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 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.
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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.
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.
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:
Beyond these basics, companies may collect more information during the sales or checkout process to augment their customer data, such as:
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.
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:
Cons of using demographic information for 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:
Cons of using behavioral data for 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:
Cons of using psychographics for 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.
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 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.
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.
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.
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.
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.
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.
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.