Scale success with AI-driven segmentation

All communication gets easier if you know a little bit about who you’re talking to.

That insight can start conversations, make friends out of strangers, and generally make you the kind of person other people want to get to know.

Digital marketing is no different. The more insight that you have into your audience, the better you can shape your message for them — and give them a reason to listen to you.

But digital marketers don’t typically get to talk to people one-on-one, at least not at first. Instead they have to capture the attention of vast numbers of people, entire demographics even, before they can offer those valuable, personalized moments of interaction that lead to engagements, and conversions.

That’s where customer segmentation comes in. The faster it is to organize your customers into groups, the more effective and efficient personalization becomes in your marketing strategy, and the easier it is to boost customer engagement, customer loyalty, customer retention, and so on.

But segmentation carries its own challenges: audiences grow and change, products change, and brands need a way of keeping pace with the way they organize their audiences so that they can continue to talk to them effectively.

The good news is, in the age of artificial intelligence (AI)-powered digital marketing, we do have a way.

In this post, we’re going to explore how AI helps brands overcome key segmentation challenges, and how Contentful Personalization’s AI capabilities for customer segmentation can supercharge your brand’s segmentation strategy.

What is AI customer segmentation?

Before we explore AI customer segmentation, let's look at the conventional approach to segmentation.

Customer segmentation is the process of organizing customers into distinct groups or categories, based on certain characteristics such as age, gender, geographic location, needs, behaviors, dislikes, and so on.

Following a traditional customer segmentation strategy, brands leverage data collection and analysis to build customer profiles and then assign those profiles to a category. Those categories can be combined as part of a rules-based process to form segments, which brands can use to define their audiences’ digital content experiences personally — for example, showing certain product pages to male customers, and different product pages to female customers.

Essentially, segmentation is a way for brands to make sure that they’re not treating their customers as a vast homogenous blob — but, instead, making the effort to understand them as individuals, working out how and why they’re different, so that they can deliver personalized messages and improve marketing efforts.

There are six broad categories of segmentation, which are based on the following attributes:

Those categories obviously have blurry boundaries, and there may be crossover between them, but they generally serve to help brands shape their voices and their content strategies to appeal to customers as individuals.

And so, for growing brands, customer segmentation scaling is just a question of good data management? Not quite.

Traditional segmentation methods create challenges when brands scale. That's because, not only do growing brands need to account for an increased volume of customer segmentation data, but also for the fact that customers aren’t monolithic — they may change behaviors, move between different segments, or expand to become part of multiple segments.

That diversity creates significant complexity. Customer profiles need to be progressive, new data must be compiled and merged under the same profile, and changes can quickly alter the shape of a segment to the point that it loses its usefulness to digital marketers.

In this environment, conventional segmentation tools can only go so far, and if brands can’t keep up, the effectiveness of customer personalization breaks down, with brands showing audiences irrelevant content.

Introducing AI-powered customer segmentation

AI addresses the data challenges of conventional, rules-based segmentation by giving brands the power to manage their data burdens more efficiently.

Not only does AI enable brands to handle more data via automated algorithmic analysis, but delivers the results of that analysis faster — all of which helps marketing teams keep pace with the real-time changes that specific customer segments typically go through from day to day, week to week, or month to month.

That means brands can be confident that their personalized digital content is always reaching the right people and optimizing engagement, even as they scale up to serve more customers.

But AI provides more than just a segmentation speed boost for growing brands.

The scaling possibilities of AI segmentation

Using AI for customer segmentation offers a range of key advantages, including:

Hidden segments

Given the technology’s capacity to analyze vast amounts of data, AI-powered segmentation can help brands discover “hidden” segments within their customer populations as they grow. In other words, segments that weren’t immediately obvious from a human perspective but that the algorithm spotted while analyzing customer data, and determined are worth personalizing content for.

For example, in a segment defined by customers living in the Pacific Northwest, AI analysis might reveal that men in a narrow age bracket within that segment visit a particular blog post on “The best surfboards for the PNW” — and adjust personalized content suggestions accordingly, showing them surf products that are likely to convert.

Data volume

AI-driven segmentation helps brands deal with increased data volume by maintaining and processing large datasets efficiently.

Rules-based segmentation tools often struggle to define segments with granularity because of the sheer volume of data involved — a challenge that only increases as brands scale. An audience defined by six variables, for example, would necessitate the creation of hundreds of segments.

AI tools can establish those segments much faster than conventional solutions, saving time and money, and increasing the efficiency of the personalized content journey.

Predictive segmentation

AI analysis may also provide a predictive segmentation advantage. For example, based on analysis of customer data inputs (such as purchase activity), and on wider global trends, AI tools could predict that segment “A” may spend more at a certain time of the year, or that segment “B” represents a high probability of churn.

Brands can use this predictive insight to create segments that will become relevant in the future, and plan their content strategies accordingly as they scale. Similarly, AI could help brands identify segments that are likely out of date, and that need to be refreshed with new data.

Cost efficiency

The setup costs for AI-powered segmentation may be higher than those associated with conventional solutions, but they lead to greater ROI over time. That’s because AI tools offer ongoing cost efficiency benefits, thanks to their speed, accuracy, and predictive capabilities.

In addition to that direct cost efficiency, AI tools also generate value by identifying and leveraging new, high-value segments as brands scale up their content operations.

AI segmentation with Contentful

Contentful Personalization’s AI capabilities are built to transform the way you approach customer segmentation and personalization at scale.

Data management

Going beyond the foundational capabilities segmentation, and in addition to harnessing vast amounts of first-party data, Contentful Personalization connects with customer data platforms (CDPs), customer relationship management platforms (CRMs), and other data sources to ensure that brands get as much value as possible from their data when building out customer profiles. In doing so, it maintains the accuracy and impact of segments at scale.

AI Audience Suggestions

The AI Audience Suggestions feature automatically identifies the most valuable customer segments, based on the available data. Contentful Personalization then provides automatic suggestions for personalized experiences or experiments, with variations for each segment based on an understanding of the brand’s content model and voice. For example, a suggested version of personalized copy for men over 40 years old, would be different from the suggestion for women over 20 years old.

AI-native personalization and experimentation

Even as your content operations grow, Contentful Personalization ensures that you don’t need to leave the impact of personalized content to chance.

Our AI capabilities also power built-in personalization and experimentation capabilities, so that you can compare the performance of content within and across segments, and use data-driven insight to further refine digital experiences.

More than a customer segmentation AI

Effective segmentation is predicated on bringing data to bear quickly, accurately, and intuitively.

With that in mind, Contentful Personalization’s AI capabilities enable brands to unlock the true power of segmentation, experimentation, and personalization by supercharging the potential of customer data. Our segmentation tools are accessible for everyone on the marketing and growth teams, and available at the click of a button in the Contentful web app. They’re designed to help users harness data-driven insight in order to contribute to campaigns and optimize customer engagement.

Browse demos of our AI capabilities to explore the possibilities firsthand, or get in touch with our sales team to find out how Contentful can transform your content personalization strategy.

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