A necessary condition for the existence of a business is to have customers willing to pay for a product or service, so much of the efforts of successful companies focus on marketing activities that allow them to maintain long-term relationships with their customers.
It is well known to marketing teams that losing a customer is very costly, as it costs much less to maintain a customer than to acquire a new one. According to marketing guru Philip Kotler it is 7 times less expensive to keep a customer than to acquire a new customer and according to the White House Office of Consumer Affairs, it is 6 to 7 times more expensive to acquire a new customer than to retain an old one.
According to the consulting firm Bain & Company, a 5% increase in customer retention can generate an increase in profits of up to 25%. According to Emmett C. Murphy and Mark A., authors of Leading on the Edge of Chaos, a 2% increase in client retention (or decrease in turnover) is equivalent to a 10% reduction in costs.
That's why detecting customers at risk brings a lot of value after all. Isn't it smarter to invest more in loyalty and less in customer acquisition?
It is common practice to track customer loss and one of the most widely used indicators is the "churn rate", which is the percentage of customers who stop using a company's product or service over a period of time. It is calculated by dividing the number of customers lost over a period of time by the number of customers at the beginning of the period
Some types of churn depending on the business can be:
- Expiration and non-renewal of a service contract
- Cancellation of a subscription
- Use another service provider
Now, what if you could predict when a customer is at risk of dropping out, and in a timely manner you could run retention campaigns (promotions, discounts, personalized emails), since you know which customers to focus on?
This is possible through the application of machine learning algorithms, where you can obtain a list of users with the probability of performing churn, this allows you to establish cut-off points to categorize users according to probability and actively manage them. Additionally, it is possible to detect which factors may be influencing a client to unsubscribe. This can be achieved by analyzing the characteristics that have more weight in the decisions made by customers, which allows to obtain a list of possible points to reduce churn rates.
Companies that implement this type of practice can achieve churn reductions, depending on the sector they belong to, of between 5 - 12%.
Some of the data required to carry out a churn study are
- Customer information: transactions, demographics, usage patterns
- Interaction with marketing initiatives: Reaction to your newsletters, emails, activation campaigns, etc
- Products: Type of product they use, Variety of products, Use of coupons, product references or combos
- Purchase habits: purchase history, purchase frequency, date of last purchase, time of day/season of purchase, purchase amounts, payment methods
- Customer interactions: service questions, store visits / online, complaint resolutions, complaint priority, complaint frequency, communication channel,
Some of the most commonly used algorithms to reduce customer churn are
- XGBoost algorithm
- Random forest
- Neural Networks
“How to Reduce Churn Rate Using Machine Learning?”– Juan Guillermo Gómez Ramírez Tweet