Measure what serves your goal
Not every campaign has the same goal, and not every metric measures the same thing. If you apply a single standard metric to everything, it’s easy to draw the wrong conclusion—a campaign can look good and still fall short of your goal. That sounds obvious, and it is. The question isn’t whether you know this, but whether you have the metrics to actually prove it. Two real-life client scenarios illustrate the benefits of this approach.
Challenge
Everyone knows that a click speaks louder than an open when you want to drive action, and that one good campaign doesn’t make a good season. Yet in practice, people fall back on the numbers that are readily available: the open rate, the conversion rate, and the return on a single campaign. Not out of ignorance, but because the metric that truly represents the objective is often not readily available. You have to be able to calculate it—and for that, you need the underlying data covering the right time period. Without those figures, you’re forced to stick with the obvious.
What It's All About
First, determine what the campaign is meant to achieve, and then make sure you have the metric that measures that goal. An informational email requires a different metric than a call to action; a long-term objective requires a different metric than a single campaign. The trick isn’t in the reasoning—that’s usually obvious—but in the data that allows you to substantiate it rather than just assume it.
Finding 1 · A Belgian fuel supplier
Every marketer knows that the effectiveness of a promotional email should be measured by clicks, not by opens. What’s more interesting is that you can see this here in black and white, based on 29 campaigns (May 2023 – Jan. 2026), categorized as informational (n=23) and promotional/survey/contest (n=6).
| Type | n | Open rate | Click to open | Click-through rate |
|---|---|---|---|---|
| Informative | 23 | 36,1% | 6,6% | 2,4% |
| Promotion/Survey/Contest | 6 | 33,4% | 16,3% | 5,6% |
The open rate differs only slightly between the two types (36.1% vs. 33.4%) and therefore does not distinguish between them. The difference lies exactly where you’d expect it: in the click-to-open rate (2.5× higher: 16.3% vs. 6.6%) and the click-through rate (2.3× higher: 5.6% vs. 2.4%). This finding isn’t surprising—what matters is that you can substantiate it with your own data, so you know which metric to use to guide your campaigns. If you only track the open rate, you’re missing that foundation entirely.
Finding 2 · A Belgian fashion retailer
This client’s objective: to maximize revenue in the long term. The question: How does a shift to fully digital communication affect revenue per customer? Here, too, the intuition is clear—a single campaign says little about an entire season. The point is that you can only see a difference when you can actually compare the two periods side by side. We therefore measured in two ways: short-term (one campaign, shortly after mailing) and long-term (the same top customers, consistently using only digital for an entire season, compared to a combined approach of direct mail and digital).
In Brief — Revenue per customer among top customers, four campaigns, direct mail vs. digital
| Campaign | Direct mail | Digital | Winner |
|---|---|---|---|
| 1 — Denim | €46 | €38 | Direct mail |
| 2 — Basics | €44 | €48 | Digital |
| 3 | €49 | €43 | Direct mail |
| 4 | €43 | €43 | Immediately |
Approximate, as shown in the graph.
Across the entire customer base, the results are so close that there is no clear winner: sometimes direct mail wins, sometimes digital, and sometimes it’s a tie. You can’t tell which way it’s going based on a single campaign—and that’s exactly the pitfall.
Long — Revenue per customer among the top customers, cumulative over two consecutive seasons
| Season | Combined | Fully digital |
|---|---|---|
| Season 1 | €420 | €380 |
| Season 2 | €268 | €233 |
In both seasons, revenue per customer is higher with the combined approach than with the fully digital approach. A separate analysis confirms that this difference becomes more consistent and larger over the course of the season, to the detriment of the fully digital approach—so it is consistent, not coincidental. The cumulative effect is definitely there; you can only see it if you have the data to measure it over the course of the season rather than on a per-campaign basis.
Key Insight
A number alone doesn’t tell you whether a campaign is successful—the objective does. The reasoning behind choosing the right number is usually obvious; what makes the difference is having the data available to actually apply that reasoning—the right number, over the right time period. Without those numbers, you’re stuck with what’s obvious, and while you’re measuring something, you’re not measuring what you wanted to know.
"The point isn't that you have to look beyond the obvious—it's that you have the data to be able to do so."
