Hyper-Segmentation Drives Personalization to Improve Customer Experience

Hyper-Segmentation Drives Personalization to Improve Customer Experience

2020 marks a key milestone for marketers. This year is when it has been predicted that customer experience will overtake price and product as the key brand differentiator. So, is your company prepared for this pivot? And are you taking the steps needed to make sure your customer experience game is strong?

One of the most important things you can do is re-evaluate your strategy and be sure to incorporate personalized marketing to the mix. A focus on hyper-segmentation – or 1:1 personalization – is the key. By delivering messages, offers and promotions that appeal to individual customers, you’ll enhance their experience with your brand, driving greater loyalty and lifetime value, as well as improving effectiveness of marketing and increasing revenue.


What is hyper-segmentation?

Hyper-segmentation goes beyond traditional segmentation, and even micro segmentation. Often referred to a 1:1 personalization or a "segment of one," hyper-segmentation is "an advanced and real-time customization of offerings, content and customer experience at an individual level," according to Cap Gemini.

With hyper segmentation, brands can identify those consumers who have high potential and high propensity to buy, and deliver the right messages and offers at the right time for the desired action or response. Going beyond basic demographics, hyper segmentation considers a wider array of data, including interests and behaviors to understand their unique preferences.


Hyper vs Micro Segmentation

Most companies are familiar with macrosegmentation, and even microsegmentation, but these are just early stages on the way to achieving true 1:1 personalization. Most brands are familiar with macrosegmentation, in which customers are organized into general groups based on basic demographics, like gender, geography or age. This type of segmentation falls short because consumers in these groups still have very different preferences.

Microsegmentation narrows consumers into more specific groups, based on other factors, such as interests and purchase and/or search behaviors. With this information, brands can develop promotions that are even more relevant. Relying on microsegmentation, though, can introduce challenges into the marketing organization, since the manual processes related to building and sending promotions to more groups of individuals is time- and resource-intensive.

Hyper-segmentation goes several steps further to achieve true 1:1 personalization. Instead of looking at "groups" of similar consumers, it uses detailed, real-time data to identify the deals, offers and promotions that would resonate with each individual. At this granularity, brands must rely on artificial intelligence (AI) and machine learning (ML) to access and process large amounts of data from first- and third-party sources. Combining and analyzing this data enables brands to understand each customer better than ever before.


Benefits of hyper-personalization

Both the customer and the brand benefit from hyper-personalization.

About 76% of customers expect companies to understand their needs and expectations – something that is not possible with macro or microsegmentation techniques. With hyper-segmentation, brands can deliver that personal touch, providing relevant offers and more value, and building a trusting two-way relationship that will yield long-term benefits.

By analyzing a multitude of data points, brands can better understand their customers, too. This can help with everything from developing new products and services to increasing sales and revenue. Personalizing a customer’s experience increases their lifetime value (LTV) because they become more loyal and continue to purchase from the brand over time. Statistics show loyal customers are worth 10x more to brands than their initial purchase, and every 5% increase in loyalty can increase the average profit per customer by 25%-100%.


Hyper-Segmentation Best Practices

If you're about to embark on a hyper-segmentation strategy or you need to refresh your marketing efforts, there are several best practices you should consider:

  • Gather detailed customer data: Don't simply rely on demographics. To achieve successful hyper-segmentation, you must collect a lot of customer data. Loyalty programs are a great starting point, since customers willingly provide personal information in exchange for the benefits of belonging. Through loyalty programs, you can also get insights into click-throughs, product purchased, behavior during previous visits, email opens and other interactions with your brand. The more data you can gather, the better your chances are to develop personalized offers that resonate with each customer and lead them toward desired actions.


  • Invest in advanced technology: Hyper-segmentation is not possible with traditional MarTech tools. AI and ML technology must be added to the stack to drive efficiency into the process. The combination of these technologies enables faster offer scaling, so you can develop millions of offer combinations tailored to individual customers.


  • Analyze and fine-tune offers: Hyper-segmentation isn't a "one and done" deal. New and updated data continues to stream in with every customer and market action, and this information must be considered to offers do not become stale. By having AI and ML in the MarTech stack, you will be able to keep learning about the customer’s preferences, and fine-tune offers accordingly.



Hyper-segmentation is a must-have component to marketing plans today, and long into the future, as consumers expect more from their favorite brands. By delivering 1:1 personalization to your customers, you'll be able to deliver a better experience, resulting in a happier customer and greater loyalty, and grow revenues through more sales and a greater lifetime value.

Learn more about the Formation hyper-personalization platform.

Read more about why segmentation falls short:

Why It’s Time to Push Beyond Customer Segmentation Models

The Problems With Customer Segmentation

5 Behavioral Segmentation Examples and How to Improve Them