Understanding customers has always been critical to the success of our customers when doing analytics consulting business. Through years of working directly with clients across industries, we’ve learned that every customer has unique needs and priorities. A one-size-fits-all approach rarely works.
So, we wrote this blog to reference our customers in how we do segmentation and a resource for them to reference whenever they need it.
But what’s customer segmentation?
Customer segmentation is the process of dividing customers into groups that share common characteristics.
The goal is to align product offerings, messaging, and marketing to be as relevant as possible for each target customer group.
Here’s what we’ll be talking about in this article:
- Types of customer segmentation
- Steps for doing segmentation analysis
- Real examples of effective segmentation
- Key lessons learned
Let’s start with the basics of what customer segmentation entails.
What is Customer Segmentation?
Customer segmentation provides a framework for separating customers based on attributes like demographics, behaviors, needs, or other properties.
It moves beyond viewing customers as a homogenous group and instead identifies distinct sub-groups that can be targeted individually.
We categorize customer segmentation into four main types:
Demographic segmentation focuses on objective qualities like:
- Education level
- Household status
Grouping customers this way is common because demographic data is readily available. It also can correlate to purchase behaviors. For example, younger customers may prefer lower-cost options compared to older customers closer to retirement.
However, demographics rarely paint a complete picture. You also need to understand customer psychology and actions.
Dividing customers by location can be helpful for regional marketing campaigns or localization strategies. We’ve used geography to group customers when expanding to new domestic and international markets.
Some location-based segments include:
- City type (urban, suburban, rural)
Behavioral segmentation categorizes customers based on how they interact with your business, including:
- Purchase history
- Purchase frequency/recency
- Brand engagement
- Customer service inquiries
- Payment/refund patterns
- Product/feature usage
Reviewing behavioral data provides clearer insight into what customers actually do versus what they say. It can reveal target segments for cross-sell/upsell and retention campaigns.
We closely track the types of analytics services purchased as a key behavioral indicator for creating customer groupings.
Psychographic segmentation groups customers according to psychological attributes like:
- Personality traits
This requires deeper research through surveys, interviews, social listening, and other tools to collect. For example, we conduct an annual customer survey with psychographic questions to understand evolving needs and expectations.
Now that we’ve covered the main customer segmentation categories let’s discuss why it’s so important.
Why Segment Customers?
Customer segmentation supports two critical business capabilities:
1. Targeting high-value specialization opportunities
Not all customers are equally profitable. Segmentation helps you identify which customer groups have the highest lifetime value to focus your limited resources on.
Early on, we wasted effort chasing low-probability prospects from certain industries that ultimately converted at less than 5%. Through segmentation analysis, we discovered a niche segment from the technology sector with over 50% conversion rates.
2. Delivering personalized experiences
Today’s customers expect personalized experiences. Segmentation is the foundation for understanding customers well enough to provide tailored interactions across sales, marketing, service, pricing, products, and more.
For example, with segmentation, we can:
- Craft unique value propositions addressing each group’s priorities
- Recommend the best-fit services for a customer based on their segment
- Incentivize high-value customers differently to promote loyalty
The result is more relevant experiences that foster greater satisfaction and retention.
Now that we’ve covered the immense value of segmentation let’s discuss how to do a step-by-step customer segmentation analysis.
How to Do Customer Segmentation Analysis
Doing customer segmentation analysis well requires an investment of time and strategic thinking. However, the payoff for your business can be tremendous.
Here is a step-by-step process:
Step 1: Define Your Objective
Be very clear about what you want to achieve with segmentation. Common goals include:
- Identifying new marketing opportunities
- Crafting more relevant customer experiences
- Boosting engagement with specific groups
- Increasing share of wallet
- Improving retention or win-back rates
- Enhancing customer lifetime value
- Personalizing sales conversations
- Developing new products/services
- Gaining competitive insights
The objective guides which segmentation model and data inputs make the most sense.
Step 2: Determine Your Segmentation Approach
As discussed earlier, you can segment consumers based on:
- Geographic location
Or combinations thereof. Define which attributes will be most meaningful for your situation. While demographics like age may play a role, additional attributes often add depth to building segments.
Step 3: Collect Customer Data
Leverage data from your CRM system, digital analytics, marketing automation platform, customer service software, and other sources to populate customer records with attributes. Identify gaps where surveys, social media monitoring, or append services could provide more insight.
Mine first-party data you own before exploring third-party data sources. Prioritize recency, accuracy, comprehensiveness, and segment relevance as you assess inputs.
Step 4: Analyze Interrelationships
Examine how customer attributes relate to each other. For example, income level may correlate to purchase frequency, or early adopters may skew younger.
Statistical analysis and data visualization can help spot trends. The goal is to identify distinct patterns that differentiate groups of customers. These become the basis for segment formation.
Step 5: Identify Segments
Synthesize understanding to divide customers into groups with common characteristics. For consumer segments, descriptive names like “Suburban Soccer Moms” often resonate better than “Segment 7.” Develop detailed profiles for each segment.
Start with larger groups first, then subdivide as warranted. Be careful not to end up with too many narrow segments, or they become difficult to manage. Prioritize groups likely to have the most impact or potential.
