How Do You Make Churn Analysis?
Ensuring satisfied customers and keeping retention rates strong even from beginning with correct churn analysis.
The conventional methods of keeping sales high consist of multiple actions that are mostly originating from the same place; gut of campaign holder. Although this method holds for decades, global spread of the companies caused them to run into a lot of customers at once. That is, the potential of the companies is exponentially rising in terms of their customers. Therefore, every other person’s action must be correctly predicted as the overall success of the companies are solely depending on the gaining customers and not losing customers in the given timespan. The analysis of expected customer leave rate is one of the most – if not the most beneficial analysis for the companies as cost of gaining new customers is higher than retaining existing ones.
The churn analysis is a vital step in order to build a sustainable business of any form. The focus on the reducing churn rates would be in-line with the latest patterns of customer behavior. Moreover, in order to reduce churn rates and from a feasible model of churn analysis, companies need to have latest tool that the technology offers. That is, having been in-line with the information technology’s latest offerings such as machine learning and deep learning, guarantees the sustainable success of the said company.
Customer churn analysis refers to statistical modelling that points out the customer attrition rate in a company. Customer attrition, if calculated by conventional methods, is straightforward as it gets. The ratio of customers using the product that are not inclined to quit the usage in a timespan is customer attrition rate. Although it is easy to calculate, when it comes to churn rate calculation, things get a little complex. In conventional methods only tool you have is to get and insight from the past data of quitted customers. This way you can get a churn rate of past terms. However, in order to have a successful strategy, one must act for one step forward. Implementing effective strategies requires identifying the cause of churn; thus, a good modelling must be pursued for predictions.
Benefits of churn analysis are hidden in their implications. That is, the correct use of it produces an effective strategical power. In its nature, churn analysis when combined with the prediction itself, is a classification tool. Looking at past behavioral data and clustering every common action yield user activity pattern, where outliers and potential leavers can be sorted out. In the pursue of high attrition rates, having a well-behaving model of predictive churn analytics gives you to comb out the loyal customers and to detect the ones that are likely to churn.
All of those can be done with conventional methods until a certain point. for example, you can see that part of your customers are leaving after a certain time. However, catching the correct patterns is hard when you have thousand of customers. Accurate use of machine learning algorithms (in our case: automated machine learning algorithms), can easily form clusters of data (customers), then it can sort out the valuable information of customers’ churn rate. After a correct application even during a new sale, churn probability of customer can be seen. Moreover, with enough information, you can calculate the highest churn risk among the customers groups; thus, a tailored offer can be made to them where your probable lost turns into profits.