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Customer Segmentation Models: A Better Approach for 2022

April 25, 2022

In this article you will learn:

Think about your lifestyle, needs, and interests. Now, think about the same for others, like your friends or family. Even though you may share a few common traits or interests, odds are you're not the exact same in every way, even with those people whom you are closest to.

And because you aren’t the exact same, you wouldn’t expect brands to use an identical customer segmentation strategy and communicate with you the same way just because you have a few shared traits or interests.

For example, you may shop at the same local grocery store as your sister, but you’re a vegan and your sister is not. The store sending you both an offer for 5% off deli meat purchases would be unproductive to them and irrelevant to you. Just because you fall into the same customer segmentation demographic of shopping at the same store location, does not mean you and your sister have the shopping habits or preferences.

Consumers that make up a brand's customer base all have various needs, pain points, and expectations from a brand in terms of communication, support, and relevance. However, many marketers stop short with their customer segmentation strategy, thinking it's sufficient.

In other words, they don’t ever move beyond basic customer segmentation models, to tap into the true power of personalization in their messaging or offers, and provide customers experiences based on their unique needs, desires, and preferences. Without deepening these relationships and showing customers you (the brand) “get them” as buyers, they’re left feeling undervalued and turn their attention and wallet share elsewhere.

With customers expecting brands to provide a true 1:1 level of personalization, the brands who don’t focus on providing that within a modern customer segmentation strategy, will soon be missing out on a portion of a $70 billion pie.

What is Customer Segmentation?

Customer segmentation is a way to split customers into groups (customer segments) based on certain shared characteristics. All customers share the common need for your product or service; beyond that, they possess distinct demographic differences (i.e., age, gender) and tend to have additional socio-economic, lifestyle, or other behavioral differences that offer companies useful marketing insights that they can use to deepen customer relationships.

In this article you will learn:

  • Why Segmentation is Important
  • Why Basic Segmentation isn’t Enough
  • Types of Customer Segmentation Model
  • The Right Tools to Utilize for Segmentation

Why Customer Segmentation is Important

Customer segmentation is important because it allows brands to understand their target audience more clearly, and create customization. Targeting an entire mass audience is not an effective marketing strategy, because buyers interact and purchase differently.

In hopes of better understanding their target audiences, many brands have implemented various customer segmentation tools and models to increase customer loyalty and ultimately drive revenue. However, in the customer-centric world of commerce that we live in today, consumers demand a level of personalization that segmentation alone can’t solve.

According to McKinsey & Company, 75% of US consumers changed their shopping behavior and shifted to new brands during the COVID-19 pandemic, despite the fact that most brands were using some level of segmentation to engage customers.

In many cases, techniques such as micro-segmentation, which categorizes customers based on a complex set of attributes, and smart segments, which use AI tools to examine available data and use that to decide how to divide customers, can create unnecessary complexities for loyalty marketers and confusion for brands.

These customer segmentation practices did not provide customers with what they were looking for- personalization, engagement, and relevancy, so consumers went looking for brands that could connect with them at a deeper level.

Instead, brands are better served by creating large customer audiences to organize campaigns and support broader corporate objectives. By clearly defining large audiences segments such as net-new customers, brand advocates, and lapsed customers, marketers and business leaders can focus on long-term strategies to improve business results and design programs that help achieve their corporate objectives.

While customer segmentation is important, brands need to understand that times have changed and buyers shopping habits and expectations have changed. Any unmodernized strategies will no longer be a key driver for long-term customer retention and revenue.

Why Basic Customer Segmentation is Not Enough

Customer segmentation only takes a company so far—because it does not allow marketers to target customers as individuals and provide them with offers specific to their individual needs. While breaking a customer base into smaller groups could enable marketers to identify segments that they are not already reaching, there are still variations between consumers within given customer segments, so it's never a truly personalized experience.

The best way to target one person in the segment may be different than the best way to target another person within the same segment. And knowing what motivates a consumer to purchase a product or service can help companies position their products more appropriately.

To have a truly effective commerce strategy, brands must realize that customers are not fixed and their shopping behaviors can not be predicted based on customer segments alone. Their shopping patterns ebb and flow to match their lifestyles- which are regardly changing. It’s all about knowing your customers, meeting them where they are at, and guiding them along their journey with your brand in a way that feels personal and relevant to them.

Customers are now craving a personalized, engaging, and relevant experience, with 79% of consumers agreeing that the more personalization tactics a brand uses, the more loyal they are to that brand. And 73% say they feel more inclined to shop at brands that make their loyalty experience fun and rewarding.

What’s the Difference Between Segmentation and Personalization?

Segmentation is a solid foundation, but it doesn’t offer all you need to develop personalized offerings and build strong relationships and loyalty with today’s customers.

Segmentation refers to grouping customers based on demographics, geolocation, or other broad characteristics. Again, it’s a great foundation to work with customer data. But personalization is how marketers target and tailor messaging and offers to each individual customer based on their unique desires, motivations, and needs.

Customer segmentation models offer a very general view of the customer but don’t help you achieve personalization in your loyalty offers or messaging. Many brands are stuck using basic customer segmentation and thus limit their capacity to develop deeper, more profitable relationships with customers.

Let’s look at various customer segmentation models to understand precisely what they are, why they stop short of providing the value of true 1:1 personalization in your marketing efforts, and how adding machine learning to customer segmentation can help brands achieve personalization.

Information Used in Customer Segmentation

You can create customer segments by using any information available about them. Direct-to-consumer brands and B2B companies are at a distinct advantage because of the amount of customer information they can obtain about their customers just from their transaction data alone.

