Customer Segmentation Models: A Better Approach for 2021

Customer Segmentation Models: A Better Approach for 2021

When customers are passionate about you, they’re more likely to choose you over competitors and stick with you (in good times and bad). If you offer them personalized, individualized messaging and loyalty offers based on each customer’s unique needs, desires and preferences, you can expand relationships with customers, improve loyalty and increase revenue.

Increasingly customers expect 1:1 personalized offerings and messaging from companies. But many marketers stop short with customer segmentation, thinking it's sufficient. In other words, they don’t ever move beyond basic segmentation models to tap the true power of personalization in their messaging or offers.

Segmentation won't get you to 1:1 Personalization. Download the free guide.

In this article you will learn:

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 the process of grouping customers based on demographics, geolocation or other broad characteristics. Again, it’s a great foundation to work with customer data. But personalization (also known as “individualization”) is the means by which marketers target and tailor messaging and offers to individual customers based on their unique desires, motivations and needs.

On their own, customer segmentation models offer a very general view of the customer and don’t help you achieve personalization in your loyalty offers or messaging. That’s why we often refer to segmentation as Stage 1 and Stage 2 of personalization or macro segmentation (1-10 segments) and microsegmentation (10-30 segments) respectively. Many brands are stuck at one of these basic stages and are thus limiting their capacity to develop deeper, more profitable relationships with customers.

To advance to Stage 3 and achieve true 1:1 personalization, marketing teams need to leverage artificial intelligence (AI) and machine learning (ML) algorithms. These advanced technologies allow marketers to continually capture and analyze every interaction a customer has with your brand. Using that analysis, companies can individualize offerings and messages to specific customers, on a mass scale.

For example, Starbucks, which has more than 30,000 stores and almost 19 million active rewards members, aims to become the most personalized brand in the world. The company currently uses Formation’s AI-powered platform to continuously learn customer preferences and desires based on purchases and interactions. Formation’s 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 personalized marketing sales lift.

Segmentation alone doesn’t come close to delivering insights with the same level of sophistication or detail, nor can it deliver targeted, personalized messages to an enormous volume of unique customers.

Let’s take a closer look at customer segmentation so we can understand exactly what it is, how marketers typically use it and why it stops short of providing the value of true 1:1 personalization in your marketing efforts.

What Is Customer Segmentation?

Customer segmentation is a way to split customers into groups based on certain shared characteristics. All customers share the common need of 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.

Customer Information Used in Segmentation

You can segment customers 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 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 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

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Why do many companies think customer segmentation is important?

Companies learned long ago that targeting an entire mass audience was not an effective marketing strategy, so they turned to customer segmentation to better target their consumers. Marketers believed that customer segmentation was a more effective way to reach a consumer with specific needs and wants. However, segmenting customers into smaller groups may not be enough in a world that requires true personalization for marketing efforts to be effective. Let’s take a look at what have traditionally been thought of as the benefits of customer segmentation to better understand why it’s no longer sufficient:

Marketing efficiency: There was a belief that breaking down a larger customer base into smaller segments makes it easier to identify and target a desired audience. However, traditional customer segments are too broad to actually deliver on promises of efficiency.

Allows companies to determine new market opportunities: Breaking a customer base into smaller groups could allow marketers to identify segments that they are not already reaching. While this is true, there are still variations between consumers within any given segment, so 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.

Improved brand strategy: Knowing what motivates a consumer to purchase a product or service can help companies to brand their products more appropriately. But customer segmentation may only take a company so far—they may need to look past this broader breakdown to have a truly effective brand strategy.

Better distribution strategies: Companies need to know where and when customers are purchasing their products and services so that they can better shape their distribution strategies. Unfortunately, traditional customer segmentation is not enough to provide this information on an individual level.

Increased customer retention: An effective marketing strategy will keep customers coming back for more. However, customer segmentation does not allow marketers to target customers as individuals and provide them with offers specific to their individual needs, so it’s not sufficient to build loyalty.

There are a number of other outdated views about why customer segmentation is so important. These include:

It allows a company to learn about their customers on a deeper level. In fact, it only allows a company to make broad generalizations about their customers based on whatever segment they are being sorted into it.

Companies can use segmentation to create campaigns that resonate with customers. As we mentioned, it only helps marketers reach a very basic stage of personalization. True 1:1 personalization is needed to really resonate with consumers.

It improves customer service and customer support efforts. Although there may be commonalities among the issues that customers in a given segment face, these efforts should always be individualized.

Companies can identify their most valuable customers. Customer behaviors are constantly changing and evolving, so it’s difficult to identify “valuable customers” based solely on their belonging to a particular segment.

Types of Customer Segmentation Models: Pros and Cons

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.

Pros of using demographic information for segmentation:

  • Demographic variables are typically easier to collect and measure versus those of other segmentation techniques.
  • Targeting is typically more straightforward when using demographics as a metric—for example, you can target consumer groups, such as college-educated millennials or men between the ages of 35 and 45.
  • Consumer profiles are easy to understand across the board, which makes it easier to develop strategy among departments (sales, customer service, management, etc.).

Cons of using demographic information for segmentation:

  • The model offers limited insight for marketers. Similar demographics among customers do not always imply similar needs, values or motivations within a particular demographic group.
  • Because customers have varying needs, a "one-size-fits-all" approach to consumers based solely on broad demographics may make your marketing message less effective.
  • Skewed or problematic demographic data within a given region can produce unreliable assumptions, potentially reducing the accuracy of your marketing message and methods.

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 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.

Pros of using behavioral data for segmentation:

  • Marketers can build targeted consumer segments based on their responsiveness to certain product categories, promotion types or path-to-purchase preferences.
  • Monitoring and understanding the behavior of consumers online has become easier due to advances in data collection and tracking technologies (cookies, beacons, pixels, etc.).

