Audience segmentation has been at the forefront of business strategies since its introduction as a standardized concept by Wendel Smith in 1956. Smith proposed it as an alternative to product differentiation, which at the time was a basic way to separate and connect the sale of a group of products with a specific group of buyers. The way consumer trends have evolved since then, along with the emergence of an ever-growing number of products and services, has shifted the focus of marketing teams to the consumer and what they have in common with others who want and need the same things, thereby optimizing the offering. Today, segmentation is a part of the marketing process that must be viewed from a perspective of constant evolution and change. In other words, it must be adjusted according to perceived shifts in the market, almost in real time. Trends are constantly renewed, whether due to the emergence of new stereotypes and technologies or the definition of generational behaviors, such as the now-called Gen Z, which previously didn’t exist in the corporate landscape but now challenges many of the profiles companies once relied on to guide their marketing campaigns. Companies are now encountering unexpected behaviors in how this generation consumes and interacts with brands. 

Due to these constant changes in consumers and the increase in supply and demand, we see the need to integrate new tools into the marketing process, such as automation. Automation aligns closely with elements and concepts that require scalability and constant change, providing an advantage not only when targeting predetermined profiles but also when building these profiles. The process is typically carried out through data intelligence, using information obtained from purchase sessions, browser searches, market studies, and surveys that allow for a more precise understanding of customer preferences. This isn’t just a one-way process; it also involves channels that are open to consumers sharing their future desires for a product or service at their own discretion. For example, a consumer might initiate a conversation with a chatbot, a traditional advisor, or even call the company to inquire about how a specific service is being offered. This information is processed by marketing experts and introduced as a segment or profile through the creation of a Buyer Persona in the automation program. The goal is to integrate and relate it more efficiently with different groups across various marketing campaigns, even simultaneously if necessary. 

Today’s automation tools offer numerous options for segmentation in both B2C and B2B contexts, including demographic, geographic, psychographic, industry type, income, cultural orientation, generational, technological orientation, perceived benefits, behaviors, and more. These tools also allow for real-time processing of this information, enabling the delivery of content to predefined groups through various formats such as emails, SMS, calls, and chats. Users can respond to these, generating real-time updates to their profiles, which simplifies and optimizes the work of the marketing team. Additionally, these tools enable the execution of advanced campaigns based on geolocation, leveraging mobile GPS to target accurate and contextual profiles. 

When Are We Overusing the Power of Automated Segmentation? 

As the level of information we receive from users through various channels grows, and as we continue to refine profiles thanks to the scalability offered by marketing automation tools, we are approaching a fine line between privacy and ethical commercial practices. Today, we see cases where the details are so specific that a user might feel overwhelmed by the realization that a corporation knows them so well that it can almost anticipate offering products and services they were only just considering. This is because the algorithms and marketing teams know that, based on their profile, they will demand these products or services soon. This ethical dilemma becomes even more significant with the recent inclusion of artificial intelligence, which will allow for the interpretation and prediction of consumer behavior and needs with greater precision. While this gives companies more power, it also presents greater challenges in controlling and respecting privacy.