How to spot signs of disinterest and a drop in consumption—and what to do

In the relationship with consumers, it is not enough to attract customers and make a sale: it is necessary to maintain the attractiveness of the brand, whether through new consumer opportunities, through positive experiences or through the delivery of solutions that solve everyday problems. In this way, the customer's life cycle is extended.

In an ideal scenario, what begins as product experimentation becomes a constant connection with customers, with an increase in the frequency, and intensity of purchases. Brands capable of understanding consumer behavior and adapting to their changes are able to innovate in the development of products and solutions to extend this life cycle and thereby expand customer value over time (LTV).

It is natural that changing customer behavior, competitors' movements, and company strategic decisions make certain Clusters of consumers starting to have more adherence to the business, while others are moving away and ceasing to make up the customer base. It's simply impossible to please the entire audience with the same product, service, or solution.

The use of technology, especially based on the collection and analysis of data with the use of Artificial Intelligence, makes it possible to identify opportunities for consumer reengagement, both to recover lost customers and to avoid Churn. Another important aspect of this journey is to take advantage of these re-engagement opportunities without pressuring the purchase, adding value and showing that deepening the relationship with the brand (or becoming a customer again) is a good decision for the consumer.

Often, the combination of these elements allows brands to reverse negative sales trends, strengthen relationships with customers, and expand their value over time.

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AI: essential to know the customer

Artificial Intelligence has proven to be a powerful tool for predicting consumer behavior trends - and that was long before the buzz generated by Generative AI. The ability to analyze large amounts of data allows AI to identify signs of customer disinterest before this becomes a bigger problem. From then on, taking steps to reverse this disinterest becomes natural for the business.

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Machine learning and data processing

The adoption of machine learning algorithms (Machine learning) allows historical data to be used to identify patterns and correlations in customer behavior - which often results in unobvious cause-and-consequence relationships. Os insights brought in allow companies to adjust their marketing and sales strategies to re-engage customers or add more value to the relationship with the brand, avoiding migration to the competition.

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Natural language processing

The use of natural language processing capabilities allows AI to analyze the feedbacks left by customers in reviews, comments and interactions on the site, customer service and social networks, among other means. Keyword detection and consumer sentiment analysis tools make it possible to identify areas of dissatisfaction that need to be addressed to avoid losing customers.

When to re-engage the customer?

The use of Artificial Intelligence makes it possible to fine-tune the detection of the ideal moment for re-engagement of customers who show signs of disinterest. The analysis of behavioral data such as browsing profile, downtime, and purchase patterns allows us to suggest when, how, and on what channel to contact these customers.

Add value but don't pressure

There is a cartoonish figure in retail: the seller who tries to push products to customers, no matter what the consumer's situation, motivation, or interest. Every cartoon has a grain of truth, which can be as simple as an extra commission for the sale of a certain item or the need to spawn the stock. Be that as it may, pushing products is an approach focused on the interest of the business, not the customer.

As we move on to retail customer-centric, it is necessary to give the consumer the leading role in what they will choose, how, at what moment and from what interaction with the customer. This does not mean passively waiting for a decision to be made: adding value to the customer through the shopping experience, knowledge about the product, and the relationship already established is an approach that generates trust and renews interest in the brand.

Those who “sell without selling” create positive experiences and develop relevant content to meet customer needs, without pressing for a transaction. To do this, it is necessary to know in depth the needs and challenges of customers - open questions and active listening are best practices for sellers, and the analysis of browsing, purchasing and comment data contribute to “listening” to customers in e-commerce.

Another way to “sell without selling” is to offer educational content - and here the analysis of consumers' behavioral data helps to give assertiveness to the production of the content. Use blogs, ebooks, and explanatory videos on social networks and in your e-commerce to address topics of interest, provide tips for using the products, and become part of customers' daily lives outside of transactional moments.

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The role of personalization

One of the great possibilities of gaining efficiency, productivity and assertiveness through Artificial Intelligence lies in personalizing the relationship with customers. Rational aspects, such as free shipping and good prices, are considered as basic aspects to enter the game - to win, it is necessary to offer something more. Study data CX Trends 2025 show that 6 out of 10 consumers are already influenced by the use of hyperpersonalization tools.

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Personalized messages, product recommendations based on purchase history, showcases based on the consumption profile and tailored offers have been perceived by customers as necessary to build good consumer experiences, increasing satisfaction, reinforcing the bond with the brand and bringing an important message: the company understands and values their preferences.

A real-life example

Leroy Merlin, one of the largest retailers in the construction and decoration sector, faced challenges related to e-commerce, such as the high cart abandonment rate and the need to learn more about the behavior of its customers. To solve these problems, the brand adopted the platform CRMBack, whose omnichannel approach integrates physical stores, e-commerce, telesales, WhatsApp and the app.

With this tool, the company was able to collect valuable data about its consumers, allowing it to segment personalized campaigns and increase the conversion rate. In 2023, the implementation of this solution resulted in the recovery of 8,443 abandoned carts and generated an increase of R$ 7.9 million in revenue.

Solutions such as CRMBack are essential for retail, as they transform anonymous users into well-known customers, reducing the rate of Churn and increasing engagement through channels such as WhatsApp, email, and SMS. The personalization of the consumer experience and the automation of interactions are differentials that directly impact billing and loyalty. The Leroy Merlin case demonstrates how the strategic use of data and automation drives sales and optimizes the buying journey.
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Identifying signs of disinterest and a drop in consumption in your brand requires a combination of tools, such as Artificial Intelligence, and strategies that prioritize customer value. By predicting changes in consumer behavior and offering personalized and relevant experiences, companies can reengage disinterested customers and strengthen their position in the market.

To achieve superior results, it is necessary to act proactively, keeping the focus on the customer's needs and expectations. In this way, it is possible not only to reverse negative trends, but also to build lasting and profitable relationships with consumers.