In a globalized and increasingly competitive world, the ability to preserve one's customers is vital to a successful company's business. In fact, several industry studies have shown that acquiring a new customer is on average more expensive than retaining existing customers.
In addition, increase the retention rates of customers seems to affect revenues significantly. The so-called Churn Rate, or churn rate, is one of the main challenges in the marketing field for several business domains, but today businesses have one more ally: Artificial Intelligence.
Making use of Machine Learning and Advanced Analytics, it is possible to employ activities of Churn Prediction preventive to intercept customers at risk of abandonment and define ad hoc retention strategies.
So let us try to understand what the dropout rate consists of and what are the best predictive tools provided by AI to curb this problem.
What is the Churn Rate
Before delving into the topic of Churn Prediction, it is essential to understand exactly what the concept of Churn Rate. It is one of the performance evaluation metrics (KPIs) and symbolizes the churn rate, as a percentage, relative to the existing customer base.
In other words, it represents the number of consumers who leave a service or product in a specific time frame, compared to the overall number of customers in the same time frame. Often even large, industry-leading companies face a very high Churn Rate. This is because there is a tendency to focus on the new clientele, instead of consolidating relationships with existing customers.
So, a low churn rate means that the customer base is very satisfied, and thus it will be possible to devote more time and resources to acquiring new consumers. But how is the Churn Rate calculated?
To obtain the percentage number, it is necessary to choose in advance a time frame over which to perform the calculation. The time frame may be monthly, quarterly, or annually. Then it will be necessary to enter the total number of customers at the beginning of that time window and the number of customers lost at the conclusion of this period. Then it will be sufficient to divide the first value by the second and multiply everything by 100. The number obtained will be the percentage value of the Churn Rate.
What is Churn Prediction and what benefits it offers
The expression Churn Prediction, on the other hand, represents the opportunity to predict what the next Churn Rate may be by performing a Churn Analysis. Churn Rate analysis is therefore a predictive strategy that can be of great help in identifying customers who may leave the brand in a short time.
Churn prediction can be carried out thanks to CRM systems to understand of detail the buying behavior of the target audience. However, there are two foundational elements underlying this activity:
- Factors related properly to the consumer;
- Behavioral factors related to customer actions on the site.
Indeed, the churn rate can be lowered, but to do so, one must understand the reasons why customers leave the brand. Such an analysis is then crucial for so many businesses as it can be a valuable ally in retaining new customers and grasping the reasons why they stay.
Here then are the most important benefits of Churn Prediction strategies:
- Retaining existing customers: identifying customers at risk of dropping out allows companies to take proactive actions to retain these customers, increasing their engagement and maximizing their value over time;
- Delivering a better customer experience: with data analysis, one can identify the reasons why a consumer no longer purchases a certain brand. Such knowledge allows one to solve defects related to the customer experience and to meet the customer's needs more effectively;
- Lowering customer acquisition expenses: as already pointed out, several researches have shown how more costly it is to acquire new customers than to retain those who are already loyal. For this reason, predicting the Churn Rate allows one to focus on retention by offering loyalty programs or better customer service;
- Lower risk of dilution: generic retention offers would risk facilitating customers not really at risk of dropping out with unnecessary benefits and discounts.
The importance of AI for Churn Prediction
Keeping the dropout rate low has a strong relevance for companies, and to predict this value, today we use new tools, including those made available by Artificial Intelligence. Let us see what means are offered by AI:
- Identification of triggering events: AI systems are able to amalgamate historical data to find events that could cause customers to drop out, such as service interruptions, price increases, and communications with customer service. In addition, they can identify behaviors inherent in such changes and perform A/B tests to delineate risk clusters;
- Machine Learning: Machine Learning algorithms are used to sort customers into risk categories based on input variables. All of this is very useful to have an assessment of the real risk of abandonment of a consumer;
- Sentiment analysis: leveraging NLP (Natural Language Processing), AI can analyze the interactions it has had with customers (emails, chats, reviews, phone calls...) to find any reasons for dissatisfaction or dissatisfaction;
- Analyses of the Explicit and Alleged Churn Rate: there are customers who may stop interacting with the brand gradually, without obvious abandonment. By analyzing historical Churn Rate data, AI systems are able to find patterns of behavior based on assumed abandonment;
- Recommendation for the next best action (NBA): there are AI algorithms that offer recommendations on actions a company should take to save at-risk customers. Such actions can be messages, personalized offers, and other strategies based on information about the customer;
In conclusion, combating Churn Rate is a daunting challenge that every brand unfortunately faces. Through careful Churn Prediction, companies will be able to more easily predict and contain the churn rate, especially by making use of AI and CRM tools.
Success in counteracting the Churn Rate lies essentially in a multifactorial approach that will be critical to capture the triggers of abandonment and anticipate customer behaviors so as to build stronger and longer-lasting relationships.