We’ve discussed marketing data science principles before, and now I’d like to walk through the segmentation process in a more structured way. At first glance, segmentation seems straightforward—just categorize prospects based on visible traits. In reality, it’s a nuanced exercise in identifying meaningful groups within your audience.
What is Segmentation?
Segmentation is the process of grouping prospective buyers into smaller, more manageable categories, each defined by shared characteristics or common needs. These groups are likely to respond similarly to specific marketing efforts. While I had been focused on achieving hyper-personalization, I realized that the critical first step is simply identifying who’s truly ready to engage and buy, and who isn’t. From there, it’s about determining what type of customers they might be, and whether they align with the kind of clients you want.
Everything starts with a goal. What do you want to accomplish with segmentation? Which outcome would have the greatest impact? From a marketing and sales perspective, some key objectives include:
- Identifying the highest-potential buyers
- Pinpointing those who can drive the greatest revenue
- Determining which customers will be least resource-intensive
- Finding who is ready to buy now
- Recognizing existing customers who could purchase more
By clarifying your goals upfront, you set the stage for a segmentation process that is both strategic and effective.
Preparing and Organizing Data
If your data is already well-structured and aligned with your objectives, the next steps become simpler. Knowing what you want to achieve helps you pinpoint which data points matter most. My usual approach is to gather all relevant information and arrange it so that each row represents a single individual, resulting in a “wide” dataset packed with attributes.
Because I’ve been involved in cleaning the data, I understand its limitations and can interpret results with caution. For instance, if my model leans too heavily on a handful of variables, I’ll know to adjust the approach or consider alternative models. Trying different modeling techniques is valuable—if one method doesn’t pan out, another might yield better insights.
Begin by gathering your data in a way that’s easy to reference. Beyond basic organization, look for ways to interpret each variable more effectively. For example, if every prospect in a large firm’s database shares a similar flag, can you differentiate them further? Maybe by ranking individuals based on certain criteria or calculating a relative score. Such refinements unlock new levels of personalization and accuracy.
Defining Segmentation
Now it’s time to create clear, actionable segments from your data. While you can slice and dice your audience in countless ways, it’s best to start simply. Yes, one person might fit into multiple segments, but initially, focus on a single, pivotal criterion.
Several approaches can guide you:
- Primary/Secondary Segmentation: Assign a primary segment based on the most important attribute, and note others as secondary.
- Hierarchical Rules: Establish a hierarchy (e.g., prioritize revenue potential over engagement), and classify accordingly.
- Combined Micro-Segments: Combine multiple traits into more granular categories once you’ve mastered the basics.
- Dynamic Personalization: Go beyond fixed segments and personalize your outreach in real time based on behavior and preferences.
For your first attempt, keep it simple. Choose one key data point—perhaps engagement level, product usage, or portfolio size—and create a few broad tiers (e.g., high, medium, low). If your company already has defined categories or performance metrics, start there. The goal is clarity and manageability at this stage.
Suggested Starting Points:
- Start with One Criterion: Pick a single attribute (e.g., asset size) to form basic groups.
- Segment by High-Level Tiers: Limit yourself to a few tiers rather than many sub-segments.
- Use Existing Breakdowns: Align with current business objectives or frameworks.
- Focus on One Goal: Keep your initial segmentation tied to a clear, measurable outcome—like identifying top prospects for an upcoming campaign.
By beginning simply, you set a strong foundation. Over time, you can add complexity and incorporate multiple attributes as your comfort and data sophistication grow.
Analyze and Model
Once you’ve established a foundational framework, leverage analytical techniques and modeling to refine your segments. Two key categories can guide you:
Clustering:
Clustering, often called “auto-classification,” groups prospects into segments based solely on their observed similarities—such as past buying behavior, engagement levels, or product usage patterns. Rather than relying on predefined rules, clustering algorithms discover natural patterns in the data. These emergent segments may uncover opportunities you wouldn’t have identified manually.
