Technology and Customer Analysis

Today's technology offers powerful tools for identifying and resolving conflicts, transforming the way organizations interact with their customers.

These tools leverage data analytics to detect patterns of behavior and language that indicate dissatisfaction, enabling a proactive and effective response.

Identification and Resolution Tools

There are several technologies that support this process.

CRM (Customer Relationship Management) systems track customer interaction history and purchasing patterns, making it possible to identify behavioral changes that could precede a conflict, such as a decrease in product usage or a drop in purchases.

Chatbots can analyze customer language in real time for negative keywords, such as "disgusted" or "refund," and alert support teams or transfer the conversation to a human agent.

Meanwhile, NLP (Natural Language Processing) goes beyond chatbots, analyzing Sentiment in email texts, social media, and other platforms to detect negative attitudes.

These tools not only identify the problem, but also help solve it.

Ticketing systems prioritize requests, while self-service portals and generative AI (such as large language models) allow customers to resolve issues on their own or agents to generate personalized responses more efficiently.

Behavioral and Sentiment Analysis

These tools are based on two main types of analytics: behavioral analysis and sentiment analysis.

Behavioral analysis focuses on customer actions, such as their purchase history, engagement level, or frequency of use of a service.

For example, on a software platform, if a user starts logging in less frequently, the system can detect this as a sign of potential churn and alert a team to intervene.

Sentiment analysis, on the other hand, focuses on words written or spoken by the customer, looking for negative emotions and attitudes in their communicati