Step 6: Determine Segment Strategy
Now that you have defined segments build a plan to market to each one effectively. Identify segment-specific strategies related to messaging, lead nurturing, promotions, products, services, channels, content, and more.
These targeted strategies form the basis for superior customer experiences based on a deep understanding of segment needs. Track performance over time to refine.
Step 7: Operationalize Segments
Lastly, embed segments into technology and processes to take ongoing advantage. Within your CRM, marketing automation platform, and other systems, tag records with segment names for easy identification.
Build campaigns, workflows, and business rules tailored to each group. Provide customer-facing teams education and training on the segments to inform day-to-day decisions with insights learned.
Revisit segments periodically to ensure they stay current as customer needs evolve. Segmentation analysis is not a one-and-done project but rather an ongoing discipline.
Customer Segmentation Examples
Here are a few segmentation examples that could give you some insights into how customer segmentation could be used:
Enterprise Software Company
This B2B startup requested customer segmentation support as part of launching a new product line. Their goal was identifying early adopter segments with the highest willingness to buy new innovations coming to market.
They conducted a survey of 300 recent customers assessing interest in trying the new offering. The data revealed three distinct segments:
- Innovators – Actively sought new functionalities and willing to take risks on unproven technology.
- Early Majority – Interested in new features but expected established references and stability first.
- Laggards – Hesitant to adopt new solutions and prone to sticking with the status quo.
Initially, the startup should target Innovators for product feedback and initial traction before expanding to the Early Majority. Laggards ranked second.
This allowed them to conserve resources by acquiring the most accessible segment upfront through niche partnerships in cutting-edge industries. It also informed their roadmap to ensure the initial minimal viable product met Innovators’ expectations.
Specialty Retail Company
A national retailer of eco-friendly consumer goods wanted to improve inconsistent sales across their 200+ locations. They wanted to analyze their customer data to determine if meaningful segments existed needing differentiated support models.
The data included annual customer spend, purchase frequency, products bought, and basic demographics across all stores. Visualizing this data revealed three clusters with distinct behaviors:
- Die-Hard Fans – Shopped 2x per month on average and spent 3x more than other groups. Preferred specialty products.
- Deal Seekers – Visited 3-4x per year, motivated by promotions and clearance items. Average order value was roughly half of the other segments.
- Wanderers – No set pattern. Mixed baskets of staple and specialty items but relatively small spending overall.
We worked together to outline unique customer experiences for each group:
- Die-Hard Fans – VIP access to new product launches and exclusive member-only sales. Assigned personal shopper for high-touch service.
- Deal Seekers – Targeted promotions sent based on previous seasonal purchases. Bonus loyalty points offered.
- Wanderers – On-site recommendations from staff based on recent or related purchases to nurture baskets. Welcome gift for new customers.
The retailer saw a 9% increase in revenue over the next year from this tailored segmentation strategy. The Die-Hard Fan segment also gave them new product innovation insights they had previously overlooked.
Now, let’s switch gears to the lessons we’ve learned over the years.
Key Lessons Learned
Implementing customer segmentation has taught me three key lessons:
1. Segmentation is an iterative process
The best segments evolve as new data emerges. Expect to refine groups to reflect changing needs and behaviors continuously.
For example, we recently split a segment into new sub-segments after quarterly sales data showed diverging conversion rates based on deal size. This uncovered an opportunity to develop higher-touch sales plays tailored to specific deal thresholds.
2. Balance art and science
Customer segmentation leverages data analysis but also relies on human judgment.
The ideal segments balance quantitative evidence and qualitative insights from sales, service, and marketing experts on what makes the most sense for the business.
We always supplement data findings by interviewing front-line teams to validate segments and identify potential gaps in the data. They provide an invaluable perspective on nuances not captured in reports.
3. Focus on actionability
The end goal of segmentation is defining actionable strategies per target segment, not the analysis itself. We’ve learned to clearly communicate insights in business terms and focus our discussions on what we will do differently across priority segments.
For example, when presenting segments, we ground the discussion with questions like:
- Who do we want to reward and retain?
- Who should we upsell first?
- Who needs a more high-touch service model?
- Who should we enhance pricing for?
- Who should we target more aggressively to accelerate growth?
This drives clearer direction to teams on defining segment-specific plays. The segments themselves are means, not the end.
This first-hand look at customer segmentation gives you ideas on how to get started or improve your own segmentation models. The use cases and benefits are immense.
Here are our core recommendations if you’re just getting started with customer segmentation analysis:
1. Start by gathering customer feedback directly
Surveys, interviews, and reviews will provide the richest data on who your customers really are and what they need. Text analytics can help surface hidden insights from open-ended responses.
2. Identify 3-5 meaningful segments to start
Avoid getting overly complex too quickly. Look for broader patterns first that balance uniqueness within each segment and clear differentiation between segments. You can always create more targeted micro-segments later.
3. Build customer experiences aligned to segment needs
Customer segmentation is not just an academic exercise. Use it to tangibly enhance value delivery through tailored offers, messaging, services, and tools per segment.
Prioritize segments representing significant revenue potential and build experiences to maximize their lifetime value.
If you have any other questions on customer segmentation best practices or want to support further optimizing your segmentation models, please reach out! We’re always happy to help companies better understand their customers.