Many companies are now extending their marketing automation stack by adding AI technologies to predict churn, in order to prevent it using customer retention initiatives.
Most businesses suffer from churn – that is, customers who defect and stop purchasing your products or services.
Most businesses suffer from churn – that is, customers who defect and stop purchasing your products or services.
This is a major problem, as conventional wisdom commonly claims it is about five or six times as expensive to recruit a new customer compared to keeping an existing one. If not more.
Existing customers are also more likely to buy additional products from you. Understanding why customers stop purchasing and assessing the risks are key aspects of a data-driven retention strategy.
Churn prediction may be particularly important to companies offering subscription plans, such as mobile phone operators or cloud software vendors. To them, it is of utmost importance to keep the subscription plans active as long as possible.
Detecting unhappy customers early gives you the opportunity to offer incentives to stay.
Detecting unhappy customers early gives you the opportunity to offer incentives to stay.
In effect, if you know in advance which customers are likely going to abandon you, you can reach out to them ahead of time and try to prevent this from happening.
With AI-driven predictive marketing, it is often possible to detect what factors and patterns indicate a customer is likely to leave.
Dependent on the customer value, AI-driven churn prediction algorithms could trigger the marketing automation system to send a sequence of nurturing emails, possibly with a discount offer to re-engage the customer.
This might be a suitable approach for low-value customers and in the B2C market, but for your best customers in a B2B market, the predictive marketing system might instead be configured to notify a sales representative to make a personal call to the customer and try to rectify the situation before it happens.
Churn can be detected by analyzing factors like:
- Demographic data
- Digital footprint (website/email behavior, etc.)
- Purchase history and payment patterns
- Social media sentiment analysis
- Product usage patterns, if measurable
- Customer support statistics
To detect churn, we simply want to know how likely each customer is to defect.
Classification algorithms like decision trees can be used for this, as they can classify a customer into one of two categories, based on their behavior and that of other customers.
In our case, we interpret the two classes as “likely to defect” and “not likely to defect”. We can then act upon these insights accordingly to keep the customer longer.
Many commercial vendors offer churn prediction tools, for example Optimove, Peak, and DeepSense. Omni-channel customer data platforms (CDPs) benefit from this functionality as well.
Part of predicting customer churn is to learn how to act upon such insights.
Part of predicting customer churn is to learn how to act upon such insights.
Customer retention is the mix of activities you can take to reduce churn and retain as many customers as you can through customer care, purchase incentives, loyalty programs, or other means.
Retention is an ongoing process throughout the lifetime of the customer relationship and should not be bolted on at the end as a solution to an otherwise failing customer experience.
Having said that, retention and loyalty programs may help keep your customers, or reactivate them once they have defected.
