3 on the recordJason Bland, Sahil Kakkar, Runbo Li
Expose Crosstabs and Pose Unbiased Questions
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The question desk
Public relations professionals often struggle to balance compelling data narratives with maintaining credibility and trust. This article explores practical strategies for creating newsworthy PR data stories that remain honest and transparent, drawing on insights from experts in the field. Learn how to ask the right questions, establish proper boundaries, and prioritize truth over promotional messaging in your data-driven PR campaigns.
When we run proprietary surveys for law firm clients, the biggest mistake I see competitors make is designing the survey to confirm what they already want to say. Reporters smell that immediately and pass. My approach is simple: ask questions where you genuinely don't know the answer.
When we surveyed people about how they choose a personal injury attorney, I had hypotheses, but I wasn't engineering the questions to land on a predetermined headline. That openness shows up in the data, and journalists notice. The single decision that made the biggest difference in getting reporters to take our findings seriously? We published the crosstabs and methodology. Not buried in a footnote, front and center.
Sample size, how respondents were recruited, margin of error, exact question wording. Everything. Most PR-driven surveys hide that stuff because they know it won't hold up to scrutiny. When you put it all out there, you're essentially daring someone to poke holes in it. Reporters respect that. It signals you're not hiding anything.
I also think about whether the finding is actually surprising. If your survey result confirms conventional wisdom, that's not news. When our data showed that a significant percentage of people selected their attorney based on website quality before ever reading a review, that was counterintuitive enough to generate real coverage. It challenged something people assumed they understood.
One more thing: resist the urge to overstate. If 34% of respondents said something, don't write "most people" or "the majority." That's where credibility dies. A reporter who catches one exaggeration will question everything else in your release.
The goal isn't a flashy headline. It's a finding that's defensible, surprising, and backed by methodology you'd be comfortable defending on the record. Do that consistently and reporters start coming to you instead of the other way around.
Sahil KakkarCEO / Founder · RankWatchThe most important decision was defining the limits before collecting the data. We decided what the sample represented and what it did not represent before we started. We also agreed which comparisons to leave out even if they looked interesting. This simple step helped us keep the story clear and made our findings more trustworthy.
We write the findings as if a skeptical editor is reading them first. We use simple language and avoid broad claims that the data cannot fully support. We also leave room for details that may not fit a simple conclusion. When journalists see this careful approach they are more likely to trust the insight and the story behind it.
Runbo LiCEO · Magic Hour AII'm Runbo Li, Co-founder & CEO at Magic Hour.
The single biggest mistake people make with proprietary data is designing the study to confirm what they already want to say. Reporters smell that immediately. The decision that earns trust is simple: you have to be willing to publish a finding that makes you uncomfortable.
Early on, we ran internal analyses on how our users were creating videos. We found something we didn't expect. A significant portion of our most engaged users weren't using the platform for polished marketing content. They were making memes, fan edits, joke videos. My first instinct was to bury that and only surface data about "professional use cases" because that's what sounds impressive to a journalist writing about AI tools for business.
We didn't bury it. We leaned into the full picture. And that's what made reporters trust us. When you present data that includes findings you clearly didn't engineer to flatter yourself, editors stop treating your release like a press kit and start treating it like a source.
The framing decision that mattered most: we never led with our product. We led with the behavioral insight. The story wasn't "Magic Hour users do X." The story was "here's how real people are actually adopting AI video, and it looks nothing like what the industry predicted." That reframe turns your data from self-promotion into a trend piece that a reporter can build around.
One practical rule I follow. If every single data point in your release makes your company look good, you've already lost credibility. Include at least one finding that's surprising, counterintuitive, or even slightly unflattering. That one data point is what makes a journalist believe the other nine.
The bar for earned media is higher than ever. Reporters don't need your data. They need data that tells them something they couldn't find anywhere else. If your proprietary findings just confirm conventional wisdom with a company logo on top, it's dead on arrival. The newsworthy move is always the unexpected truth you were brave enough not to edit out.
Focus on multi-year trends so one odd spike does not drive the story. Use the same method each year so changes are real. Show the baseline and seasonal patterns. Explain what outside events may shape the line.
Call out when a change is within normal noise. Share a simple chart and a short note on how long the trend holds. Build a trend view before you write the pitch.
Co-author releases with respected schools, labs, or public agencies to add rigor and reduce doubt. Spell out how each side collected, cleaned, and checked the data. Include a clear note on funding, conflicts, and review steps, such as an ethics check when people’s data is used.
Give both names equal space on the release and in media notes so it feels like shared work, not a paid seal. Invite reporters to a joint briefing where both teams take questions and explain limits. Reach out to a trusted partner and plan a joint release.
Write measured headlines that match the data and its limits. Use clear verbs like may signal or is linked to when proof of cause is not there. State the group, place, and time in the headline so claims are not too broad. Keep numbers exact and avoid hype words that promise too much.
Make sure the first line repeats the warnings that the headline implies. Test two calm versions with editors to see which reads best. Draft a careful headline that your data can support.
Link the story to issues that many people face right now, like costs, jobs, health, or safety. Use plain words to show who is helped or harmed and by how much. Connect the data to current policy moves, market shifts, or seasonal events so it feels urgent but fair. Offer state or city views so local outlets can make it theirs.
Avoid newsjacking by making sure the link is real and not a stretch. Add a short explainer on why the issue matters this week and this year. Map your key finding to a timely public need and pitch it today.
Invite independent experts to review the findings and share context that is not in the raw tables. Choose voices who did not work on the study and who can speak to method, bias, and real world use. Give them full access to methods and notes before release so they can check the work.
Encourage them to share quotes that explain what the results do and do not mean. Publish their comments and your replies in a short note to show open review. Line up two outside experts and schedule a pre-brief today.