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Last week, we talked about how to stop reporting on your marketing metrics and start telling the story behind them. We covered the four types of data stories—confirmation, revelation, warning, and opportunity—and explored how to translate those stories into business relevance, structure a compelling narrative, and influence decisions.

But that’s not the end of the, er, story. 😂

This week, we’re picking up where we left off—and digging into how to make your stories stick.

You’ll learn how to explain complex data in a way normal humans can understand, how to share tough news without losing trust, how to bring your insights to life through real-world examples, and how to make your recommendations actionable and unforgettable.

Because if your data story doesn’t land? Neither does your strategy.

Making Data Relatable

At this point, you’ve found the story in your data. You’ve translated it into something meaningful. You’ve structured it so leadership knows what to care about and what to do.

But one final piece will take your story from “Got it” to “Ooooh, I’m stealing that for my next meeting.”

And that…relatability.

This is where metaphors, analogies, and mental models come in. Some of the stuff we have to explain is complex and messy and sounds like it belongs in a data science journal, not a boardroom.

So instead of explaining that conversion rates dropped due to diminishing marginal returns on channel saturation, try this: “It’s like we squeezed every single drop of juice out of that lemon. If we want more juice, we need a new fruit.”

Boom! Suddenly, everyone’s on the same page.

You already know this because you’re a storyteller, but analogies help people feel the insight, not just understand it. They create stickiness. They make your message portable. Someone hears it, nods, and then repeats it to someone else later—and that’s when you know it landed.

Let’s run through a few examples you can borrow (or adapt):

“It’s a leaky funnel.” A classic. Everyone knows what a leaky funnel is. And suddenly, your conversation about drop-offs between marketing-qualified and sales-qualified leads makes perfect sense.
“We’re pushing a boulder uphill.” Or, as I like to say, we’re pushing a boulder up Mt. Everest. This is great when you’re describing an effort that requires constant energy but isn’t gaining momentum. Bonus points if you can pivot to a recommendation that helps it roll downhill instead.
“We’re weeding in the wrong garden.” This is a nice one when the budget is going toward a channel or tactic that’s no longer yielding results. It helps shift focus to where growth is actually happening.

And then there’s the mental model approach—creating simple frameworks that your team or execs can reuse.

Things like:

Green, yellow, or red metrics so someone who is glancing at a slide can quickly triage performance
Early, mid, or late funnel stories to match content with behavior.
Or going back to your four data stories—confirmation, revelation, warning, and opportunity.

These aren’t just clever soundbites. They’re shortcuts to shared understanding. They help your audience process the insight faster, which is exactly what they need when they’re sitting through four other strategy meetings that day and trying to decide where to put their money.

One important note here: relatability doesn’t mean dumbing it down. It means translating complexity into clarity. It’s not about making data cute. It’s about making it useful.

If you’ve ever had an executive look at you and say, “OK, now I get it,” you know how powerful this stuff is.

And once you start weaving analogies and frameworks into your stories, you’ll find people remember them. They’ll refer back to them. They’ll start using your language in their conversations.

And that, my friends, is the dream.

Turning Numbers into Narrative

OK, we’ve talked to death about what it should look like. Let’s bring it to life with a real example.

A couple of months ago, I got a panicked call from someone in the Spin Suck Community. He told me that they had been consistently generating inbound leads through their blog, newsletter, podcast, and downloadable resources—nothing overly flashy, just smart, steady content that worked.

Until it didn’t.

Over a quarter, their lead flow had slowed to a trickle. Website traffic was steady. Email engagement was still decent. But new business opportunities? Poof. Gone.

Watching that decline in data, they did what a lot of us do when content underperforms: doubled down on publishing frequency, refreshed a few CTAs, and added a pop-up.

But still—nothing.

So we rolled up our sleeves and went into data-detective mode.

Here’s what we found:

The content was still getting read, but by existing subscribers, not new audiences.
Organic search rankings had slipped just enough to tank top-of-funnel visibility, and they weren’t showing up in generative AI searches.
Even though they had refreshed a few CTAs, most of them were buried or outdated, leading to content dead ends.
No one had looked at content performance through a lead quality lens in over a year.

This wasn’t a volume problem. It was a relevance and visibility problem.

Rather than dump a pile of metrics in a slide deck and call it a day, we crafted a narrative around what the data told us.

Act 1 (Context): They were still creating great content, but it wasn’t reaching new audiences or moving people toward a decision.

Act 2 (Insight): They were optimizing for consistency, not conversion. It was clear that their content strategy had not evolved alongside their audience’s needs.

Act 3 (Payoff): Then we showed them how to fix it. We suggested they:

Revisit audience segmentation;
Refresh high-performing content to improve search visibility;
Create clearer pathways from content to lead generation; and
Bring back lead scoring so they could see what was working and what was not.

Our community member didn’t have to beg for more budget or stress about why it wasn’t working. He was able to show his boss the problem, the path, and the potential, and he felt confident in the solution, which was immediately given approval.

That’s the power of building a narrative around your data.

Data Storytelling Requires Insights

You are sitting on insights like this all the time. But without narrative, they get lost in the noise. The story doesn’t rise to the surface. And as a result, neither do the strategic decisions you want to drive.

So if you’re wondering how to get leadership to stop skimming your reports and start listening, show them what the story means—and then tell them what to do with it.

So far, we’ve looked at how to make your data relatable, how to turn it into a clear, structured narrative, and how to bring it to life in a way that makes people lean in—not tune out.

But what happens when the data doesn’t tell the story you were hoping it would? When the numbers aren’t great—or worse, when they contradict each other?

