to leave and who is not? Check below to see how machine learning helps you in this matter, too!

You may have heard this phrase a thousand times, “new customers are costly to get, current ones are beneficial to keep”. This is into no discussion. It has been proven solid. However, how would you know the churn probability of an individual? There are so many parameters and stochasticity in this function that leaving it be seems easier. Nonetheless, for a good matter of time, managers have been trying to understand the notion behind customer churn. Idea is that, if you reveal the sequences of behavior of the churned customers, it may lead to a conclusion that a pattern can be drawn. Thus, whenever a familiar pattern has seen, managers would understand there is a high chance of churn.

Why all trouble trying to calculate this difficult parameter. Reason is mentioned above: new ones are costly old ones are not. If a business constantly loses customers, its CLV (customer lifetime value) will drop. That is, with every customer leaving the company, means of gaining a new customer will be expensive in terms of marketing campaigns etc. Moreover, not knowing who is going to leave or not will affect the public relations department as given loyalty’s effects will drop. That is, as you do not know who is leaving, you may end up giving extra stuff to a customer determined to leave whereas not giving anything to a customer who is on the verge of leaving and not leaving. Thus, you may lose the second customer, and as first customer is already made up his/her mind, you have ended up losing two customers while you had chance to at least hold one. In fact, currently there are some tools that can signal you about the highest likely to churn even during the initial purchase; hence, saving you from the superfluous job from the start.

Conventional way of calculation churn is not totally a “calculation” but a “guess”. That is, without proper tools and methods, a customer’s churn probability may not be known; however, some managers are trusting their guts and opting the possible churners. This method’s false negative error chance (indication of a not churning whereas there is churning) is so high that, efficiency-wise, between not doing the “test” and doing it, there isn’t almost any difference. However, current technological advances proved the optimal way for calculation of churn probability. Use of statistics, data science and machine learning became widespread in the churn probability calculation. Those methods, however, do not solve the problem immediately. In fact, they must be correctly implemented into business data, which is sometimes a problem by itself.

How machine learning can be used to help churn probability calculations is not as straightforward as the conventional methods are. As in everything machine learning is used, a correlation between parameters is sought to distinguish the churners and non-churners. The methods in algorithmics structure vary from collecting historical data to nest different functional forms into each other. Aim of the churn probability can vary also. A simple probability score attained to every current customer or a persona structure to use as a guide to every incoming customer are used in order to best use the churn probability knowledge. For example, in the first model, a company may list every customer’s churn probability scores; thus, may lead offers to highest likely ones to leave. Or, via the second model, a persona guide can be constructed in which it shows personal types that likely to leave in a short time. Via that guide, even during the initial transaction phase an individual’s probability of churn can be calculated helping to take further action immediately from the start.

Churn prediction with machine learning works as follows: A pattern recognition survey is conducted with machine learning to “churned customers of the past”, that way we can teach model on the correlations between data points of customer behavior and product. Then, simultaneously, existing customer’s data is fed to model while output of “happy customers” is also added to algorithm. Thus, machine learning algorithm selects the best predictive model for churn calculation. In fact, managers can, using the model, see the customers at risk of churn and take proactive measures for not losing them. Therefore, companies with large customer base and various offerings may benefit from using the machine learning for churn risk calculation. Types and number of ML models deviates with the customer segmentation results. Revising and adding additional features would help to get better and faster results.

Enhencer, having provided machine learning solutions to numerous firms from different sectors, proudly utilizes its own AutoML platform for calculation of churn risk and more. The best thing about it that you don’t need to know a thing about constructing machine learning models and such. Only thing you need to know is how to interpret them, which is made even easier with data visualization tools of Enhencer!

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