Unlocking Business Potential Through Effective Customer Segmentation
In the previous article, we explored how key performance indicators (KPIs) help track business growth, and highlighted that to truly understand your user base, it's essential to dive deeper into a crucial analytical technique: Customer Segmentation. Segmentation provides a powerful means to understand different groups within your customer base and tailor strategies to their unique needs. Without it, even your best metrics may not reveal the whole picture. In this article, we'll explore the various types of customer segmentation, how to implement them using advanced tools, and practical applications for your business. Here's what you'll find:
Table of Contents
· Why Customer Segmentation Matters · Types of Customer Segmentation · Implementing Segmentation: Algorithms and Tools · Practical Application: Bringing Segmentation to Life · Conclusion
Why Customer Segmentation Matters
You might have thousands of customers, but do you really know who they are? Without segmentation, you could be losing your most valuable customers without even knowing it.
Segmentation allows businesses to go beyond the surface-level trends of customer activity and uncover deeper insights. By splitting your customer base into relevant groups, you gain the ability to focus your efforts on maximizing retention, improving customer satisfaction, and driving sustainable growth.
Types of Customer Segmentation
Customer segmentation can be compared to how one draws a constellation in the sky. Every customer is an individual star but when you start to connect the dots, patterns emerge. These patterns, or segments, would assist you in better understanding your customers, marketing to them, and thus guiding your business.
It's important to understand that just as there are constellations in the sky, there are many ways to slice and dice your customers. Here are a few common types and algorithms that you can use to create the constellations based on the patterns:
- Demographic Segmentation: This is like grouping stars based on their brightness or color. You're categorizing customers based on measurable characteristics like age, gender, income, or geographical location. For example, a fitness apparel brand might create segments for young professionals, middle-aged fitness enthusiasts, and retirees. Each segment receives tailored marketing messages and product recommendations that resonate with their specific needs.
- Behavioral Segmentation: This is like grouping stars based on their movement patterns. This approach involves identifying the behavioral attributes to understand customers and specific segments. Behavioral aspects include responses to campaigns, preferred communication channels or web browsing behaviors. These are people who are coupon lovers, indecisive buyers (browse a lot and often, but rarely buy), prefer one specific channel or rarely respond to any campaign offers. Different companies might want to use different types of behaviors to segment their customer base. For example, an e-commerce company may segment customers who respond to flash sales differently from those who only purchase during holiday discounts. One commonly used and effective method for behavioral segmentation is RFM Segmentation. RFM (Recency, Frequency, Monetary) segmentation groups customers based on their transaction history – how recently, how often and how much did they buy. RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promotions and also for future personalization services.
- Psychographic Segmentation: This is like grouping stars based on their origins or compositions. You're categorizing customers based on their attitudes, interests, or lifestyles. It goes beyond what customers do and focuses on why they do it. A real world example of such application is Nike, which uses psychographic segmentation to reach various customer types. Nike appeals to the feelings and behaviors of athletes by displaying their "Just Do It" slogan that comes closer to the hearts of people who consider sports as a life passion rather than a hobby. Nike's marketing speaks to the belief in pushing boundaries and achieving greatness, something that is appealing to the aspirational, competitive mindset of their customers.
- Value-Based Segmentation: This is like grouping stars based on their importance in the night sky. You're categorizing customers based on their value to your business, such as their lifetime value or potential for growth. It helps a company to identify the groups to focus on acquiring and retaining, allocating marketing dollars, as well as insights like which groups can (or should) be serviced with lower-touch, lower-cost interactions. The differences between value-based market segmentation and other methods are also especially critical for pricing. Most segmentation criteria correlate poorly with the different motivations of different buyer groups to pay higher or lower prices. Three criteria are commonly used in the value based segmentation: current value (e.g. recent 3 months spend), potential value (Customer Lifetime Value) and customer loyalty (churn probability). A good example of value-based segmentation is the amazon prime. Amazon has many customers, but the most loyal customers are those that subscribe to the Prime membership. Another reason is that prime customers have a higher lifetime value as they do not only pay for the subscription but they are also willing to spend more on the platform since they are offered free shipping, access to certain deals, and faster delivery. Amazon's strategy is to continually add value to these customers. The company ensures it adds more and more benefits (such as Prime Video, Prime Day sale, and early access to products) so that the customers keep coming and spending more. For the normal customers, Amazon still makes them interested in the discounts or lower-cost promos but the resources geared for the Prime members are more since they are very valuable.
