There is a statistic for everything. The problem is, most of them aren’t stories.
Research has become a staple of communications. Consumer surveys, industry benchmarks, proprietary datasets and annual reports give brands plenty to talk about. Add increasingly sophisticated analytics and AI into the mix, and we have more information at our fingertips than ever before.
Yet anyone who has spent time pitching research to journalists knows that having a compelling statistic doesn’t necessarily mean you have a compelling story.
“72% of businesses say…” might look impressive in a report. In a journalist’s inbox, it is competing with dozens of other pitches beginning in almost exactly the same way.
The real work starts after you have the number.
A statistic tells you what. A story asks why.
When looking through a new piece of research, it is tempting to head straight for the biggest percentage. But the biggest number isn’t always the most interesting one.
Say a study finds that 80% of Australian businesses plan to increase their investment in AI. That’s useful information. But what makes it interesting?
Perhaps businesses are investing heavily while employees say they aren’t equipped to use the technology. Perhaps investment is rising despite executives struggling to demonstrate a return. Maybe Australian adoption is significantly ahead of another market, or enthusiasm has fallen sharply compared with last year.
Suddenly, there is tension. And tension gives us something much more valuable than a statistic: a question worth answering.
Why is this happening? What does it tell us about the market? Who does it affect? What happens next? That’s where a research report starts becoming a story.
Look for the number behind the number
Some of the strongest insights are rarely sitting conveniently on the first page of a report.
They can be buried in demographic cuts, differences between markets or the relationship between two findings that weren’t originally intended to be viewed together. This is where curiosity matters.
Rather than asking, “What’s our strongest statistic?”, try asking: “What’s surprising here?”
Look for contradictions. Gaps between intention and action. Differences between what leaders believe and what employees experience. Changes from the previous year. An unexpected result within a particular industry or demographic.
Sometimes a relatively small percentage can tell you far more about what’s changing than the 90-something-per-cent figure sitting in the executive summary.
The job isn’t simply to repeat the research. It’s to interrogate it.
Global data still needs a real-world reason to care
This becomes even more important when working with global research.
A finding can be fascinating at a global level and still leave a journalist asking the most important question: so what does this actually mean for my audience?
I saw this play out recently while working on an interview with RNZ technology journalist Kaya Selby. We had plenty of global statistics available around AI adoption – the kind of big numbers regularly leading research reports and media pitches. But that wasn’t what interested him.
He wanted to hear about what the executive was seeing firsthand, and how that experience could benefit organisations in the Pacific Islands – smaller, more vulnerable markets that can easily disappear within the sample sizes and sweeping conclusions of global surveys.
That distinction stuck with me.
We often assume the biggest dataset gives us the biggest story. But sometimes relevance comes from zooming in, not out. A global trend becomes far more meaningful when someone can explain how it plays out for a community, industry or market that isn’t necessarily represented by the headline number.
Local data certainly helps, but relevance isn’t simply replacing a global percentage with an Australian or New Zealand one. What’s happening in the market that makes the issue matter right now? Who is experiencing it differently? Is there a group whose experience the global numbers don’t adequately capture?
And occasionally, the journalist might tell you they’ve heard enough statistics about AI adoption already. That’s useful information too.
Don’t make the data do all the talking
There is another trap with research-led PR: expecting the numbers to carry the entire story. They rarely can.
Data can establish that something is happening. A good spokesperson helps explain why – and sometimes, the most memorable part of the conversation has very little to do with the statistic that got you through the door.
I was reminded of this recently after an executive interview with AFR BOSS. We had gone in prepared with the business story, industry trends and plenty of proof points. But afterwards, journalist Sally Patten told us that one of the things she found most interesting was the executive’s candour about his own learning curve – including the mistakes he’d made along the way.
She could relate to it, and importantly, she felt her readers would too.
It was a useful reminder that while data can earn attention and establish credibility, people still connect with people. The perfectly polished statistic may open the conversation, but an honest observation, a lesson learned or an unexpected admission can be what makes the story memorable.
That’s why a spokesperson shouldn’t simply restate the research in an interview. What are they seeing that helps explain the result? What have they learned firsthand? What might businesses be getting wrong? Has their own thinking changed?
The strongest research gives an expert evidence for a point of view. It shouldn’t become a substitute for having one.
Data is abundant. Interpretation is the advantage.
As access to information grows, finding another statistic isn’t going to become difficult. Working out which one matters will. And for communications professionals, that’s an opportunity.
Our value isn’t in turning every research finding into a media pitch. Sometimes the most useful conclusion we can reach is that there isn’t a story there.
The value is knowing when there is one.
It’s spotting the contradiction nobody expected, connecting a global finding to a conversation happening locally, listening when a journalist tells us where the real interest lies, and understanding why one statistic on page 37 might matter more than the headline number on page one.
Because everyone has data. The story is what you do with it.
