You know that feeling when you’re staring at a spreadsheet, hoping—just hoping—that somewhere inside those rows and columns is a brilliant insight, ready to leap out and impress your boss?

Yeah. Me too.

Last week, we talked about the moment when I proudly marched into a meeting with a beautifully polished marketing deck—charts, benchmarks, attribution models, the whole circus—and the CEO looked at it, looked at me, and basically said, “Cool… but is this working?”

Which, to be fair, is exactly the question he should be asking.

And it’s exactly the one we should be ready to answer.

But that moment was a gut check. It reminded me—and maybe you—that most of us were taught how to report on data, not how to translate it into meaning, and definitely not how to build a story from it that influences decision-making.

So today, we’re going to fix that.

We’re going to talk about what your data is trying to tell you—because it is saying something. And when you know how to listen, the story starts to come through.

But—and this is important—not all data stories are the same. Some tell you you’re on the right track. Some surprise the heck out of you. Some are low-key shouting, “Danger ahead!” And some are full of potential, just waiting for you to notice.

The Confirmation Story

Let’s start with the four types of data stories you should recognize. The first is the confirmation story.

This is the story your data tells when it’s giving you a high-five. It says, “Yep, that thing you did? Solid move. Keep going.”

Maybe you doubled down on a particular channel, and now it’s reliably delivering high-quality leads. Or you introduced a new nurture sequence and saw your conversion rate tick up just like you predicted. Confirmation stories validate your existing strategy. They tell you you’re on the right path.

They’re reassuring, which is great. 

But here’s the thing—they’re not the end of the story, they’re the very beginning. 

Just because something is working doesn’t mean you stop analyzing. You still need to ask: Should we invest more here? Is there room to scale? Is this still aligned with our overall goals?

Think of confirmation stories as the “You’re not crazy” moments. They’re not flashy, but they build trust. Especially with executives who need to see that you’re making smart bets with their money.

The Revelation Story

Then we have the revelation story, which is my favorite.

This is the plot twist. The surprise. The data equivalent of “wait—what just happened?”

A revelation story happens when your data shows you something you didn’t expect. Maybe a campaign unexpectedly takes off in a market you weren’t targeting. Or a social post you threw together in five minutes is now your top performer for the quarter. 

(Of course it is.)

These stories are pure gold, but they’re also where things get risky—because revelation without action is just trivia. 

You have to dig in and ask: Why did this happen? What’s the signal here? Is this a one-off, or is it pointing to a bigger shift in behavior?

When you bring a revelation story to the table—especially one with a clear recommendation attached—it shows that you’re not just paying attention. You’re adapting. And that is the kind of insight that earns influence.

The Warning Story

Then comes the warning story.

This is the story we don’t want to find—but we need to find.

Warning stories say, “Heads up. Something’s not working. And if you don’t address it, you’re going to have a bigger problem later.”

Maybe your engagement is sliding. Or your best-performing content is starting to plateau. Or your cost-per-lead quietly crept up by 20% while everyone was focused on the rebrand.

These aren’t always dramatic red flags. Sometimes they’re little signals—a trend line that’s bending the wrong way, a funnel stage that’s getting stickier, a metric that used to be reliable but now feels off.

Your job is to spot those shifts early. Not so you can panic—but so you can plan. 

Bring these stories forward with solutions, not just problems. Say, “Here’s what’s happening, here’s what it could mean, and here’s how we get ahead of it.”

Executives don’t need you to be perfect. But they do need you to be proactive.

The Opportunity Story

And finally, the opportunity story. 

This is the fun one. The one where your data turns into a lightbulb over your head and says, “Hey… we could be doing more here.”

Opportunity stories are the ones that say this is working, and we haven’t even maxed it out yet.

Maybe there’s a topic your audience can’t get enough of, and you’ve only scratched the surface. 

Maybe one asset is quietly driving demo requests at 3x the rate of everything else. Maybe there’s a segment you’ve been ignoring that is suddenly showing signs of life.

When you find one of these, don’t just smile and move on. Build the business case. Show the upside. Tie it directly to business goals—revenue, retention, and/or margin. That’s how you go from “Here’s something interesting” to “Here’s something we should absolutely prioritize.”

