In our webinar, “Your customer database is a revenue engine,” you saw firsthand how a strong data foundation can reveal hidden growth opportunities that most businesses never discover.
In the webinar, we walked through a live customer intelligence report from one of our clients and showed how layering engagement data onto traditional RFM analysis uncovered something remarkable: Over half of their ~250 highest-value customers weren't engaged with their email marketing efforts at all. This kind of disconnect represents hundreds of thousands in potential revenue at risk — until you uncover it in the data.
Whether you're currently using basic email tools and hitting growth limitations, or you have sophisticated software but aren't seeing the results you expected, this methodology will show you why your data foundation determines everything else in your business.
The revenue growth ladder
Everyone wants advanced features: smart automation, dynamic personalization, and complex triggers. But without solid data layers supporting these capabilities, you're just using powerful tools to act on educated guesses.
Each layer of data strengthens what's built above it, turning your customer data into a system that actually drives revenue. Here's how it works:
- Real-time purchase behavior: Understanding who actually buys, how often, and what they spend gives you the baseline data that everything else builds on.
- Engagement insights: Layering how customers interact with your brand reveals who's likely to buy again and how they prefer to be communicated with.
- Segmentation: Combining purchase and engagement data creates actionable customer groups that guide where to focus your marketing efforts.
- Automation: Systematic campaigns that deliver the right message to the right segment at scale.
- Personalization: Content and timing optimization based on demonstrated behavior patterns rather than demographic guesswork.
Step 1: Define "engagement" for your business
Engagement isn’t a one-size-fits-all metric — and trying to force a generic definition can lead you to target the wrong people, at the wrong time, with the wrong message.
Before you can segment or automate anything, you need to define what “engaged” actually looks like for your business. That starts by understanding how your customers naturally behave across two dimensions:
Purchase behavior
Recency parameters: The default 30-day window for defining “recent buyers” works for many businesses, but consider your industry. A cosmetics brand might discover their most active buyers have purchased within a month, whereas a high-end mountain bike retailer may have active buyers who haven’t made a purchase within six months. Their "recent" buyer definition would need to reflect standard purchasing behavior.
Frequency parameters: Similarly, what constitutes "frequent" purchasing varies by business model. A grocery retailer might expect weekly purchases from loyal customers, while a luxury furniture company might see annual purchases as highly frequent behavior. Product lifecycle also matters — subscription businesses expect regular renewals while one-time purchase products might see repeat buyers only when replacement is needed.
Engagement metrics: Your engagement metrics should track behaviors that genuinely signal buying interest for your specific products and customers. This might include email opens and clicks combined with website visits and time on site, or it could extend to social media interactions, product reviews and ratings, or customer service touchpoints. The key is choosing metrics that actually predict future purchasing behavior rather than tracking vanity metrics that look good in reports but don't drive business outcomes.
Brand engagement
This is where you track who’s still listening when they’re not buying. Are they opening emails? Clicking links? Visiting your site or engaging on social? Do they abandon carts or browse product pages?
The key is to define engagement in ways that actually predict buying behavior — not just vanity metrics like open rates or social likes. Choose signals that match your customer journey and product lifecycle.
Once you have the right definitions in place, you're ready to map your audience in a way that reveals where revenue is hiding — and where churn is likely.
Step 2: Calculate your average customer value by segment
Once you've established your engagement framework, map your customers into a two-dimensional grid:
- Vertical axis: RFM segments (Champions, Recent buyers, Need attention, etc.)
- Horizontal axis: Engagement levels (Highly engaged, Engaged, Unengaged)
The vertical axis shows you what customers have actually done with their wallets: how recently they've bought, how often they purchase, and how much they spend. The horizontal axis reveals how they respond to your brand when they’re not purchasing: whether they open emails, visit your site, or ignore you entirely.
For each cell, calculate: Total revenue ÷ Number of customers = Average customer value
This helps you quickly see which groups are driving the most revenue and which ones might be flying under the radar.
For example, if your “Champions + High Engagement” group includes 50 customers who generated $25,000 in total revenue, that’s an average value of $500 per customer.
Compare this across all cells to spot your highest-value, highest-potential combinations — the segments that deserve focused marketing attention.
Step 3: Turn data into action with targeted automation
Now comes the strategic work: reading the customer intelligence map you created to identify where your biggest risks and opportunities lie. It’s important to understand the relationships between purchase behavior and engagement that reveal actionable insights.
Look for disconnects between value and engagement. High-value customers with low engagement represent significant churn risk because they're not staying connected to your brand between purchases. Meanwhile, highly engaged customers with lower purchase frequency might just need the right nudge to increase their buying behavior.
Consider the movement patterns you want to create. Your map shows you not just where customers are, but where they could go. Recent buyers with high engagement are your clearest path to creating new champions. Customers who need attention but remain engaged are telling you they're interested but something is preventing purchase.
Focus on the segments with the highest probability of movement. A highly effective strategy is targeting segments that combine decent purchase history with medium-to-high engagement levels. These customers are already responsive to communications and have demonstrated buying intent, making them the highest-probability targets for increasing purchase frequency and value. Rather than trying to reactivate completely unengaged customers or over-marketing to champions who were already buying regularly, it’s often smart to focus resources where engagement signals suggest customers are receptive to more communication.
Use your customer intelligence map to identify similar high-opportunity segments in your business: those where the combination of purchase behavior and engagement suggests both willingness and ability to move to higher-value categories.
Step 4: Maximize impact with smart personalization
The beauty of layering engagement data is that it allows for precision targeting that goes far beyond inserting someone's name in a subject line. You can customize send frequency based on how often different segments actually engage with your content, tailor product recommendations to actual purchase history rather than demographic assumptions, and adjust messaging tone for different behavioral segments.
Some key personalization strategies to remember include:
- Send frequency optimization based on segment engagement patterns
- Product recommendations driven by actual purchase history
- Message timing aligned with when each segment typically engages
- Content type selection that matches customers’ demonstrated preferences
Most importantly, you can optimize your send times based on when each segment typically engages. Remember: Never send promotional campaigns to unengaged segments, regardless of their purchase history. This damages your sender reputation and reduces inbox placement for all your emails.
The goal is to create marketing experiences that feel relevant and timely rather than generic and intrusive, using the behavioral insights from your customer intelligence map to guide every decision.
The bottom line
Your customer database contains untapped revenue opportunities right now — but most businesses focus on the sophisticated tactics at the top of the ladder without building the proper foundation. By adding engagement as a dimension to your customer analysis, you create a method that shows high-value customers at risk of churn, helps you prioritize marketing efforts for maximum ROI, and lets you design targeted campaigns that move customers into higher-value segments. The companies winning in today's competitive landscape aren't just collecting customer data — they're building it into a foundation that supports scalable, profitable growth.
The methodology isn't complex, but it requires connecting purchase behavior data with engagement insights in a way that most businesses find it difficult to achieve when their data lives in disconnected systems. When you get this foundation right, everything else becomes more effective.
Your next high-performing campaign could already be sitting in your database. All it takes is the right lens to find it.
Watch the full webinar to start mapping your customers across both purchase behavior and engagement levels.
Need the tech to make it happen? Book a demo to speak with our team about Marketing Cloud.