Churn Prediction

September 2022

410 Words, 3 Minutes

Churn Prediction

“When they have to give something up, they are hurt more than they are pleased if they acquire the very same thing.” Articulated precisely by Richard Thaler, the author of Nudge. Telecommunication companies are becoming hot cakes. The advent of fame, yields in the brumous frost of contention.

What is Churn Prediction ?

In business, churn is defined as “when a customer cancels a membership to a provider they use regularly. People cancelling app memberships are a common example. So, predicated on their usage of the service, Churn Prediction predicts which customers are most likely to terminate a membership, i.e. ‘leave a company.’ From a business standpoint, obtaining this data is vital even though acquiring new clients is frequently more difficult and costly than retaining existing ones. As a result, the insights obtained from Churn Prediction enables them to concentrate more on the customers who are most likely to leave.

Three vital tactics provide an anchor to stay afloat in this cumbersome corporate market - 1.The first is to bring in new clients, upsell to current clients, or lengthen client retention. 2.Customer retention measures not only how successful a company is at acquiring new customers but also how successful they are at satisfying existing customers. It also increases ROI, boosts loyalty, and brings in new customers. 3.To apply the third strategy, companies have to decrease the potential of customer churn. It is poised to have a variety of potential causes, including the emergence of new rivals that offer better prices or perhaps better services, if done early on, identifying consumers who are likely to quit the business might offer a sizable new income stream.

How to forecast churn for prepaid consumers ?

A proposition on an advanced data mining approach utilising a dataset for call details of 3333 users with 21 characteristics and a dependent churn parameter with two values: Yes/No was done. Among the features were data on the quantity of received and sent messages as well as voicemail information for every client. To minimise the number of data dimensions, a principal component analysis was done. To forecast the churn factor, three machine learning techniques were used - 1.Neural networks 2.Support vector machines 3.Bayes networks.

How can we build a Churn Prediction Model ?

  1. Establish the Business Case - Knowing your desired result from the Ml model is all there is to this phase. The ultimate goal in this situation is: By anticipatorily remaining at the forefront clients, you can reduce customer attrition. Create effective interventions to boost retention

  2. Collect and Clean Data - The following phase is data collecting, which involves identifying the sources of data that will power your churn prediction model. Businesses collect customer data throughout the consumer journey using software like CRM, web analytics, emotion analysis tools, social media listening techniques, and more.

  3. Engineer, Extract, and Select Features - A key step in the database preparation is features engineering, which aids in selecting the characteristics that best describe how customers engage with a service or product.

  4. Build a Predictive Model - Churn prediction is frequently approached by data scientists using a variety of techniques, including classification model, regression models, choice trees, random forests, and many others.

  5. Deploy and Monitor - That once a design has been created, it must be connected with current software or used as the foundation for a fresh software application. The efficiency and precision of the model must be closely monitored.