As the CEO sits through yet another meeting in their daily lineup, their eyes glaze over halfway through the latest monthly update. They hear something about reduced spend, conversions, and how the company is seven sales under goal. Their attention is lost because, well, they already know all of this. While the data is important, there is no clear story, no hook, and certainly no urgency. In truth, the presentation feels more like reading a weather report with pretty charts and graphs than a business-critical performance update. What’s the takeaway? What should they do with this? The message might be in there somewhere, but it’s buried under too many data points and not enough meaning.
As the clock ticks on, time is running out for the team to craft something actionable from the data they received. If we’re underspending and under goal… well then the obvious is just to spend more, right? If the team is asking the CEO for more money, then they better darn right see the results the following month, or at least that’s the unspoken expectation.
But alas, that’s not what happened. Did they spend more? Sure did. Did numbers improve? Yes. Yet, they’re still under the sales goal. So what else could be going on? As they enter their next monthly update, they’re hoping for answers, for solutions, and for some real understanding of how to improve. Hopes are low and frustrations are high, because once again they’re delivering the same phrases. Same solutions. The same words around slightly different data points.
It’s Time to Change the Narrative
This situation is so common because data storytelling is a skillset that is so incredibly difficult to teach, to learn, and to put into practice. It starts first with the understanding of what exactly data storytelling is, and more importantly, what it isn’t.
Repeating metrics in a meeting isn’t storytelling. Putting data points in a sentence isn’t storytelling. Saying “traffic is up 23% and key events are down 10%” is simply reading a dashboard. There’s no story there. There’s no “why,” no context, no journey. The story develops once you connect those data points, and more importantly, how you add context to the connections.
Real storytelling is emotional, thought-provoking, and provides a different level of understanding. The art of data storytelling is rooted in your ability to weave a web of interconnected truth that allows the audience to become emotionally connected and convinced.
To get you started, I’ve listed the 5 rules of data storytelling below:
People Need More Than Logic
Data appeals to logic. But change isn’t made through logic alone. Humans require an emotional motivator. If your company is underperforming and you’re given the opportunity to present data, your goal should be to guide them in the direction for change. To do this, you need an emotional trigger. When presenting only data points, you miss your opportunity to emotionally connect with your audience.
Let’s break this down in an example: There’s a stark difference between “We converted at 9% from appointment-to-sale” and “We had 41 of 45 well-qualified, motivated customers that submitted a form, talked to the OSC, scheduled an appointment, showed up for said appointment and still did not buy.” For an extra bang, “that’s $8,200 (let’s say your cost-per-appointment is $200) of advertising dollars, and $12.3 million (at an average sale of $300,000) in sales opportunity.”
Every single CEO would get fired up at the opportunity to capture an additional $12.3 million rather than spending another $10,000.
Go Beyond the Surface to Find the Trigger
The first layer of data is rarely the full picture. It’s just the start. When you dig below the surface to identify all variables at play, you often uncover the most impactful variable that will be the key to your story and steps forward. “Lead-to-kept appointment conversion is down 15%” is the surface-level data point. But the real story could be about the follow-up process, training gaps, or maybe even the weather. Keep digging until the trigger reveals itself, because the trigger is probably what deserves to be acted on (unless it ends up being the weather).
In our example above, the in-depth analysis may have uncovered, for example, of the 41 appointments that did not purchase, they only received one follow-up email with the classic (and dead) call-to-action of “let me know if you have any questions.” There’s nothing providing next steps, no additional information, nothing personal to their search, and frankly - we made ourselves forgettable.
Because we dug further than the data point, we’re able to provide an actionable solution towards change. “We have the opportunity to re-capture these 41 appointments through diversified follow-up (video email, phone calls, texts), and a clear next step. It’s time to reengage.”
Ask Better Questions, For Insight Rather than Answers
The best way to dig deeper is through questions. Each time you uncover something new, ask “why?” or “how?” Those questions challenge perspectives and lead to better decisions. As you dig deeper into your data, it’s important to ask meaningful questions, and to prepare yourself for the questions you, too, will be asked. Sometimes these questions require you to go outside your comfort zone and into the field. Have conversations, listen to your peers and to your customers. Consider psychology, experience, and challenge assumptions.
We would have never uncovered the impact of the variable of lost follow-up if we first hadn't asked, “what happened to these other 41 customers?” or “what did their experience look like, and is there an opportunity to improve it?”
Be Concise, Be Powerful, and Anticipate Questions
A good story is simple and impactful. You don’t need 47 slides to prove your point. Remember, time and attention are precious. Respect the time you’re given. One clear narrative with a compelling takeaway can create momentum. But prepare for objections. People will challenge your story. That’s okay. If you’ve done your homework, and your data supports the emotional arc of your story, you’ll be ready.
Yes, it’s somewhat dramatic to say $12.3 million of opportunity still exists, and follow-up is the variable we need to change. There will be questions. There will be push back. Don’t craft your story around delivering everything so as no one has questions. Welcome the questions, and be prepared to speak on them. Only then will you gain the trust and confidence of your audience.
Objection: “These people aren’t ready to buy; we need better quality leads.”
Truth: “On average, we’re 10% less likely to convert a lead to an appointment, as customers are taking longer in their buying journey. It’s taking us 3 web visits, and 5 touch-points with the OSC to get them in our doors. If they’re not ready to purchase at the time of their appointment, then it’s going to take more than one email with no next step. We need to guide them.”
Don’t Use AI for a Story
Can AI analyze data? Sure. Can it write headlines or suggest insights? Absolutely. But can it feel the nuance in a conversation with your sales leader? Can it understand your builder’s unique market quirks, internal operations, or customer psychology? Does it know how badly the model home kitchen smells because someone reheated leftover shrimp for lunch? Nope. This kind of storytelling still requires you. Layer those real-life details alongside your data points, and then view the web of information through the eyes of your audience. When you craft a story through the personalized lens of your intended audience, you draft meaning specifically for them, thus providing context, analysis, and emotion that resonates with your key stakeholders. As a result, you’ll lead with a personalized, powerful narrative that cannot be replicated by a machine.
AI systems require learning and time in order to compound the knowledge that you already have today. It’s not going to know, innately, that the email follow-up was singular and not formulated to company best practices and be able to connect that directly to the 41 missed opportunities, with a price tag attached. You need to build that skill and knowledge. Need more convincing? In order to become a trusted expert, you can’t ask your CEO, “May I have a moment to plug your question into Chat GPT so it can think for me?”
Data storytelling takes time and practice. Most importantly, it takes getting comfortable with the uncomfortable. Go beyond the surface, and think deeply about the hidden variables behind your data points. Remove the biases and craft a narrative that resonates with your audience. Following these 5 rules will challenge you in new ways, and you’ll begin to see your data in a new light. As a result, you will help the root issues that need to be properly addressed and be able to provide actionable solutions. Moreover, once a problem is solved, then everyone is freed up to solve another problem, and it is that pattern that helps builders navigate market turbulence well while others fail.
