A Quick Guide To Getting Better At Audience Segmentation

4 minute read
Almost every modern-day marketer today is sold on the benefits of audience segmentation. When done well, you can deliver highly personalized and contextual experiences. And customer experience is the holy grail of contemporary marketing. But how do you make sure you are doing audience segmentation well?

Three Pillars Of Audience Segmentation

The primary task of audience segmentation is to discover clusters in your audience that are unique enough to receive different (personalized) experiences. In my experience, three pillars build the foundation of customer segmentation:


Data is the heart of any kind of personalization you want to deliver. Data decides when you trigger personalization rules and for whom. It is important, then, to make sure your data is clean. Common data problems include incomplete data, invalid data and data duplicity. For example, if you don’t have a unified customer view, data for the same customer from different channels might be recorded as unique entries. Using a CRM that can integrate all your different data sources is one way to solve it.

Similarly, you might miss collecting important contextual information on customers, which can skew your data analysis. This Adobe ad puts forth the point cleverly.

Make sure your first- and third-party data is clean before you even begin to segment your audience.


This is the easy part (relatively speaking) of audience segmentation. Once you have identified customer segments, you need content to deliver a personalized experience to those segments. The more segments you have, the more granular you can be with your personalization. But the catch is, the more granular you go, the more complex it gets, and the more resources you will need.

Therefore, when you are segmenting, you should get an idea of the value a segment will bring in relation to the resources it will take.

Targeting Rules

This is the final stage, where you decide how to trigger custom content for your different segments. There are two ways to go about it: business logic and AI-enabled segmentation.

Let’s dive into each and see how you can optimize your audience segmentation strategy.

Two Methods Of Audience Segmentation

Business logic is the traditional method of audience segmentation. Typically, you rely on intuition, historical data and correlations between historical data to identify customer segments. A/B testing is a big part of this segmentation method, too. A big downside of this method is you are not really using a data-driven approach.

For instance, if you look at enough historical data, you will find some correlation between different events. But correlation does not necessarily equal causation. Just because you have five 40-year-olds as repeat customers for your shampoo does not mean the demographic needs a segment of its own.

Your best bet at utilizing this segmentation methodology is to run A/B tests and identify valuable customer segments.

The AI-enabled approach, also called predictive segmentation, is another way to segment your audience. AI algorithms take the guesswork out of defining customer segments and do it for you based on the customer data you have and the predicted outcomes.

For example, an AI algorithm can segment your audience based on age and browsing history vis-a-vis the prediction of signing up to your email list. Classification algorithms are your best bet here. There are no-code AI platforms with classification algorithms that you can use for predictive audience segmentation.

What’s The Best Approach To Audience Segmentation?

Artificial intelligence has made life easier for analysts and marketers alike. However, the tricky part of audience segmentation is finding the balance between enough granularity and making ROI sense. In my experience, here are a few points you can experiment with to fine-tune your audience segmentation strategy:

• Go beyond basic demographics. Basic demographics can be your building blocks. But to really get value out of audience segmentation, go a little deeper. See what drives your consumers. For example, if you are selling baby formula to new mothers, what are they like in terms of their lifestyle? What are the values that they live by? Are they conservative? What is their typical family size? How often do they dine out? Once you have this deeper data, you can create content that resonates more deeply with them. You can also identify similar audiences based on this deeper data and reach newer potential customers.

• Use a mix of A/B testing and predictive segmentation. Often, a healthy mix of traditional segmentation methods and AI-enabled tools will give you the best results. Identify segments based on A/B testing. Feed these segments and your targeting rules to an AI tool. Let the AI tool predict the probability of success for each segment and the expected ROI. Let the ROI figures guide you on whether a segment is worth keeping or not.

• Break down your cultural segments. Let’s say African Americans are one of the core audience segments for your fitness brand. Instead of treating a customer segment as one large monolith, find out if it can be broken down. For example, maybe African Americans in New York prefer YouTube for their news while those from Colorado have a stronger affinity to podcasts.

The Takeaway: Keep It Iterative

Despite the advancements in AI technology, audience segmentation is not an exact science. The best way to get the most out of it is to keep it iterative. Start small and simple. Rely on A/B testing. Once you have enough segments, include an AI tool in the mix to start curating and fine-tuning your segments. Avoid going too granular from the get-go. Instead, let your resources and ROI be the guideposts for how deep you can go in your segmentation strategy.

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