High investments directed at sophisticated strategies, persuasive texts, and creative campaigns do not always translate into the expected results. This frustration, quite common in the market, is often not related to the quality of execution but rather to a frequently overlooked element: outdated contacts.
More than just a database, a qualified, lively, and reliable contact list is the true foundation of any successful corporate campaign. Ignoring this point can compromise not only the delivery of actions but also the relationship with the target audience and, consequently, the return on investment (ROI).
Managers and decision-makers recognize the value of data as a source for strategic actions. However, having a large volume of information is useless if there is no guarantee that this data is true, valid, and up-to-date. According to a Validity study, 75% of respondents state that approaches based on inaccurate or outdated data lead to customer loss and hinder the actual measurement of action results.
The problem goes beyond incomplete registrations or typing errors. The real bottleneck is the lack of monitoring of the communication journey. Many contacts return to the relationship cycle after a period of inactivity, but through other numbers or channels. Not recognizing this pattern prevents the company from adjusting its actions in real time and compromises the exploitation of opportunities. Without a continuous validation structure and proper integration between the databases and the channels that consume the data, the database quickly becomes outdated — even if it appears to be large.
The lack of governance over data and the absence of mechanisms to prioritize the right number for each CPF generate direct impacts: poorly targeted campaigns, ineffective attempts, wasted budget, operational rework, loss of performance, and in more sensitive cases, strain on the customer relationship due to persistent contacts with incorrect or repeated numbers.
Furthermore, the lack of intelligence about return behavior—how and when the customer re-engages—hinders the development of more effective re-engagement strategies. The result is an operation that attempts, without criteria, multiple numbers for the same CPF, consuming resources, team time, and running legal risks such as violations of LGPD due to inappropriate contacts.
Although it may seem like a complex problem, this scenario can be reversed with methodologies that prioritize the intelligent use of data. The key is to direct efforts toward the validated number, with the highest likelihood of response and aligned with the relationship history with that CPF. Having an enriched, up-to-date, and strategically built database is essential to ensure that the message reaches those who truly matter — the right phone, through the right channel, at the right time.
In this context, technology plays a central role, not only as a tool for point validation but as part of a continuous relationship framework. Models based on inference and behavior, combined with a daily feedback system, make the contact database a living asset — capable of learning from data and continuously improving.
More than identifying if a number is active, it is essential to recognize which contact has the greatest potential to generate results. This means reducing attempts, increasing the effectiveness rate, protecting the brand's image, and offering a more relevant experience to the customer.
The use of data-driven decision layers is what differentiates high-performance operations from those that just broadcast to everyone. Therefore, it is essential for companies to adopt models that not only clean the data but also learn from it. What worked? When did it work? And what could work as new?
Treat the contact database as a strategic asset — integrating external sources, historical behavior, carrier validation, and channel preferences — is the safest way to generate real value. After all, no one likes to be impacted by a communication that doesn't make sense for their moment or profile. Just as excessive attempts harm the customer, they also harm the company.
Avoiding this type of wear requires consistency, intelligence, and structure. A living database is one that evolves over time, adapts to customer behavior, and delivers value both to the sender and the receiver.
