How Causal AI supports businesses in reducing Churn Rate

Churn rate represents the rate of abandonment by customers of a service or company. It is a critical indicator (generally expressed in percentage terms) that relates the number of customers who have left a brand or service in a given period of time, compared to the total number of customers who have used it within the same time frame.
Recent studies by leading consulting firms have found that an annual Churn rate of between 5% and 7% is acceptable, although these figures differ greatly from market to market. For example, in the energy resale market, the rate of switch between operators following the liberalization of the market has increased exponentially.
Many companies, reasoning in the short term and not to succumb in daily competition, opt for an approach based on the war of price to acquire new market shares and still ensure acceptable volumes of turnover. Promotions and discounts are the most used weapons in the customer acquisition phase, often without considering the risk of compromising the present and future marginality. In this mechanism, focused more on acquisition than loyalty, brands often do not have the tools to report in time how many and which customers are going to turn to a competitor or have already abandoned them and understand the reasons.

In order to be able to better know and satisfy their customers, reducing the risk of abandonment, companies have a valid support in Artificial Intelligence. Through the analysis of the large amount of data acquired by the company-customer interactions, whether it is purchases, sharing of personal data, subscriptions to social channels or newsletters, New technologies allow the extrapolation of useful indications to predict consumer behavior.
Traditional methods based on manual analysis and tools such as the classic Excel sheets or studies carried out by consulting firms, the result of a market photograph at a precise time, lose their effectiveness in a rapidly changing scenario. In order to extract maximum value from the data, it is therefore necessary to use more efficient solutions, refined tools, specific IT skills and the use of innovative approaches.
Software based on Artificial Intelligence, for example, is able to automate the processes of information acquisition and analysis thanks to sophisticated algorithms and provide structured indications translatable into direct actions on customers, adopting an offer determination process for specific customer segments.
It then becomes faster and more effective to analyze the characteristics of customers who in the past have stopped buying to recognize their behaviors and derive useful patterns to identify in the current customer base, those at risk of abandonment. These same systems, in an integrated way, also suggest the best combination of products to offer to stimulate a new purchase, also identifying the most suitable pricing based on the different types of customers and their sensitivity to price.

Among the main outputs that a prescriptive analysis of this kind can provide are: For example, estimates of the likelihood of abandonment for different customer segments and identification of the impact that external factors and not directly under the control of companies may have on this.
Supported by AI tools, management can therefore make increasingly data-based decisions, obtain simulations of the evolution of the Churn rate according to the variation of different parameters of the offer (products and prices), to verify the impact on margins and turnover, and improve business performance.