Churn and Propensity to Churn
The relationship between company and consumer develops far beyond the purchase phase and finds its maximum expression immediately after the customer becomes part of the customer base.
This newly established relationship enjoys many advantages, such as making a profit for the company and satisfying a customer’s need. The most attentive companies do not consider the closing of a sale a milestone, but simply a starting point for increasing customer satisfaction and generating new touchpoints for upselling or cross-selling.
It may happen that the customer is not satisfied with the purchase, that they are unable to perceive the value of the product or service, or that they have been enticed by a competitor’s captivating advertising campaign. In this way, we run the risk of the customer abandoning us, this “danger” is also known as Propensity to Churn.
Churn (the abandonment of a company by a customer) necessarily translates into a loss, also from an economic point of view, since the company will have to start creating a relationship all over again, through expensive advertising channels, sales or marketing activities.
Keeping a customer in a company’s customer base certainly has a lower cost than having to acquire them a second time and helps to enhance the brand, as well as the product marketed by the company: a customer who stays with us is a person who is satisfied with what they have purchased, especially for subscription services or recurring payments.
Anti-Churn
For companies it becomes essential to be able to set up strategies capable of reducing the churn rate which, if uncontrolled, can have serious repercussions both on revenues and on the corporate image, and which are called Anti-Churn strategies.
Before deploying such a powerful tool, we need to start with the development of an Anti-Churn model to understand which subjects are most likely to abandon our customer base.
In statistics and in all those sectors that work with data, the Propensity to Churn is expressed with a numerical score that summarizes the processing of a great deal of information: for example the acquisition channel, the time spent in the customer base, the product /service purchased, the economic value paid, the personal data of the prospect. It is usually an activity developed by data scientists who actively process this data to calculate the propensity to churn score for each customer.
Generate Anti-Churn predictive models quickly and easily
In an increasingly automated and dynamic market, we have received a lot of feedback from our customers, especially from their marketing, CRM and BI departments, looking for a simple and fast tool to automate the generation of a predictive model.
We have worked hard while always maintaining the principles with which our platform is developed: Effectiveness of the Models, Ease of Use and Information Processing Speed.
And today we are happy to announce the inclusion of a new feature in our platform, effectively creating an Artificial Intelligence capable of rapidly processing large quantities of information useful for generating Propensity to Churn models and enabling strategic departments to implement targeted and effective anti-churn strategies.
Starting from a user-friendly interface, which can be used without any knowledge of coding or data science, BigProfiles allows you to predict the probability of each customer’s churn and to set up the most suitable strategies to reduce the abandonment rate and increase Customer Lifetime Value.
This feature joins those already valued and consolidated: prediction of purchase propensity and identification of the debt recovery probability.
Churn and Customer Lifetime Value
Churn consists of diverse types, it could refer to a non-renewal, a cancellation, or an interruption of purchases or use. For this, it is necessary not only to predict the probability of abandonment, but also the Customer Lifetime Value and the length of relationship with the company, to synthesize them in an indicator capable of representing numerically the importance that each customer has for our business.
Is it complicated to apply Artificial Intelligence to an Anti-Churn campaign?
The BigProfiles Platform works in two phases: the first is the learning phase, in which it is necessary to upload the data present in the company CRM and from which Machine Learning will identify the common characteristics of customers who have previously abandoned the service, adding up to 650 statistical indicators, derived from data like the micro-area of residence, for each customer.
The second phase is that of prediction, during which the probability of each customer’s churn is identified on a recurring basis, whether daily or periodically based on the company’s needs. This phase can be fully automated through an API connection or connectors with the CRM.
What are the possible applications of AI in Anti-Churn campaigns?
The new BigProfiles feature dedicated to Retention can enable companies to adopt different data-driven strategies, depending on business needs. Below, we propose four possible applications:
- Premium caring for those clients who are at high risk of churn, such as offering discounts on already active services;
- Targeting high propensity customers the purchase of specific products;
- Retention campaigns focused on customers with an elevated Anti-Churn score;
- Providing levels of premium service for customers with high Customer Lifetime Value.
Do you want to know more or would you like to understand how to apply BigProfiles to your company’s business? Request a free demo!