You'll likely be familiar with segmentation already. It's a core part of marketing that involves organizing consumers into groups based on shared characteristics so that you can target them more accurately.

With the help of AI-driven automation, brands can now carry out something called smart segmentation. This involves using AI models to generate and adjust highly targeted segments based on changing patterns and complex data.

This type of automation ensures that your brand's marketing remains responsive to new or emerging segments. Segments can change at any time for a number of different reasons, like a new competitor entering the market or a new social media platform gaining traction.

When this happens, an AI model can detect how these events impact the market or consumer behavior and adjust your consumer segments to reflect these changes. This way, you can ensure that the right people continue to receive the right messages at the right time.

You can also apply AI-powered automation to a mix of different segmentation-related tasks. For example, you can first use categorization to identify new market segments and then use generative AI to produce tailored content that appeals to these new groups.

Imagine that a consumer electronics brand is facing increasing competition, and revenue is suffering as a result. In response, the marketing team decides to target consumers who are the most likely to respond to campaigns about products.

First, the creative team develops multiple campaign images, with each one highlighting a different product feature. Using these images, the marketing team runs an ad campaign targeted at the brand's consumers. During the campaign, the team gathers as much data as possible on the level of engagement each consumer has with each version of the ad.

By running this data through an analytics tool, patterns emerge between engagement, demographics, and lifestyle, but the exact correlation isn't clear. This is where data scientists come in. They train an AI classification model to identify the relationships between consumer demographics, like age and location, and lifestyle factors, like occupation or hobbies.

These relationships are then compared to engagement levels with the new ads, which can be measured by click-throughs or likes. Through offline testing, this model identifies a correlation between consumer age, occupation, commuting distance, and the level of engagement with the ads.

The model suggests that consumers in educational occupations aged 45 and over, with a commute longer than 30 minutes, are more likely to engage with ads focused on tech specifications. On the other hand, consumers with the same age and job, but who have shorter commutes are more responsive to ads focused on fast delivery.

A traditional approach to segmentation, not powered by AI, might view these groups as one segment. It's only through a well-trained AI categorization model that the two distinct groups and their different preferences become clear.

The marketing team then uses this AI model to generate a new set of smart segments focused on these characteristics, which can then be used in future campaigns.

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