AI Writing & Creator Studio
How to write a press release from your clipped articles — a step-by-step guide for marketers and PR teams who want to study competitor press releases
A competitor announces a partnership with an enterprise client, and within a week five trade publications have covered it. A different competitor launches a genuinely new feature the same month and gets a single blog mention. Ask most marketers on the team why, and they'll shrug — it's a feeling, not something they could point to.
It's not actually a feeling. It's a pattern, and if the team has been clipping articles — competitor releases, trade coverage, announcement roundups, industry news — the pattern is sitting in the clip collection already, encoded rather than explained. Which announcements got picked up. What angle each one led with. What evidence the reporters actually cited. How the sector's flagship publications choose to frame the stories they run.
The work is making that pattern explicit instead of leaving it as a hunch: read back through the clips before writing, name what the covered announcements had in common that the ignored ones didn't, and apply what's found to the release being written now.
A swipe file of industry press releases and coverage tells you:
What types of announcements get covered: Product launches, funding rounds, partnerships, research releases, executive appointments, award wins, market data reveals — which of these get significant coverage in your space? Some industries cover funding rounds prominently; others don't. Some trades cover product launches deeply; others treat them as brief notes.
What makes a release newsworthy in your specific sector: The data that journalists in your industry find credible. The types of customers or partners whose involvement generates story interest. The framing that signals "this matters."
What headlines look like in your space: Are competitor releases headline-heavy (making bold claims)? Or matter-of-fact (announcing without editorializing)? The pattern is a signal about what the publication's editors accept.
How long a release should be: Some industries have long-form trade releases; others have short, focused announcements. The releases that get covered tell you what length the publications prefer.
What the boilerplate looks like for comparable companies: Companies at your scale and stage use specific language to describe themselves in their boilerplate; reading your competitors' boilerplates is the fastest way to calibrate your own.
Before writing, analyze what your clip collection shows:
CLIP COLLECTION ANALYSIS
My clip collection includes: [N press releases, N coverage articles, date range]
Sources in collection: [Trade publications, company newsrooms, industry newsletters — list]
Coverage pattern analysis:
1. What announcement types get covered most often in my clips?
[Funding / Product launch / Partnership / Research / Award / Executive hire]
Most covered: [...]
2. What's in the headline of press releases that got significant coverage?
Pattern: [Makes a specific claim / Uses a number / Names a notable partner / Other]
3. What data types appear in covered releases?
[Market research citations / Customer statistics / Internal performance data / Industry survey]
4. What's the typical length of covered releases in my space?
[Short — 300-400 words / Medium — 500-700 words / Long — 800-1000 words]
5. What do competitor boilerplates say?
Pattern across competitors: [How they describe themselves]
What's standard language: [...]
What's distinctive: [...]
6. What angles/frames do journalists use when covering releases in my space?
[Trend/industry context / Customer impact / Competitive implications / Technical detail]
Gap I can fill: What type of story in my space is underrepresented in coverage?
[This is where a distinctive press release opportunity might exist]
With your analysis of what gets covered, assess how your announcement fits:
ANNOUNCEMENT-COVERAGE FIT ASSESSMENT
My announcement: [Brief description]
Most comparable announcement type in my clips: [Which type of announcement this resembles]
Example covered release closest to mine: [Which clip — what was announced and how it was covered]
How my announcement fits the pattern:
Similarities to what gets covered: [...]
Differences that might require adjustment: [...]
What I need to add to match coverage patterns:
□ A data point that grounds the significance (most covered releases in my space include one)
□ A named partner or customer reference (if partnership/customer announcements get coverage)
□ A specific claim in the headline (not just description)
□ Other: [Pattern from analysis]
Angle I'll take:
[Based on analysis: what framing will resonate with the journalists who cover my space]
From your clip collection, select one or two releases that are most comparable to your announcement and study them in detail:
Headline structure: How did they phrase the announcement? What did they emphasize?
Lead paragraph: What was the "so what" they led with? What facts did they put in the first 40 words?
Research or data integration: Where did they cite data? How specific was the data point? Which source?
Quote: Who quoted? What did the quote add that wasn't in the body? (Good quotes add perspective, not just restatement.)
Length and structure: How many paragraphs? Where did they put the most detail?
Reverse-engineering a comparable covered release is the fastest way to understand what works in your specific context — not as something to imitate word-for-word, but as a structural model that proves itself.
COMPARABLE RELEASE REVERSE ENGINEERING
Release: [Company, Product/Announcement, Date]
Where it was covered: [Publications that ran it]
Engagement (if known): [Pickups, shares, journalist response]
Headline analysis:
Structure: [Verb + specific claim / Company + Product + Outcome / Other]
Key element that made it click: [...]
Lead paragraph analysis:
What facts it contains: [5Ws]
What it doesn't include (saved for body): [...]
Why this works: [...]
Research/data point used:
"[The data point]" — Source: [...]
