Why engagement metrics keep fooling us

Start with a metric most teams still lean on hard: Email open rate.

Since Apple introduced Mail Privacy Protection in 2021, Apple Mail pre-loads every email’s tracking pixel the moment it lands, whether the recipient ever looks at it or not. Today, Apple Mail accounts for roughly 58% of all email opens globally. A 2024 Validity study found open rates running 18 to 32 percentage points above what recipients are actually doing. For any email list, “opened” no longer means read.

Even when an engagement metric is accurate, it’s not necessarily helpful.

The National Retail Federation and Happy Returns’ 2025 Retail Returns Landscape report found that Gen Z online shoppers return 7.7 times per year, more than any other generation. And frequent discount buying combined with the high return volume? That’s exactly what an “engaged” customer looks like on a dashboard, and exactly the profile whose real contribution to the business shrinks considerably once those returns are accounted for.

And it gets worse. Harvard Business Review found one company where the top 20% of customers generated 225% of total profit, meaning the remaining 80% were pretty much unprofitable.

So, why do we keep holding onto these KPIs if they, in isolation, say very little? Because they’re the easiest to capture and track.

Every platform has a dashboard with open rate, a click count, and ROAS built in. But lifetime value isn't any harder to measure. It’s just spread across many channels, and you need one platform to connect and analyse them.

What happens when you segment by value, not just activity

Let’s take a look at a brand that does it differently:

Imerco, Denmark’s largest home and kitchenware retailer, ran a Royal Copenhagen email campaign built around four genuinely different customer profiles:

• high-end collectors
• Easter shoppers
• set collectors
• clearance buyers

Rather than sending one campaign shaped around general engagement, each segment received different products, different copy, and different offers, built from what Raptor’s CDP knew about their preferences and value.

Every segment outperformed standard campaign benchmarks on both click-through rate and cost per click. The high-end collector segment alone hit a click rate index of 338.

The take-away is clear: Campaigns targeting based on the full picture – interests and value – perform better at every metric.

Explore the full Imerco case. 

Where else the same blind spot shows up

Once you know to look for it, the pattern repeats across every channel:

• Paid ads: a customer with a strong ROAS on Meta can still have a low CLV if they buy online and return everything in-store, or if the “conversion” the platform sees isn’t the full picture of their value.
  
• In-store: A customer who suddenly starts buying in-store every time will look liked a churned customer in your dashboards if online and offline data is not connected.
• Loyalty and retention programmes: Activity-based tiers can reward “engaged” customers, even if they aren’t exactly profitable.

What a CDP changes about how you segment

A customer data platform doesn’t replace channel-level metrics, but it gives you much more detail and context on which to build campaigns.

Instead of building segments from Meta’s engagement signals or your email platform’s open rates alone, you build them from unified purchase data across every touchpoint: online and offline, one-off and repeat customer, product interest, and demographic data.

In Raptor's CDP, this runs through the CLV Model, an AI model that turns customer data into 12 attributes per customer, recalculated daily:

• Predicted Alive (%) – probability of placing another order, the inverse of churn risk
• Historic value (last 365 days)
• Historic value (all time)
• Predicted future value (next 365 days)
• Predicted Customer Lifetime Value (historic value + predicted future value)
• Predicted number of orders (next 365 days)
• Number of orders (frequency)
• Average order value (monetary value)
• Days since first order
• Days since last order (recency)
• Average days between orders
• Inactivity score (days since last order relative to a customer's normal buying rhythm)

Together, these split your customer base into a simple grid, from high-value loyal customers to high-value customers at risk of churning, to low-value but loyal customers worth nurturing.

With just one click on the grid, you build a new audience: Everyone in the "high-value, at risk" square becomes a win-back campaign. Everyone in "low-value, loyal" becomes an upsell push. No more waiting for an analyst or guessing which customers deserve extra attention.

Marketers at every level can always assess which customers are worth the time and effort.

Get to know the Customer Lifetime Value Model within Raptor's CDP.