Handling Challenging Data

Just like real life, not every data story has a happy ending.

Sometimes your conversion rates are down. Sometimes that campaign you swore would crush it… absolutely did not. Sometimes the data is confusing or incomplete or contradicts itself like it’s trying to gaslight you.

And when that happens, the temptation is strong to quietly slide that deck under a stack of papers and move on.

“Oh look, something shiny over there—should we talk about Instagram Reels instead?”

But here’s the thing: your credibility doesn’t come from only sharing the wins. It comes from telling the truth, even when it’s uncomfortable, and showing that you’ve thought through what to do about it.

Nearly every exec on earth is totally cool if you present the challenges you’re having with solutions. No one wants to hear the problems and then have to lean in to fix them. But if you present the issues with solutions? You’ll be a winner every time.

So let’s talk about how to tell the more challenging stories without undermining trust or sounding like you’re making excuses.

Step One: Lead with Honesty

Don’t bury the bad news on slide 17. Don’t talk in circles, hoping they won’t notice. Just name it.

“Our email engagement is down 20% compared to last quarter.”
“The landing page didn’t perform the way we expected.”
“We missed our MQL target this month.”

Keep it factual, not emotional.

Step Two: Offer Context

Is this a trend or a blip? Did something change in the market? Have the algorithms changed? Was the competition down, too? Was the test designed to push limits on purpose?

Add the perspective they need to make sense of what happened.

This is where you can remind them that marketing is a system, and not every piece of it performs perfectly at all times. The key is how you respond.

Step Three: Come with a Plan

This is the most important part. Bad news without a recommendation is just a bummer. But bad news with a proactive fix? That’s leadership.

“Engagement dropped after we changed the CTA. We’re reverting and A/B testing new copy this week.”
“Leads are down, but we’re seeing stronger intent signals from the ones we are getting. We’re shifting spend to prioritize quality over volume.”
“The webinar flopped, but registrations showed strong interest in the topic. Next step: new format, same theme.”

You’re not just saying, “Here’s what went wrong.” You’re saying, “Here’s what we’re doing about it.”

And sometimes, the tough data becomes the story that earns you trust. Because when you show that you’re not afraid to confront problems head-on—and that you have a strategy to fix them—you build credibility. And credibility buys you more room to experiment. More buy-in. And yep, more budget.

Oh—and one more thing: messy or contradictory data? Don’t be afraid to say, “We don’t have a complete picture yet.” That’s not a weakness. It’s transparency. Just make sure you outline what you’re doing to get better data, and what you can infer in the meantime.

Because your job isn’t to have all the answers. It’s to lead the thinking, even when the answers are still fuzzy.

Making Your Insights Actionable

OK, so now you’ve told a clear story. You’ve wrapped it in a relatable framework. You’ve even delivered the hard truths with grace and a game plan.

Now comes the part where so many great presentations just kind of fizzle out.

You know the moment. You wrap up your data walk-through, wait for the applause, and instead hear… crickets. Or worse, someone says, “Cool—thanks for the update.” And then moves on to the next agenda item.

Noooope. Not today.

If your insight doesn’t lead to action, it’s just trivia. And while trivia is fun at bar night, it’s not exactly career-advancing.

So let’s talk about how to bridge that final gap—from “here’s what we found” to “here’s what we do about it.”

First: Create Decision Points

Don’t leave your audience wondering what they’re supposed to do with what you just told them.

Spell it out. Literally say:

“Based on this data, we have three options.”
“This insight suggests we should either A, B, or C.”
“Our recommendation is to double down on what’s working and shift budget from X to Y.”

You’re not just presenting the insight—you’re shaping the next step.

Second: Make it Simple

If you’ve ever watched an exec get visibly overwhelmed three minutes into your perfectly planned data walkthrough, you know what I’m talking about.

So make it easy for them. Build lightweight frameworks like:

Do more of this, less of that
Start, stop, continue
Invest, pause, kill

These help execs quickly understand where to take action without needing a whiteboard and a double espresso to process it.

Third: Set Up the “Next Chapter”

Don’t treat this report or presentation like it’s a one-and-done. The best data stories are part of an ongoing narrative.

So leave them with a preview:

“Here’s what we’ll be tracking next.”
“Here’s the test we’re running based on this.”
“We’ll report back in two weeks with early indicators.”

This turns your insight into a drumbeat, not a one-time mic drop. It reinforces your role as a strategic partner who isn’t just reporting the past but shaping the future.

If you’ve ever wondered how to earn a seat at the table, this is it. You don’t just show them what happened. You tell them what should happen next. You make the path forward clear. And you help them walk it with confidence.

That’s how you stop being “the marketing person with the charts” and start being “the one we go to when we need a smart decision.”

Data Storytelling Framework

During the last two weeks (Mastering the Art of Data Storytelling and Creating a Data Story Your Executives Will Love), you’ve learned how to find the story in your data, translate it into business meaning, structure it for effectiveness, make it relatable, handle it when it’s hard, and drive action from it.

You now have a complete framework—from data diagnosis to business decision—from “What happened?” to “Here’s what to do next.”

And maybe most importantly, you’ve seen how this isn’t just about looking smart in a meeting. It’s about becoming someone who leads the conversation, not just joins it after the fact.

That’s how you earn trust. That’s how you grow your influence. And yep, that’s how you get a bigger budget.

You don’t have to be a data scientist to be a data storyteller. You just have to be curious, clear, and committed to making your insights matter.

Because the person who can tell the clearest, most strategic story? That’s the person people follow.

And I have no doubt that’s you.

Until next time—go tell a better story.