Turn a defensible finding and transparent method into a newsroom-ready story.

Your usage data, survey results, or analysis of public records may contain a story unavailable elsewhere. Data-Led Digital PR identifies a defensible finding, documents the method, and builds a pitch for relevant reporters.

You walk away with a verified finding, a methodology note that survives a skeptical read, a pitch sent to a qualified list, and someone who owns the data if a reporter asks.

Turn your data into a story

Some of the 500+ brands we've worked with

See all references

We start with the finding, test the method, shape the story and visuals, pitch relevant reporters, and prepare for data requests.

Find the finding
We review the material you have, whether that's usage data, transaction logs, or a survey you could run. The useful candidate is a number that surprised your own team and can withstand a closer look.
A candidate finding with a first read on whether it's newsworthy.
AI assist
Agents can scan months of usage data or survey responses and surface candidate anomalies with basic statistical checks. Whether an anomaly is news is a judgment made after returning to the raw data.
Human gate
A strategist calls whether the candidate finding is genuinely newsworthy.
Stress-test the method
Sample size, source reliability, seasonal noise, and obvious counter-arguments get checked before anyone builds a pitch around the number.
A methodology note that survives a skeptical read.
AI assist
Agents can surface candidate anomalies and run initial statistical checks. The strategist then tests whether any of those findings still hold under scrutiny.
Human gate
Whether the finding survives a skeptical read is the strategist's decision. The agent's sanity check is only a first pass.
Build the story and the visuals
The number becomes a narrative a reporter can use, with a headline finding, context, and a chart or two that make the evidence easier to read.
A press-ready story with supporting visuals and the underlying data available on request.
AI assist
Agents can assemble the methodology note from the analysis record and check the figures in the story against the source table. Whether the note would survive a skeptical reporter is read by the strategist who will answer the questions.
Human gate
The narrative moves forward only after an editor verifies that it doesn't overstate what the number shows.
Pitch it to the right beat
We match the story to reporters who cover this kind of finding, then lead the pitch with the number and its relevance to their beat.
A targeted pitch sent to a qualified reporter list.
AI assist
Agents can build the beat list from recent bylines and prior coverage of similar findings. The pitch is written by a person who can explain why this finding belongs on that reporter's desk.
Human gate
The strategist approves the reporter list before any pitch goes out.
Track pickup and hold the data ready
Once it runs, we watch for coverage, follow-up questions, and requests for the underlying dataset, and keep the method note ready to hand over.
A coverage log and a data-request response ready for follow-up.
AI assist
Agents can monitor pickup, capture coverage, and flag where the finding has been restated inaccurately. Correcting a misreport is a conversation, and the data owner has to be available for it.
Human gate
Someone on the account is named as the person who releases the underlying dataset if a reporter asks.

You have to be able to defend the method to a reporter

Months of usage data or survey responses can hide a useful finding in plain sight. Agents help by surfacing candidate anomalies and running basic statistical checks across the full set. News judgment comes later. A Zeo strategist returns to the raw data, tests the sample and methodology, and decides whether the finding is strong enough to put in front of a reporter. The same strategist answers questions about the method. If the math is shaky or the sample is too small, the campaign stops before a headline is forced from it.

The story leads the pitch, while the methodology note gives reporters the evidence needed to assess it.

The data story and headline finding
The core number, written up as a story a reporter can use.
Methodology note
How the number was calculated, what it excludes, and where the honest limits are.
Press visuals
The chart or graphic that makes the finding legible at a glance, sized for how outlets use them.
Pitch and coverage log
Who received the story, what ran, and what's still open.

We call it done when: the finding is verified, the methodology note can survive a skeptical read, the pitch has gone to a qualified list, and someone owns the data if a journalist asks for it.

This method requires a genuine finding from data you measured or analyzed. A borrowed statistic or an opinion presented in a chart is not enough.

A good fit when

Your usage, sales, or survey data contains a finding nobody outside the company has seen, but the team has not tested whether it can carry a story.
The result surprised your own team and emerged from the raw data, while the conclusion had not been chosen before the analysis began.
The finding will face questions about how the number was produced, because a skeptical reporter needs the method to trace back to raw data.
The deadline leaves enough room to stress-test the sample and method, while the story and visuals still need to be built before outreach.

Better handled as other work when

The conclusion was chosen before the analysis, so the team is now looking for a statistic that will make an existing claim appear data-led.
Basic fact-checking exposes a weak sample or method, so the campaign stops before anyone builds a headline or pitch.
A fixed placement date is non-negotiable, while reporter interest and timing remain outside anything the dataset or campaign can control.

Some of these consultants write the pages themselves. Others plan distribution or check how a piece performs once it's live, so the team below covers more than one stage of the work.

Typeform
collects original responses when the story needs its own dataset
Jupyter
the reproducible analysis behind every finding we put in the pitch
Datawrapper
builds newsroom-ready charts directly from the verified result table
BuzzSumo
checks whether the proposed finding repeats a data story already exhausted
Muck Rack
finds reporters who have already handled this kind of dataset
Airtable
the claim ledger tying each pitch line to its evidence