The ultimate guide to customer segmentation

A brand’s goal is to connect with customers who may benefit from their products and services, and the task of establishing that engagement typically falls on marketers. In the past, many marketers took a one-size-fits all approach, with generic copy aimed at their entire audience. However, this often resulted in messaging that wasn’t relevant to everyone who saw it.

Today, modern marketers use data to drive decisions. By better understanding their audience, marketers can deliver personalized experiences and solutions tailored to customers’ unique needs.

Brands can leverage data by grouping, or segmenting, similar customers together to increase conversation rates and satisfaction. Using data to group customers is easier said than done, but to help you get started, we’ll guide you through everything you need to know about customer segmentation including what it is, why it’s important, and different customer segmentation models for you to choose from.

Customer segmentation is the process of grouping customers together using a set of common characteristics. By segmenting customers using shared characteristics, brands can deliver highly relevant messaging targeted to each group, resulting in greater marketing effectiveness.

For example, an online sports and outdoor equipment retailer may decide to segment customers based on the activities they’re interested in. Instead of sending all customers a generic email noting there is a storewide sale, the brand may have better success sending personalized emails based on each customers’ unique interests: “Get 30% Off All Basketball Gear” or “Check Out Our Sale On Camping Equipment.”

Segmenting customers helps brands deliver better, more customized experiences; in turn, this can result in a higher likelihood to convert or better customer satisfaction.

You can apply this technique across your entire business: on your website, within your brand community, on social media, and even during customer service interactions in your digital contact center. Anywhere you engage with customers can benefit from tailoring your messaging through segmentation.

And it’s not just about creating relevant content! Here’s how you can benefit from customer segmentation:

• Understand customer wants and needs by testing what content drives the most engagement

• Identify different groups’ likes and dislikes of your customer experience to find areas for improvement

• Determine customers’ preferred channel for interaction to find the best place to reach them

• Find your most important customers and give them extra service to ensure they’re happy

• Upsell and cross-sell products and services based on what similar users purchased

• Improve customer service by understanding and preparing for challenges different groups may be likely to encounter

• Uncover opportunities for new products and services by finding underserved customer groups and developing solutions for them

Customer segmentation is more important now than ever because of the widespread availability of information and solutions online.

In the past, consumers had few options and little information so brands aimed to give them as much as possible and hoped something would be applicable. Department store retailer Sears was known for sending an annual Big Book Catalog featuring hundreds of pages of products. However, over time consumers gained more options and the catalog became a costly expense with decreasing relevance — leading to its discontinuation in 1993.

Today, consumers don’t want to sift through irrelevant information because they can usually find exactly what they’re looking for in just a few clicks. If it’s a product or service, one quick search can yield millions of solutions from brands around the world. If they’re looking for information on that product or service, they can find it with reviews from their peers or other experts.

Because consumers can find what they’re looking for on their own with ease, brands really need to make their marketing efforts count by offering highly relevant, personalized information. This is where segmentation comes in — ensuring that marketing materials are tailored to each person’s unique needs.

Customer segmentation can be done on several levels using one or more models. One way for the online sports and outdoors equipment retailer we mentioned above might be to segment customers based on a sport they’re interested in, or they could take it a step further and segment customers by specific teams they support. Instead of promoting general basketball attire, they might promote team-specific gear based on where customers are from: Lakers jerseys for customers from southern California, Bucks hats for those from Wisconsin.

Generally speaking, there are four different customer segmentation models, and brands can use any combination of them depending on their goals and the data available to them.

Demographic segmentation, the most common form of customer segmentation, focuses on grouping people through non-character traits such as age, gender, and location, among other factors. This information is generally easy for brands to obtain either from customer account info or surveys. It’s also easy to organize and doesn’t require human analysis; thus brands can automate the process using AI and machine learning.

Common demographic segmentation considerations:

• Age

• Gender

• Location

• Income

• Marital Status

As an example of demographic segmentation, consider a luxury clothing boutique. The store may wish to segment customers by location so they can focus their marketing efforts on people who live nearby. They may also wish to focus on gender if they only sell women’s clothes, and could use age to suggest styles that are popular among customers in the same range.

While demographic segmentation provides valuable information, many brands take it a step further and combine it with data from another model to better understand their customers.

Behavioral segmentation aims to help brands understand customer behaviors, specifically how they interact with the brand’s products and services. The information in this model varies from brand-to-brand depending on what they wish to collect. For example, a single segment within this model such as “usage frequency” could refer to several factors, including how often a customer logs in, posts on a community forum, or clicks on a product listing.

Behavioral segmentation considerations:

• Habits

• Actions taken

• Benefits sought

• Usage frequency

• Brand loyalty

Consider a travel booking site seeking to increase their marketing to people they believe are close to making a purchase. They could use behavioral segmentation to identify people who’ve browsed vacation packages more than once in the past week, then email those people an exclusive discount offer to incentivize a purchase. They could also segment people by actions taken to find those who added something to their cart but abandoned it, then add these individuals to a Facebook remarketing or Google Ads campaign with messaging emphasizing a need for urgency.

