Better marketing starts after the results arrive Clio

A campaign starts. The results are coming. They are good; better than expected. Everyone celebrates (as they should), the numbers are entered into a report or dashboard, and the team moves on to the next campaign.

Or maybe the results are not good. There’s discussion about what might have gone wrong, the numbers continue to find their way into a report or dashboard, and the team moves on.

Do you see the problem? In both cases a fundamental step is missing: why?

Why did this campaign perform better? Why wasn’t that up to par? What could explain the result? And, perhaps more importantly, what might we do next to find out whether our explanation is right?

You don’t need an advanced degree in statistics to ask these questions, nor do you need the latest martech platform or AI tool. You need curiosity, one of the most underrated skills in marketing.

We talk a lot about marketing skills

Marketing has many skills to master: analytics, automation, SEO, paid media, email, content, social media, conversion optimization, and now artificial intelligence. Technical skills matter, a lot.

But being able to run a campaign is not the same as being able to improve one. The tools can tell you what happened, help you run the next campaign, and even surface patterns or recommendations. But someone still has to ask: why did this happen? What else could explain it? What should we test next?

This is the difference between routine campaign execution and continuous optimization. Execution gets the campaign out the door; curiosity makes the next campaign better. And as the tools we use become more sophisticated, I would argue that curiosity is becoming more important, not less.

Curiosity is what turns data into insights

Marketers have access to more data than ever before. Opens, clicks, conversions, traffic, engagement, revenue, attribution… there’s no shortage of numbers to look at. But the data tells us what happened. Intuition begins when we are curious about why it happened.

Let’s say the conversion rate drops by 18%. It’s useful information, but it’s not in-depth information. A curious marketer begins to investigate: has conversion decreased across the board or just for certain audiences? Has the quality of traffic changed? Has there been a change in the landing page or offer?

This is how curiosity turns reporting into optimization. We start with an observation, ask what might have caused it, develop a hypothesis, and test it. Then we look at what we’ve learned and use what we’ve learned to shape the next question.

Observation → Question → Hypothesis → Test → Learning → Next question

There is no real end point to this process, and that’s a good thing. Optimization is about continuously learning what motivates your audience and using that knowledge to improve results. The value isn’t simply in having the data. It’s about being curious enough to act accordingly.

Don’t just investigate what went wrong

When results disappoint, curiosity often kicks in naturally. We want to know what happened so we can fix the problem. But positive results deserve the same scrutiny.

If a campaign exceeds expectations by 40%, it’s time to start asking questions: Why did it work so well? Was it the offer, the audience, the creativity or the timing? Has anything changed in the market? Or was it simply an anomaly?

If we don’t understand why something worked, we can’t confidently reproduce the result. Curiosity isn’t just about problem solving; it’s also about understanding success well enough to leverage it. Some of your best optimization opportunities may be hiding in the campaign you already consider successful.

Curiosity prevents best practices from becoming bad habits

Marketing is full of received wisdom called best practices. Keep forms as short as possible. Place your main call to action above the fold. Limit the number of calls to action on a landing page. Follow the recommended posting frequency.

Best practices can be useful starting points, but don’t treat them as universal truths. They ask a few more questions: better for whom? Under what circumstances? Based on whose data?

What works for one audience, brand or channel may not work as well for another. Benchmarks can provide context, but they can’t tell you exactly how your audience will respond.

I’m a big believer in testing with your audience whenever you can. Use best practices to generate ideas, not close questions. If conventional wisdom says one approach should work better, try it. Win or lose, you’ll learn something more useful than the best practices themselves.

Curiosity requires being comfortable with being wrong

There’s another aspect of curiosity that marketers don’t always talk about: You have to be willing to discover that your hypothesis was wrong.

It’s harder than it seems. We become attached to our ideas. We want the campaign we recommended to work well, the test cell we supported to pass scrutiny, and the data to confirm our beliefs.

But a hypothesis is not something to be defended; It’s something to investigate. If the test disproves it, that doesn’t mean the test has failed. It means you learned something.

Some of the most useful marketing lessons come from results we didn’t expect. A “losing” test can challenge an assumption about your audience, eliminate an idea that isn’t worth pursuing, or point you towards a better question.

Curiosity shifts the goal from proving you’re right to finding out what’s true. And “I don’t know, let’s find out” can be a remarkably productive starting point for a marketer.

Create curiosity in the process

Telling marketers to “be more curious” isn’t particularly helpful. Curiosity becomes useful when you integrate it into how campaigns and tests are examined.

To do this, ask these five questions after every significant campaign or test:

1. What happened? Let’s start from the facts and not from explanations.
2. Why do we think this happened? Separate what you know from what you take for granted.
3. What surprised us? Unexpected results are often where the most useful learning begins.
4. What hypothesis does it suggest? Turn questions into something you can investigate.
5. What should we test next? Don’t let the learning stop with the campaign report.

Make these questions part of campaign summaries, test briefs, or periodic performance reviews. The goal is to make curiosity a habit rather than something that emerges only when outcomes are unusually good or bad.

Over time, that habit changes the conversation. Instead of just reporting on performance, the team starts looking at what the results can teach them and what they should do differently as a result.

Curiosity is a competitive advantage

Most marketers have access to many of the same tools, platforms, and, increasingly, the same AI capabilities. The benefit is not access. It’s what you do with it.

Curious marketers continue to learn. They question assumptions, investigate unexpected results, test ideas, and use what they learn to improve the next campaign. Over time, these improvements add up.

So the next time you look at the results of your marketing, don’t stop to wonder if they were good or bad. Ask why. Ask what else might explain them. Then find out how you can test it.

Technical skills will help you execute the marketing you have today. Curiosity is what will make tomorrow’s marketing better.

Stay curious. Keep testing. Keep learning.

Author’s note

I used ChatGPT as a thought partner in developing this article, including helping me organize the structure, test ideas, and narrow down the draft. The point of view, examples, conclusions and final text are mine and I reviewed and edited everything before publication.

Transparency around the use of AI is important. I’ve written about it before a practical framework for AI dissemination in marketing and I try to follow the same principle in my work. If AI had a significant role in content creation, readers should know about it.