Predictive Models:
Building on clustering insights, predictive models help you anticipate how segments might behave in the future. For instance, propensity models predict the likelihood of a desired action—such as making a purchase, responding to an offer, or increasing usage of a service. By analyzing historical data to find patterns that correlate with positive outcomes, these models enable more targeted messaging, product recommendations, and resource allocation.
Propensity Models Explained:
Propensity models use statistical or machine learning techniques to predict the probability of specific actions. Armed with these predictions, your marketing and sales teams can direct their efforts toward the prospects most likely to convert. Over time, this leads to more efficient outreach and better alignment between your products and your customers’ needs.
Create Segments and Personas
Create Segments and Personas
After defining your initial segmentation criteria, you can bring these segments to life by creating personas. Personas give each segment a human face and a relatable story, making it easier for marketing and sales teams to connect with their target groups.
Consider which tiers are most meaningful for your business. Are you focusing on high-growth, high-potential customers, those ripe for cross-sell opportunities, or perhaps those who appear high maintenance but yield low returns? Tailor personas to reflect these strategic priorities.
Try developing three to five personas that represent key segments. Each persona should include:
- Profile: Demographic and behavioral attributes.
- Goal: What the customer aims to achieve.
- Challenge: Obstacles or pain points they face.
- Marketing Tactic: How you’ll address their needs and guide them toward conversion.
Once created, validate these personas. Check with your internal teams for alignment with their real-world experiences, and if possible, gather external feedback from customers. Comparing persona assumptions against actual data—whether from CRM records or sales history—helps ensure accuracy.
Although there is no strict “industry standard” for personas, common frameworks often segment customers by attributes like revenue potential, engagement level, channel preference, or product specialization. By validating and refining personas against qualitative and quantitative insights, you maintain their relevance and practical value.
Prioritize and Align Tactics
With clear segments and personas, the next step is determining where to focus your sales and marketing efforts. Resources are finite, so prioritize those segments and individuals offering the highest return on investment.
- Focus on Top Potential: Identify which segments offer the most significant growth opportunities. Devote more time and personalized strategies to these high-value groups.
- Automate for Lower Priority Leads: For less promising segments, consider automated outreach like periodic newsletters or standardized product updates. This ensures everyone stays engaged, but doesn’t strain your team’s bandwidth.
- Leverage Data to Tailor Communication: Even within a priority segment, individuals differ. Use CRM records, historical buying patterns, and other data points to personalize offers and communication strategies.
By prioritizing segments according to ROI and aligning tactics accordingly, you maintain efficiency and ensure that your highest-potential customers receive the attention they deserve.
Measure and Iterate
Segmentation isn’t a one-time exercise; it’s an ongoing process that evolves with your market and customer behavior. After implementing your segmentation strategy and tactics, monitor key performance indicators (KPIs) to determine if you’re achieving your desired outcomes.
Track Key Metrics:
- Sales Conversion: Are you converting high-potential segments at improved rates?
- Revenue Growth: Are targeted messages contributing to an increase in overall sales or service usage?
- Engagement Levels: Do personalized communications lead to more frequent interactions or product trials?
Refine Your Segmentation:
Use what you learn from these metrics to adjust your segmentation. If a certain persona drives strong results, allocate more resources toward them. If another segment remains unresponsive, consider altering how it’s defined or changing your approach.
Continuous Improvement:
By actively measuring outcomes and revisiting assumptions, you create a feedback loop that strengthens your segmentation over time. As conditions change—whether due to market shifts, product updates, or evolving customer preferences—your segmentation model will adapt, ensuring that your marketing and sales efforts remain both effective and efficient.
Segmentation is a powerful tool that, when approached strategically, can elevate your marketing and sales efforts. By setting clear goals, preparing your data thoughtfully, defining simple yet meaningful criteria, and continuously measuring and refining your approach, you’ll be well on your way to delivering targeted, impactful customer experiences and driving sustainable business growth.