Psychographic segmentation is similar to behavioral segmentation, but focuses more on understanding customer beliefs and emotional characteristics. While some types of psychographic information can be obtained through surveys, brands may also benefit by using social media listening to better understand their customers.

Psychographic segmentation considerations:

• Personality

• Values

• Interests

• Attitudes

• Lifestyle

Let’s look at a brand that produces water filtration pitchers. They’ve been using demographic segmentation to identify people who live in areas with a high concentration of contaminants in the local tap water. However, over time the brand finds prospects aren’t converting as well as expected.

To better understand why people aren’t converting, they run a survey asking customers about the beliefs and values that influence their buying decisions. The results come back and reveal that this customer segment places a high value on eco-friendliness — an aspect of the brand they don’t emphasize in their marketing. To connect more with the values of its customers, they could focus on their eco-friendly manufacturing practices and how using a water filter is much better for the environment.

Firmographic segmentation is the process of grouping B2B customers based on shared organizational or industry attributes. Unlike the other customer segmentation models which focus on selling to individual consumers, firmographic segmentation focuses on selling to brands and industries as a whole. Firmographic information is typically easy to acquire through public information online, or through people within the industry or organization.

Firmographic segmentation considerations:

• Industry

• Company size

• Organizational structure

• Position in the sales cycle

• Performance

Want an example? Look no further than the menu bar above this post. Khoros uses firmographic segmentation to highlight benefits and offerings for specific industries. Each industry has a dedicated page discussing how Khoros can help brands better serve their customers, with testimonials from other customers within that vertical.

Another example would be brands using firmographic segmentation to group organizations by size. A small organization may not have the resources to serve a global brand, so they’d want to focus on marketing to other small- or mid-sized organizations. But enterprise brands have different priorities and greater resources, so using segmentation to market to other global brands (in addition to smaller ones) might make more sense for them.

Once you have familiarity with the customer segmentation models, you’ll want to create a customer segmentation strategy for your brand. You can do this by following four general steps:

What are your goals with segmenting customers? What do you want to learn about your customers and how will you use that information to better serve them and increase conversions?

Segmentation goals are unique to each brand. While there will be overlap between organizations, such as increasing conversions, brands should also develop a set of unique goals. For example, an airline may want to improve their in-flight experience by pulling feedback from a segment of customers who post suggestions or complaints on social media. The goal of improving in-flight experience is highly relevant to airlines, but not applicable to other industries. When deciding on segmentation goals, start with your brand’s core values and how you can better deliver on your mission statement.

Brands aren’t limited to one segmentation model; they can choose as many segments as they’d like, so long as they can collect and organize the necessary data. Brands may struggle initially by trying to break down customer segments too much, so it’s better to start off with large customer segments and get more granular over time.

You could start by segmenting customers into large groups, such as by state. Then, once your customer base grows, you can get more specific by county or even city, and eventually layer on customer behaviors and beliefs in order to provide more relevant messaging. It’s best to decide on a few segments at the beginning, then add others later as the need arises.

After deciding on customer segments, the next step is collecting and organizing data. How you collect data depends on what information you need. Demographic data can likely be acquired through customer account info, while behavioral and psychographic data may require surveys and the use of social media listening software. The organization of data will also depend on the type of information. Demographic data is easy to organize with a spreadsheet and may require little human analysis, while behavioral and psychographic information may require someone to manually review and categorize. As always, if you’re just starting, it’s best to pick something easy — like state or age group — before ramping up to something a bit more challenging.

The final and most important step in a customer segmentation strategy is analyzing the data to uncover insights and make changes. When analyzing customer segmentation data, look for trends within each customer group to see if there’s something that’s working particularly well or needs improvement. Additionally, make sure to analyze and revise your customer segments at least once a year to ensure you have visibility for your core audience.

The most common challenge with customer segmentation is that brands don’t know what information to collect, how to collect it, or how to organize data in a way that’s helpful for connecting dots and generating insights. Going about the process manually takes considerable resources, so brands can benefit by using AI and machine learning to help automate some of these tasks.

Beyond the collection and organization of customer segmentation data, AI and machine learning can help brands in several ways such as providing suggested responses for customer care agents, customer intent tagging and routing, proactive chat initiation, and automated responses for inbound messages.

Customer segmentation is a great way for brands to deliver personalized experiences for specific groups and individuals, but this is only one part of a well-rounded marketing strategy for an enterprise brand. Khoros has a wide array of solutions to help you make the most of your customer segmentation data.

Connect all of your channels through a unified platform to track and market to segments across channels. Improve your customer care by customizing your AI and agent suggested responses based on communication preferences of different customer segments. For example, some groups may prefer formal language while others will feel more comfortable with more casual messaging during support interactions. Additionally, create an online community to allow like minded customers within segments to connect and engage with each other. Finally, improve your customer experience by aggregating all sources of contact and feedback, then make adjustments based on what specific customer segments like and dislike.

Request a demo to learn how Khoros can help you use customer segmentation data to the full extent.