Cons of using behavioral data for segmentation:

  • While consumer behavior can be tracked, it is not always easy to pinpoint the motivations behind those behaviors with segmentation models, because they can vary greatly from person to person.
  • Behavioral segmentation is often based on complex data constructs that are not always easy to understand without the help of a large team of data scientists and marketers.

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.

Pros of using psychographics for segmentation:

  • Marketers can get some insight into customer motivations.
  • Psychographic segmentation could help brands execute more emotive marketing to highly responsive segments.

Cons of using psychographics for segmentation:

  • Psychographic data has traditionally been more difficult to obtain than other "a priori" data collection methods (demographics, consumer behavior, etc.).
  • Psychographic surveys that are self-reported might be inaccurate.
  • Although marketers can use predictive modeling to create statistical projections and build psychographic insights across customer segments, the accuracy of those predictions depends on the quality of your data.
  • Marketers need clear rules about how to interpret psychographic data to ensure a consistent approach among the individuals or departments that engage in customer segmentation analysis.

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 to analyze customers is known as techonographic segmentation.

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

Pros of using technographic segmentation

  • This type of segmentation gives marketers a better sense of their actual addressable customers and how to reach them. For example, if a customer typically uses their mobile device, you know to reach them there versus a customer who typically engages with a brand via their desktop.
  • Marketers are better able to reach new customers by knowing what technological tools they are engaging with.
  • Can improve marketing campaigns by tailoring it to a customer or potential customer’s technology stack.

Cons of use technographic segmentation

  • It may be difficult to interpret this type of data.
  • Customers who use technology in similar ways may still have a host of other differences, so marketing solely based on this data may not be efficient.

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 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 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 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.

Cons of using geographic segmentation

  • This type of audience segmentation makes the assumption that people who live near each other have similar needs, which is not always the case. This is especially true the wider the geographic area you are targeting is.

Needs-Based Segmentation

This type of 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.

Pros of needs-based segmentation

  • Marketers can tailor messages that specifically target consumers who have a need for your products or services.
  • By continuing to offer must-have products and services to a consumer based on their need, you can foster brand loyalty.
  • You can use this data to improve products and services to better meet customer needs.

Cons of needs-based segmentation

  • Needs can shift and evolve quickly, so it’s necessary to market to consumers at the time of their need; lag time can make this type of segmentation ineffective.

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.

Pros of value-based segmentation

  • Allows companies to price products and services at a price they know the customer is willing to pay based on historical data. This can prevent under- or overcharging when a price is variable depending on the customer.

Cons of value-based segmentation

  • This assumes that the value of a customer will remain consistent over time. However, someone who is “valuable” now may drop off, or vice versa. It’s difficult to predict a customer’s future value based on past behavior.
  • It doesn’t give a full picture of the consumer and what their wants and needs are.

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Challenges of Customer Segmentation

Data quality

One of the biggest issues with customer segmentation is data quality. Many marketing databases aren’t maintained or cleansed regularly, and inaccurate data in source systems will usually result in low quality segmentation.

Data management

Good customer segmentation relies on data being tagged using precise terms and phrases. Many users entering data into systems don’t understand the segmentation definitions and use them incorrectly. Database users must be trained to understand the different customer segments that have been defined, the actual data within the segments that represent the categorizations and when to use the correct customer segmentation for the proper analysis scenarios.

Time drain

It also takes a considerable amount of time to identify a target audience, analyze research data and develop advertising campaigns. Most small companies use market segmentation to determine their target customers. Market segmentation entails developing customer profiles from demographic data. Marketers then determine if their target audiences are large enough from which to earn significant profits. Subsequently, they spend time searching for channels to help them reach their core customer group.

Lack of personalization

Segmentation models (even microsegmentation or more detailed segments of 10 to 30) aren’t personalization. Only personalization can offer uniquely tailored messages and offers that meet the exact needs, preferences and desires of lots of individual customers. Brands that use segmentation are not tailoring marketing to the individual, which is critical in today’s competitive world. And by using a technology solution that leverages artificial intelligence and machine learning, marketers can create messaging and offers in high volume that reflect quality, 1:1 personalization.

The end result? Higher and longer-term customer loyalty, deeper engagement and more purchases that drive revenue and grow your business.

Customer Segmentation and Machine Learning

The approaches described above are marketer-designed segmentation models—parameters are set and examined by marketers themselves. Another approach to customer segmentation is done by machine learning, which saves time and resources. With machine learning segmentation, advanced algorithms and artificial intelligence can be utilized to discover insights and groupings that marketers may not be able to see on their own.

Machine learning also allows for constant refinement of customer segments—it can refine parameters, find new segments and identify specific subsets within larger segments. Utilizing machine learning can better help companies to identify the segments that are outperforming the rest, which can help them to optimize marketing initiatives to meet the needs of those specific high-value customer segments.

Customer segmentation and machine learning examples

There are two types of customer segmentation by machine learning—supervised and unsupervised. In the first instance, a marketer sets the rules and machine learning is used to sort the data based on these rules. For example, you can set parameters to sort customers by number of products ordered, average return rate and/or average spending.

With unsupervised machine learning, an algorithm is used to find different “clusters” based on similarities between customers that might not be so obvious. These clusters are typically very small, so marketers are better able to define super-specific customer groups, which can allow for more personalized offers and better targeting. For example, with the three metrics listed above, unsupervised machine learning was able to determine a cluster of customers that are the most favorable to target—those that ordered the most products, spent the most and had a return rate of zero.

Learn more about the Formation offer optimization platform.