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Churn rate refers to a measure of an employee or customer attrition. It can be defined as the number of customers discounting a service or an employee leaving a company during a particular period of time divided by the average total number of employees or customers over that specified period of time. The churn rate is quite useful in areas where many companies compete, and it becomes easy to transfer from one service to another one.
When alterations in churn rate occur, it provides feedback for the organization that indicates competition, the average length of time an individual remains as a customer, pricing and services. It is a very vital business metric. Sometimes predictive technology is applied for estimating future churn rates and such procedure is referred to as predictive churn modeling.
Voluntary and Involuntary Churn:
Companies classify churn as voluntary and involuntary churns. Voluntary churn and involuntary churn are different. When, a customer makes a decision to switch to another service producer or another company, voluntary churn takes place. Whereas involuntary churn takes place when there occur customer's relocation to a long term care facility, relocation to a distant location, death etc. However, in most of applications, in analytical models, the involuntary reasons for churn are not included. Voluntary churn are the ones where the analyst concentrates, and this is due to the fact of the relationship between the company and the customer that company's control. This relates to how after sales help is provided and how billing interactions can be handled.
For churn modeling, the applications of predictive analytics are used by financial services; the reason for using this is that, it permits customer retention, which is a necessary part of the financial services business model. In business, churn indicates customer's migration and also loss of value. Thus, churn rate indicates the percentage of customers ending relationship with the companies and also, it indicates the customers perceiving the service, but not as much as previous times. For this reason, a huge challenge is faced by companies, the challenges are as follows:
- Reducing Costs and Risks and gaining the efficiency and competitiveness.
- The ability to anticipate to customer abandon for retaining them on time.
There are market advanced tools and applications that are designed for analyzing, in details, the vast data of the companies, and predictions are made based on the information obtained from exploring and analyzing the data.
But, a strong belief was created within business that such offer was limited only to some group of customers. In addition to it, there are no more successful approaches. For this reason, an uplift model was built for identifying a sub segment where, the treatment was found to be effective.
For avoiding "sleeping dog" retention campaigns made an adjustment, where the customer attrition was averted. The gain of campaigns was also achieved through saving of cost of contacting "lost cause".
It may happen that the customers who, are predicted to be cancelled are those, who should be censored from the contact list of retention campaign. Thus, they are prone to depart, and the easiest "sleeping dogs " wakes.
There is a solution to this problem, the solution will be to predict the customers and save those, only if contacted.