Data Storytelling Cheat Sheet

To help you remember each of these, I put together a quick cheat sheet: 

  • Confirmation says, “Keep going.”
  • Revelation says, “Rethink this.”
  • Warning says, “Fix this before it’s a problem.”
  • Opportunity says, “Double down.”

Every piece of data you touch tells one of these stories. The key is knowing which one and then using it to drive action.

Because remember: metrics don’t move people. Stories do.

Now that you have a handle on the four types of data stories—confirmation, revelation, warning, and opportunity—you can start to see your metrics for what they really are: signals. Signals of what’s working, what’s changing, what needs attention, and what’s worth chasing.

But recognizing the story is just the first step. The real power comes from what you do with and with what separates the pros from the PowerPointers.

Identifying the insight is one thing. Translating it into a language your executives care about? That’s where the real influence kicks in.

Here is how you move from “Here’s what happened” to “Here’s what it means—and here’s what we should do about it.”

From Activities to Outcomes 

Once you know what kind of story your data is telling—confirmation, revelation, warning, or opportunity—the next step is translating that story into something the business actually cares about.

And I say this with love, “Our click-through rate improved by 14%” is not that thing.

Because what leadership actually wants to know is:

  • Are we making progress toward our goals?
  • What decisions do we need to make right now?
  • Is this worth the money we’re spending?
  • And is there anywhere we can save money without blowing everything up?

So yes, metrics are useful—but only when you give them meaning.

This is where what you learned from a toddler becomes helpful. Instead of asking “why 17 hundred million times in a row,” though, ask, “so what?” 

For every data point you review, ask yourself, So what? What does this metric tell us about our strategy, our audience, and/or our spend?

If it doesn’t answer a business question—or lead to a decision—it probably doesn’t belong in the deck.

Let’s take an example. Let’s say your social engagement rate has gone up by 12%. Cool. But… so what?

Did that engagement lead to more downloads? More traffic to key product pages? A bump in lead quality? Or is it just more people liking your memes?

If it’s the latter, that’s still useful—but not strategic. Unless, of course, you turn it into something strategic by connecting it to bigger outcomes.

What you’re really trying to do here is move from “what happened” to “what it means.”

Data Storytelling is the Highlight Reel

Because executives don’t want the play-by-play. They want the highlight reel—with commentary. 

They want to know:

  • What changed?
  • Why did it change?
  • What should we do about it?

And if you really want to level up? Help them understand causality—not just correlation.

Let me explain.

Let’s say your website traffic jumped right after a big story was published. That’s correlation.

But if you can show that the people who came to the website from that story moved through the funnel faster, or converted at a higher rate, or had bigger deal sizes—that’s causality. That’s insight. That’s budget-earning data storytelling right there.

Now, to be fair, causality isn’t always easy to prove. Sometimes all you have is a strong directional signal and a decent hypothesis. That’s OK. The key is to show you’ve thought it through—and that you’re not just grabbing the nearest stat and hoping it lands.

Because when you can walk into the room and say, “This is what we’re seeing, here’s what we think is driving it, and here’s how we’d recommend responding”—that’s when you stop being the person who pulls the reports… and start being the person who shapes the strategy.

Structuring Compelling Data Storytelling

Because “let me walk you through these 47 slides” is not a story—it’s a hostage situation. And sometimes, as Meredith D’Agostino pointed out last week in the Spin Sucks Community, sometimes all you have is one slide to tell your story—not 47 or 20 or even 5. Just one. 

The next step, then, is to package it in a way that makes people care—and in a way they want to see it.

This is where most marketing reports fall apart. Not because the work isn’t good. Not because the results aren’t solid. But because the story is buried under a mountain of metrics, a sea of slides, and a whole lot of “Look what we did!” instead of “Here’s what you need to know.”

So here’s the golden rule of data storytelling: Start with the business question, not the spreadsheet.

Before you even open PowerPoint or Google Slides or Word or whatever flavor of reporting torture you’re into, ask:

  • What decision is this meant to inform?
  • What question are they already asking—out loud or silently? (Remember, we discussed last week the questions execs are likely thinking, but not asking)
  • What part of the business do they care about most right now?