Implementing Segmentation: Algorithms and Tools
With the right tools, segmentation becomes not only easier but also more actionable. Here are some of the techniques you can use:
- K-Means Clustering: It is one of the most widely used techniques for customer segmentation. This algorithm groups customers based on proximity to each other in data space. It's great for businesses looking for straightforward, manageable clusters. It is a hard clustering technique, meaning one customer can be in only one group and have one label.
- Decision Trees: A predictive model that segments customers based on their characteristics and behavior. This is useful for businesses that want to anticipate future customer actions.
- Gaussian Mixture Models (GMM): Unlike hard clustering methods, GMM allows customers to belong to multiple segments. It recognizes the fact that customers can have multiple interests. One mom can be a big fashion purchaser. While another mom could be an avid tennis player. Therefore k-means clustering technique may put a mom into the mom's group due to her purchases of many baby related products, while ignoring other frequent purchase products. GMM can put two different tags on the moms, main tag being a mom, secondary tag being a fashion purchaser for the first mom and sports lover for the second mom. For marketers, it means that they are able to better tailor to the needs and wants of the customers. Figure 2 illustrates the output from a GMM algorithm. The % is a probability of belonging to a certain group. Again one customer can have multiple tags.
- Social Graphs: Customers are naturally formed into communities. They may not know each other, but they act and behave like how a social network works. Some people are much more influential than others in a community. Detection of these communities and the leader of the community becomes super important because it works the best if you can craft your communications or product strategies specifically for the leader of a community that behaves similarly to the leader. A great real-world example of using social graphs for segmentation comes from Facebook (now Meta). Facebook detects communities within its user base. These are groups of people who frequently interact with each other. For example, a business might want to target a segment of health-conscious users who belong to communities discussing fitness. Facebook can then help the business identify the leaders in those communities and prioritize ads shown to them. Also social graph can help identify individuals who frequently share content that leads to high engagement (likes, shares, comments) among their peers. Facebook segments these users as "influencers" within their social graph. These influencers have a large impact on the opinions and behaviors of their connections. Targeting influencers within social graphs encourages organic sharing. Thus leads to higher conversion rates as trusted voices drive brand recognition.
- Deep Learning-Based Clustering: For businesses with large amounts of high-dimensional data, deep learning can uncover hidden patterns within customer behavior that other techniques might miss. Deep learning algorithms, like Autoencoders or Deep Belief Networks, can handle complex, high-dimensional data that traditional algorithms might struggle with. They can uncover hidden patterns and subtle relationships, like a telescope that can see through clouds and reveal the stars beyond. However, deep learning algorithms are usually not suitable as general-purpose algorithms because they require a very large amount of data.
Practical Application: Bringing Segmentation to Life
After the customers have been classified, the next step is to apply this information for developing marketing messages, advertising campaigns, improving products and services as well as optimizing customers' experience. Here are a few practical ways to get started:
- Targeted Campaigns: Personalize your messages that will be appropriate for each of the target customer groups. For instance, the marketing emails sent to new customers will have a different message to those sent to customers who have been loyal to a company.
- Resource Allocation: Concentrate your efforts where it will be most profitable for you: the best customers. It is possible to provide selected groups of consumers with special services, while at the same time providing less valuable groups with relatively inexpensive solutions.
- Retention Strategies: With the help of identifying segments at risk of churn, you are able to create interventions, such as special offers or engagement e-mails, to bring this customer base back.
Conclusion
Customer segmentation is a very straightforward and logical method of understanding the customers that you interact with. Through segmentation, you are able to define unserved needs, enhance the targeting and guarantee that all activities are based on real and actionable insights.
Customer segmentation is just the first step in understanding your audience. In the upcoming articles, we'll explore how funnel and cohort analysis can further enhance your ability to optimize growth and retention.
Note: Unless otherwise noted, all images are by the author.