Back

Why It’s Time to Push Beyond Customer Segmentation Models

May 14, 2020

Why It’s Time to Push Beyond Customer Segmentation Models

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 segmentation, thinking its 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.

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

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.

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.

Learn more about how 1:1 personalized offers can help you create high customer loyalty and earn higher revenues. Download our complimentary white paper, “Why Segmentation Falls Short.”