That’s your starting point. Not the campaign timeline. Not the CTR trendline. Not the UTM parameters. The business question.

Once you have that, you can structure your story in three acts. 

Act 1: Set the Stage

Give them just enough context to care. What’s the goal? What’s the environment? What changed recently that makes this data relevant?

I sit on the board of an organization that starts every board meeting this way. The co-founder does an analysis of the industry, its trends, and how it might affect their goals. It’s a nice way to set the stage and help the board members, who are not industry experts, understand what’s at play at the macro level.

The same goes for your data storytelling. This is your “why should I be paying attention to this?” moment. 

Don’t skip it. 

Executives are busy, distracted, and not living inside your dashboard (or your head, for that matter). You have to meet them where they are.

Act 2: Deliver the Insight

This is the “what the data reveals and why it matters” part. Not every detail—just the ones that tie to a decision, risk, or opportunity.

Focus on clarity. Use simple, declarative sentences. “We saw X. It means Y. We believe it happened because of Z.” 

You’re not trying to impress them with complexity. You’re trying to arm them with understanding.

Act 3: Deliver the Payoff

This is the big finish. The “Here’s what we recommend, and why it matters now” moment. Don’t bury it on slide 47. Bring it forward. Make it obvious.

And for the love of Thor, make it actionable. If your story ends with “So… yeah, that’s what we found,” you’ve missed the point.

A great data story ends with:

  • A recommendation
  • A clear next step
  • A decision point

Even better? Offer options. “Based on this, we see two paths forward: we either A, increase spend and scale what’s working, or B, pause and reallocate to where we see early traction.”

At the end of last year, our paid media lead came to me and said, “The Google ads for our client aren’t working like they used to. I’d like to take some of that spend and reallocate it to boosting some of our higher performing content and see what happens.”

We talked through how he would present it to the client and what the recommendation would be, and why. He told the story in one slide and the client quickly said, “Do it. I trust you.”

When you do this, you’re not just informing—you’re guiding.

Visuals in Your Story

And a quick word on visuals: they should support the story, not be the story.

One well-labeled chart showing a clear trend is better than six dashboards with competing color palettes and font sizes that make your eyes bleed. One of my colleagues said to me a few weeks ago, “Gini, I know you love your primary colors, but the inconsistency is making my head hurt.”

Point taken!

Remember what we said last week: you’re deep in the garden, pruning every plant. Your exec team is flying overhead in a helicopter, looking for fires. Don’t show them the flower you lovingly watered for three weeks. Show them the smoke.

Bringing Data Storytelling to Life

Your data is never just a list of numbers. It’s a story—one that can confirm your direction, reveal something new, warn you of a brewing problem, or show you where untapped potential lives.

When you can recognize the story your data is telling, translate it into business results, structure it clearly, and make it relatable? That’s when things start to change.

You stop being the person who reports the news. You start being the one who influences what happens next.

And we’re just getting started.

Next week, we’re going to bring it all to life. I’ll walk you through a real-world example of turning a messy pile of marketing data into a compelling narrative—one that changed the trajectory of a campaign and helped a client make better decisions. 

We’ll also cover how to handle uncomfortable or contradictory data, how to turn insights into action that actually sticks, and how to make your story land with the people who matter most.

Until then, your homework is to look at your latest report and ask yourself, “What story is this data trying to tell?”

If it’s not clear yet—that’s OK. We’ll keep building those data storytelling muscles together.

© 2025 Spin Sucks. All rights reserved. The PESO Model is a registered trademark of Spin Sucks.

Gini Dietrich

Gini Dietrich is the founder, CEO, and author of Spin Sucks, host of the Spin Sucks podcast, and author of Spin Sucks (the book). She is the creator of the PESO Model© and has crafted a certification for it in collaboration with USC Annenberg. She has run and grown an agency for the past 19 years. She is co-author of Marketing in the Round, co-host of Inside PR, and co-host of The Agency Leadership podcast.

View all posts by Gini Dietrich