
Strategy
Part of Digital PR strategy planning and delivery explained
A digital PR ninety day plan built on data or one that drifts
How to build a digital PR ninety day plan around data and sources, using the ONS, Nielsen and Google Search Central, with review points and a glossary.
What to take away
- A digital PR ninety day plan works when the first two weeks go on source selection, not pitching. A Manchester agency chasing retail coverage needs different data from a Leeds team serving B2B software clients.
- Three sources do most of the heavy liftingONS digital economy statistics for market scope, Nielsen Media Impact for audience planning, and Google Search Central's structured data documentation for markup that makes your assets findable.
- Fix the review points before day one. A 30/60/90 rhythm with an owner on each checkpoint beats a plan checked only when a client asks.
- Keep a source register from week one. The datasets you cite at pitch stage are the ones a journalist will ask about later.
Why does week one decide the whole ninety days?
Most plans fail at the sourcing stage, not the pitching stage. If you cannot name the dataset behind a story, it will not survive a journalist's first reply.
Week one sourcing sequence
- Start with marketONS digital economy statistics
- Map storyNielsen media planning tools
- Write one-page source register
- Record owner, refresh date, intended use
Start with the market. The digital economy statistics published by the ONS give a defensible picture of sector size and direction, which turns a pitch from an opinion into a finding.
Then map where your story could land. Nielsen Media Impact, Nielsen's cross-platform media planning tool, covers audience planning and targeting, so you can match a data angle to a publication's actual readership rather than a guess about who reads it.
Write both down. A one-page source register with owner, refresh date and intended use is enough.
Weeks one and two, step by step
- Week oneshortlist two or three candidate sources and test each one — is it primary, is it current, and can you explain the method in two sentences?
- Week oneopen the planning template and record the dataset, publisher, reference period, owner, refresh date and the angle each source supports.
- Week twodraft two or three angles per source, note the publication type each one fits, and test the strongest angle internally.
- Week twoconfirm the source register, agree the outreach list and book the day 30 checkpoint before any pitch goes out.
How do you choose data that survives a newsroom?
Three tests, applied in order.
First, is the source primary? Government statistics, regulator publications such as Ofcom's Online Nation, and platform documentation from Google Search Central qualify. Aggregated blog posts do not.
Second, is it current? A dataset from 2019 will be challenged. Note the reference period next to every figure you plan to use.
Third, can you explain the method in two sentences? If not, you cannot defend it under questioning.
Implementing structured data for rich results from Google Search Central helps a data asset get picked up in search, which extends the life of a campaign past the outreach window. Validate each template with the Rich Results Test before it ships.
For a repeatable version of this process, the digital PR planning template covering data and sources sets out the fields to capture before anyone opens a media database: dataset, publisher, reference period, owner, refresh date and the angle the source is meant to support.
What should the 30, 60 and 90 day checkpoints cover?
Day 30 is about source readiness, and the strategy lead owns it. You should have the register complete, two or three angles tested internally, and a clear view of which publications are realistic targets.
Day 60 is about live outreach, owned by the outreach lead. Review reply rates by angle, not just by volume. If one angle is generating silence, retire it and reallocate effort.
Day 90 is about what carries forward, reported by the analytics lead. Which sources need refreshing, which relationships produced coverage, and what the next quarter should test.
How do you keep the plan honest?
Tie every activity to a source and an owner. An activity without either is a wish.
Separate earned coverage from link outcomes. They overlap but they are not the same measure, and conflating them hides which part of the plan is working.
Lock the quarter only after checking it against the wider strategy, because source choices should follow strategy rather than lead it.
For example, a team paying £2,500 a month for a retained programme should expect the source register to justify at least two distinct angles per month, each traceable to a named dataset.
What does a drifting plan look like?
A plan that drifts starts with a target publication list and hunts for data to fit it. The source turns up late, the register is never written, and the first question about method ends the conversation.
A data-built plan reverses that order. Sources are chosen and documented first, angles are tested against them, and the outreach list follows the data. Both run for the same ninety days; only one survives a fact-check.
Glossary
- Source register
- a maintained list of datasets, owners and refresh dates behind your angles.
- Angle
- the specific finding you pitch, distinct from the topic.
- Earned coverage
- editorial placement secured without payment.
- Structured data
- markup that helps search engines understand a page's content.
Common questions
How many sources does a ninety day plan need?
Two or three primary sources are usually enough to support a quarter of angles. More than that and maintenance becomes the job rather than outreach.
Do I need paid media planning tools?
Not necessarily, but targeting tools reduce wasted pitching. If budget is tight, use the free tiers and document the limitation.
When should the first pitch go out?
Aim for week three. That allows source verification and one internal review before anything reaches a journalist.
What if a source is updated mid-quarter?
Treat it as an opportunity. A refreshed dataset is a legitimate reason to revisit publications that covered the earlier version.