Where it appeared: [Lead / First body / Second body]
Quote analysis:
Spokesperson: [Name, Title]
What the quote adds: [Perspective / Context / Human angle]
Quote style: [Formal / Conversational / Technical]
Length: [Word count, paragraph count]
Boilerplate length: [Word count]
What I'll take from this model:
[Specific elements I'll apply to my own release — not copy, but apply the principle]
Context: Priya is the Head of Marketing for a B2B analytics software company. Her company is announcing a new partnership with Salesforce that allows their analytics to be embedded natively in the Salesforce environment. She has clipped 12 press releases and coverage articles over the past year about B2B software partnerships in her space.
What her clip analysis showed:
Comparable release she reverse-engineered: "[Software Company] and Salesforce Expand Partnership to Bring [Specific Capability] to [N]+ Salesforce Customers" — led with the customer count; cited an IDC figure on the market problem; two quotes (one per company); covered in 4 trade publications.
Before (release without clip study):
CompanyName Announces Partnership with Salesforce
CITY, Date — CompanyName today announced a partnership with Salesforce that will allow CompanyName's analytics to be embedded in the Salesforce environment. This will provide Salesforce customers with access to CompanyName's analytics capabilities.
"We're excited to partner with Salesforce," said CEO. "This partnership will help our customers."
No specifics; no reason it's newsworthy; no data; quote adds nothing; won't be covered.
After (release modeled on clip study):
FOR IMMEDIATE RELEASE
CompanyName and Salesforce Bring Revenue Intelligence to 150,000+ Salesforce CRM Users Without Switching Tabs
Native integration eliminates the context-switching problem cited by 67% of revenue operations teams
CITY, Date — CompanyName, the revenue intelligence platform for mid-market sales teams, today announced a native integration with Salesforce that embeds [CompanyName]'s analytics directly within the Salesforce CRM environment. Revenue operations teams and sales leaders at companies using both platforms can now access pipeline analytics, deal scoring, and revenue forecasting without leaving Salesforce.
The integration directly addresses a workflow problem the companies' shared customer research has consistently identified: 67% of revenue operations leaders at mid-market companies in a 2024 [CompanyName]-commissioned survey cited context-switching between their CRM and analytics tools as a top productivity barrier — averaging 2.3 hours per week per revenue operations team member.
"The Salesforce environment is where our customers run their revenue operations — it was always counterproductive to ask them to leave it to access analytics," said [Name], CEO of CompanyName. "This integration means [CompanyName]'s intelligence is where decisions get made, not in a separate tab."
[Second quote from Salesforce partner contact, if available]
[Details on how the integration works, what it enables, how to access it]
The native integration is available immediately for companies on CompanyName's Professional and Enterprise plans. Existing customers can enable it through [setup path]. A free trial is available for Salesforce AppExchange users at [URL].
About CompanyName [60-word boilerplate in the style observed in comparable covered releases]
Contact: [Details]
The specific headline format was modeled on a comparable covered release; the customer-first framing came from the coverage pattern analysis; the data point (67%) was validated against what data types get citations in this space.
Beyond structural analysis, your clip collection is also a voice and tone reference. Different trade publications have different standards:
The releases that got covered in your target publications reveal what voice and tone those editors accept. Study the language in clipped coverage of your competitors: what did the journalist use directly from the release? What did they rewrite? What they used is what worked.
I'm writing a press release for: [What is being announced]
My clip collection includes: [N releases, N coverage articles — type: product launches / partnerships / funding / research]
My target publication(s): [Where I want coverage]
Clip analysis findings:
What gets covered in my space: [Types of announcements, based on analysis]
Headline structure pattern: [What makes headlines in my sector — specific claim / customer count / market number]
Data type journalists cite: [Market research / Customer stats / Survey data — which sources appear in covered releases]
Preferred length in my space: [Word range from analysis]
Boilerplate style for comparable companies: [Standard language and length]
Comparable release I'm modeling:
Release: [Company, Product, Date]
What made it get covered: [Key elements from analysis]
What I'm taking from this model: [Structural elements to apply — not to copy]
My announcement specifics:
What is being announced: [...]
What problem it solves: [...]
What is distinctive: [...]
Data point to use: "[Specific finding]" — Source: [Organization, Year]
Spokesperson: [Name, Title] — Quote: "[Approved quote text]"
Boilerplate: "[Standard org description]"
Customer count/scale: [If relevant for the headline]
Draft a press release that:
- Headline follows the structure that gets coverage in my space (from analysis)
- Lead paragraph leads with customer benefit, not technical detail
- Body paragraph 1: Research context / significance (one specific cited data point)
- Body paragraph 2: Announcement details
- Quote: Adds perspective, doesn't restate the lead
- Boilerplate: Matches length and style of comparable companies in my clip collection
- Closes with: ### and contact information
Style guidance from clips:
[Note specific voice/tone observations from clip analysis]
A press release written from your clipped articles reflects what you've observed about what works in your industry's coverage environment — not what you've assumed. The clip analysis is the preparation: identifying the patterns that determine coverage, finding the most comparable release to model structurally, and calibrating your headline, data point, and boilerplate to match what your target publications have shown they accept. The writing that follows is more confident because it's grounded in evidence of what journalists in your space actually run.
To go deeper, check out The Personal Knowledge Management Guide.
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