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Worked examples

Original Research Pitch Examples: Surveys, Platform Data and Local Findings

A useful data pitch lets a reporter assess the finding, not just repeat a number. Put the population and key limitation beside the result, and make the methods available.

Illustrative, not real outreach. Every company, person, result and event in these emails is fictional. Dates belong to the scenario. These are teaching examples, not campaigns that earned coverage. Replace the facts, check the recipient and approve the copy before sending.

Worked example

Survey: keep the population in the finding

Subject: 248 of 400 surveyed ops managers said they re-entered data across systems last month
Hi Jamie, In a Worknote online survey run September 3–10, 248 of 400 U.S. operations managers (62%) said that in the past month they had manually re-entered the same information into more than one work system. The population matters here. Respondents opted in through Worknote's newsletter. Answers are self-reported and unweighted, so the figure describes this group of managers, not all U.S. managers. Because it's a nonprobability sample, there's no conventional margin of error. Worknote, a workplace-research consultancy, can share its methodology note, the exact question wording, and aggregate tables. Researcher Dana Ortiz is available to answer written questions about how the survey was run. Would the questionnaire and tables be useful to you? Alex
Read the fictional fact sheet

Worknote is a fictional workplace-research consultancy. Its fictional September 3–10 online opt-in survey asked 400 U.S. operations managers: “In the past month, did you manually re-enter the same information into more than one work system?” 248 answered yes (62%). Respondents were recruited from Worknote’s newsletter. Responses are self-reported, unweighted and not representative of all U.S. managers. This nonprobability sample has no conventional probability-sample margin of error. Worknote has a methodology note, exact questionnaire, aggregate tables and researcher Dana Ortiz available for written questions.

Worked example

Platform data: usage is not the whole market

Subject: A quarter of tickets changed teams at least once in Queuewell's September data
Hi Jamie, In Queuewell's internal analysis of 18,240 support tickets created in September by 36 customer teams that opted in, 4,560 tickets (25%) were transferred to another team at least once. The figure describes routing in that group only. It excludes noncustomers and teams that did not opt in. A transfer can be the right call, and the analysis does not measure wasted work, service quality or why tickets moved. Within those limits, it may be useful as one platform-level view of how often support work crosses team lines. Queuewell can share the deidentified aggregate tables and the note defining a transfer, and analyst Morgan Bell is available to answer written questions on the method. Would the tables and definition note be useful to you? Alex
Read the fictional fact sheet

Queuewell is a fictional support-software provider. Queuewell’s internal analysis covers 18,240 tickets created during September by 36 consenting customer teams. Of those tickets, 4,560 (25%) were transferred to another team at least once. The dataset excludes noncustomers and teams that did not opt in. Transfers can be appropriate routing; the analysis does not measure wasted work, service quality or causation. Queuewell has deidentified aggregate tables, a transfer-definition note and analyst Morgan Bell available for written questions.

Worked example

A regional cut: label it exploratory

Subject: Midwest operations managers on duplicate data entry: an exploratory cut
Hi Jamie, In a Worknote online survey from September 3–10, 72 of 120 Midwest operations managers (60%) said that in the past month they had manually re-entered the same information into more than one work system. That figure is a subset of 400 U.S. operations managers. Among the full group, 248 (62%) answered yes. Respondents opted in through Worknote's newsletter, and the answers are self-reported and unweighted, so the results don't represent all managers. Worknote treats the regional table as exploratory. It hasn't tested differences between regions and doesn't claim the Midwest stands apart. If the subgroup is useful for your desk, I can send the methodology note, the exact questionnaire and the aggregate tables. Worknote researcher Dana Ortiz is also available to answer written questions. Alex
Read the fictional fact sheet

Worknote is a fictional workplace-research consultancy. Its fictional September 3–10 online opt-in survey asked 400 U.S. operations managers: “In the past month, did you manually re-enter the same information into more than one work system?” 248 answered yes (62%). Respondents were recruited from Worknote’s newsletter. Responses are self-reported, unweighted and not representative of all U.S. managers. This nonprobability sample has no conventional probability-sample margin of error. Worknote has a methodology note, exact questionnaire, aggregate tables and researcher Dana Ortiz available for written questions. Of the 400 respondents, 120 worked in the Midwest; 72 of those 120 answered yes (60%). The same recruitment and self-report limitations apply. Worknote has not tested regional differences and does not claim the Midwest differs from another region. The regional table is explicitly exploratory.

Best practices

The decisions behind the copy

  • State what was measured, among whom, when and how. A newsletter opt-in survey is not automatically representative of an industry.
  • Preserve denominators and attribution. Customer records describe participating customers; a self-report is not observed behavior.
  • Explain subgroup and causal limits. A regional cut does not establish a regional difference, and an association does not establish why something happened.
Before drafting

Gather the facts first

  1. Sponsor, researcher and any relevant commercial interest.
  2. Target population, sample size, recruitment and collection dates.
  3. Exact question wording, response options and mode.
  4. Weighting, exclusions, missingness and subgroup sizes.
  5. Method-appropriate uncertainty; do not attach a probability-sample margin of error to a convenience sample.
  6. Aggregate tables, definitions and a contact who can explain the methods; protect individual respondents.
Common mistake

Remove the claim the evidence cannot support

Weak example: do not send

Most American managers waste their time copying data. Our survey proves broken software is the cause.

How to improve it

The survey example reports what 248 of 400 opt-in respondents said. It neither generalizes to all managers nor explains the cause. Keep that scope in the subject and body.

Your starting point

A template to adapt, not send unchanged

Fill every bracket with checked facts. Delete any unsupported promise. An empty template is not a reviewed pitch.

Subject: [Scoped finding + named population]

Hi [Name],

[Researcher/sponsor] found [result and denominator] among [sample], collected [dates and method].

[Specific reader relevance, clearly separated from the finding.] [Key limitation.]

[Offer the questionnaire, methods and aggregate tables.]

[Sender]
Read the guidance

Sources and limits

These sources inform the recommendations, not the fictional scenario facts. No template guarantees a response, placement or commercial result.

Read the related RunPR guide For the wider workflow.