Data posts earn or lose credibility in the caveats, not the headline stat. A sample-size flag on a 31-person sub-segment costs one sentence to include and saves the post from a much worse fate: a sharp reader spotting the gap and discounting every other number in the piece.
Content
Turn survey or research results into a data-driven post
Leads with the single most newsworthy finding from original survey data, and refuses to state a sub-segment result with full confidence when that segment's sample size is too small to support it.
Works with Claude / GPT2,700 uses★ 4.6
The prompt
content-survey-results-to-data-post
You write research-based content for a WordPress agency network. You ran a survey
of 412 agency owners about pricing models and have the raw cross-tabs. Now it needs
to become a post, not a raw data dump of every question asked.
SURVEY RESULTS (key findings): [
"- 68% of agencies (n=412) now use value-based pricing over hourly
- Agencies using value-based pricing report 22% higher average client retention
- Among agencies with under 5 employees (n=31), retainer pricing was reported by 81%
- Average project price rose 14% year over year"]
GOAL: [a post for agency owners deciding how to price their own services]
METHODOLOGY NOTE: [self-selected respondents from an email list of agency owners
who opted into the survey, not a random sample of all agencies]
Work through this before drafting:
1. Find the single most newsworthy or counterintuitive finding to lead with (likely
the value-based pricing shift, since it's the clearest actionable trend), not a
restate-every-question structure that buries the interesting result in the
middle.
2. Edge case: the "22% higher retention" finding is a correlation from the survey,
not a proven cause. State it as an association ("agencies using value-based
pricing also reported higher retention") and explicitly note that the survey
can't establish whether pricing model caused the retention difference or whether
more established, client-savvy agencies simply tend to use both, don't let the
phrasing imply causation the data doesn't support.
3. Edge case: the under-5-employee retainer stat is based on only 31 respondents.
Flag that this sub-segment is too small to generalize with the same confidence as
the full-sample (n=412) findings, either soften the language ("among the smaller
sample of very small agencies surveyed...") or note the limitation directly next
to the stat rather than presenting it with equal weight to the headline number.
4. Include a short methodology section disclosing the self-selected sample
(respondents opted in from an existing list), since that's a real limitation on
how broadly these results generalize, don't bury this in fine print at the very
bottom.
5. Decide what earns a chart (the pricing model shift, since it's the core trend
readers will want to visualize) versus what's fine as an inline sentence (the
single year-over-year price stat), rather than turning every number into its own
chart.
Draft the full post: headline built around the lead finding, an intro stating what
was surveyed and why it matters, sections for each major finding with appropriate
correlation/causation and sample-size caveats, a methodology note, and a practical
takeaway for readers deciding on their own pricing.
Replace the bracketed survey results, goal, and methodology note above with your
own.