Basic data used for customer segmentation typically include:

  • Geography (culled from billing, shipping or browser information)
  • Products or services purchased
  • How customers found you (referring URL and/or campaign info, promo codes)
  • Device used by the customer, including device type, brand (if mobile) or browser
  • If this is a customer’s first purchase
  • Payment method

Beyond these customer segmentation basics, companies may collect more information during the sales or checkout process to augment their customer data, such as:

  • Reason for purchase
  • Marketing or advertising channel that drove purchase
  • Intended usage of the purchase: business, personal, self-consumption, gift, etc.
  • Company industry segment
  • Job title
  • Age or gender

Types of Customer Segmentation Models

Demographic Segmentation

At minimum, many companies identify basics like gender, job title, or qualities like parental status to create and deliver content based on that customer segment. Companies can gather any such information about customers from purchase details, making direct requests of customers, or by acquiring the data from a third-party.

Behavioral Segmentation

Customers usually make purchases based on needs in their life cycle. For example, customers might buy offerings seasonally or for significant events like moving, getting married, or having a baby. These are all examples of behavioral insights backed by customer segmentation data.

Behavioral data can also include how and when customers make purchases, how they interact with your offerings and how they generally engage with your brand. Marketers need to consider the reasons a customer purchases your product or service and study how those reasons could change over time as customer needs change.

Psychographic Segmentation

Psychographic customer segmentation tends to involve softer measures such as attitudes, beliefs or even personality traits. For example, marketers can craft surveys using questions designed to gauge how customers feel about qualities, attitudes, or perspectives important to their brand.

Technographic Segmentation

Some marketers may categorize customers based on their ownership and use of certain technologies, including the hardware and software they are using, the tools and apps they are engaging with, and the various ways they implement and use different systems and platforms. Using this data for customer segmentation analysis is known as technographic segmentation.

Technographic data can be collected via a number of means including surveys, data scraping, or purchasing existing databases.

Geographic Segmentation

You can segment your audience by their country, state, city, or even town in order to target consumers based on their location. This type of customer segmentation can be useful for both local businesses who are aiming to only reach consumers in their area, as well as larger businesses who wish to target audiences in different areas with unique offers.

  • This type of customer segmentation can be useful for selling seasonal products, especially if you have consumers located in different parts of the world with opposite seasons. For example, if a company is selling a summer beverage, marketers may want to ensure that they are only targeting consumers that are currently in their summer months.
  • People in different communities have different needs. Something that is useful for someone in a more rural area, like gardening supplies, may not have any appeal to city dwellers. Separating consumers by where they live can ensure that marketers are only targeting those that may actually want or need your products or services.
  • Marketers can adjust advertised pricing based on cost of living/average income of the audience they are targeting.
  • You can tailor products and services based on local preferences. For example, certain foods are more popular in some U.S. regions than others.
  • You can use this segmentation to target customers in new areas where you are looking to grow your business by offering incentives specifically to those who live there.

Needs-Based Segmentation

Needs-based customer segmentation focuses on creating customer groups based on their specific needs. To do this, marketers identify the different needs a product or service can fulfill, and create groups based on which customers’ or prospective customers’ have each particular need.

This can also be useful in determining potential overlaps between the various need groups. For example, by analyzing this data, marketers may find that consumers who have Need A also typically have Need B.

Value-Based Segmentation

Marketers may segment customers based on the value they bring to a business. For example, a hotel may group their customers based on how long their typical stays are, how far in advance they book, how recently they booked their last hotel stay, how much they paid for their reservation and what type of room they reserved.

Using this approach, marketers can focus their efforts on reaching the customers that provide the most value, rather than casting a wide net to all potential customers.

Customer Segmentation using Machine Learning

To achieve true 1:1 personalization, marketing teams need to leverage artificial intelligence (AI) and machine learning (ML) algorithms in their customer segmentation strategies.

These advanced technologies allow brands to continually capture and automatically analyze the data of every customer's interaction with your brand. With that analysis, companies can individualize offerings and messages to specific customer segments on a mass scale, with the same automated ML-driven platform.

For example, Starbucks, which has more than 30,000 stores and almost 25 million active rewards members, had the goal of being the most personalized brand globally. The company uses AI-powered solutions in their customer segmentation to continuously learn customer preferences and desires based on purchases and interactions.

The AI and ML capabilities have allowed Starbucks to fine-tune its understanding of what individual customers want and create individualized loyalty offerings at scale. The results were phenomenal: 10 times the marketing operations execution speed and three times the personalized marketing sales lift.

AI automates micro-segmentation, and does it more effectively than any human could, as it understands the customers' segmentation traits down to the individual level. It then improves offers and experiences to understand customers in large business segments, new customers versus lapsed ones.

This approach means loyalty and digital marketers no longer need to manually tweak campaigns in order to achieve incremental improvements. Optimization solutions, powered by machine learning, run in the background so brands and their loyalty marketers can focus on overall strategic program decisions that will deepen emotional connections with consumers.

Use Formation to Optimize Your Segmentation

Formation helps Fortune 500 brands build deeper relationships with every customer using a patented Dynamic Offer Platform.

Formation leverages brands' first-party data to automatically generate each customer's offer with the best action and reward, then manages each offer's deployment, measurement, and fulfillment across the marketing stack.

The future of customer segmentation is taking on a new look. With brands seeing 8X revenue lift, 60% savings in the efficiency of their reward spend more engagement, and 40% increase in category exploration, personalization within your customer segmentation is the secret to success.

Learn how Formation can transform your loyalty program here ->

1:1 personalization is the only way to build trust and loyalty with your customers. Here's why you need to rethink your segmentation strategies. Download our complimentary guide, "Where Segmentation Falls Short"