How can customer churn be predicted?
Churn is a growth decelerator. It suppresses growth. One can think of it like a leaky bucket.
Predictive churn achieves three goals: understanding the key factors of customers attrition, identifying customers most at risk of leaving, and providing targeted insights on which retention actions should be implemented.
It is important to analyse how and when churn occurs in a customer’s lifetime with your company, and use that data to put into place preemptive measures.
As such, measuring churn, understanding its underlying reasons and being able to anticipate and manage risks associated to customer churn are key areas for continuous increase in business value.
Sales & marketing professionals working on customer loyalty can make good use of predictive churn modelling. However, they are, usually, not data scientists and may not have the required skills in machine learning nor the coding expertise to build models from scratch. Most data handled by these professionals are Small Data. It is a data such as: gender, age, job, number of dependents, monthly charges collected over a few thousand individuals. It is therefore called Small Data in contrast to Big Data which handles millions of Data. Traditional machine learning tools work well with Big Data but do not perform well with predictions from Small Data.
MyDataModels allows sales and marketing experts to build predictive models from Small Data automatically and without training. They can use their collected data directly. There is no need for extra data handling: no normalisation not outlier’s management or feature engineering. After this very limited data preparation, the results from a specific marketing and sales dataset were obtained with a few clicks in less than a minute on a regular laptop.
MyDataModels brings a self-service solution for those who have Small Data and no data scientists.
Most Marketing professionals know that it’s easier (and five times cheaper) to retain existing customers than to acquire new ones. It is generally too late to take retention actions after a customer has left, success rates to retain users are around 2–3%.
Customer service teams in charge of retention tend to have limited resources. Sales & marketing experts can determine which customers are more likely to churn so they can prioritize their retention efforts.
“Predictive model reached an 76% accuracy rate”
In this churn detection use case, the results obtained from MyDataModels’ predictive models are satisfying with 76% accuracy rate on average. This means that in 76% of cases, TADA predicted that a customer was not going to churn and the customer did not churn.
By using an automated machine learning solution like TADA, companies can now proactively identify the factors driving the churn and predict which of the current customers are most likely to leave to competition. This enables retention team to focus their resources on the customers most at risk and offer them personalized incentives to remain loyal.
By targeting the right potential churn audience, this technology offers a great opportunity for companies to lower their retention cost while increasing their overall customer loyalty.