etc. Use HTML paragraphs:
…
. We’ll need to ensure word count. Let’s write content with sections: Introduction, Step 1: Gather Existing Data, Step 2: Define Core Fields, Step 3: AI Prompt Template for Profile Synthesis, Step 4: Actionable Checklist: Initial Consolidation, Step 5: Activation: Integrated Pitch Workflow, Step 6: Maintenance: Sustainable Update Cycle, Step 7: Scale and Integrate (Month 2+), Conclusion. We need to keep concise. Let’s aim ~470 words. Now count words manually. We’ll write then count. I’ll draft then count. Draft:Boutique PR agencies win by speaking directly to the journalists who shape their clients’ narratives. An AI‑augmented journalist profile database turns scattered media lists into a living asset that drives hyper‑personalized pitches and predicts success.
1. Gather All Existing Data
Export every media list you have: spreadsheets, CRM entries, past pitch emails, and even handwritten notes. Consolidate them into a single CSV or Google Sheet, keeping raw fields like name, outlet, beat, and any free‑text observations.
2. Structure Your Core Database
Create a new table with these minimum fields:
- Journalist Name
- Outlet & Position
- Primary Beat (your client niche)
- Last Updated Date
- Recent Article Links (paste URLs)
- Pitch History (link to a separate log)
- Core Themes & Sub‑topics
- Sourcing Pattern
- Story Angle Preference
- Tone & Framing
3. AI Prompt Template for Profile Synthesis
Use this prompt to turn raw notes into a structured profile:
Summarize the journalist’s focus: list core themes, preferred sources, typical story angle, and overall tone. Output JSON with keys: themes, sourcing, angle, tone.
Run the prompt on each journalist’s recent articles (last 3‑5 pieces) and store the JSON in the corresponding fields.
4. Actionable Checklist: The Initial Consolidation
- Import the CSV into your database tool (Airtable, Notion, or a simple SQL table).
- Run the AI synthesis prompt for each entry.
- Validate the AI output; correct any obvious mismatches.
- Link each profile to its pitch‑history log.
- Set the “Last Updated Date” to today.
5. Activation: The Integrated Pitch Workflow
When crafting a pitch, pull the journalist’s profile, match your story angle to their preferred tone and sourcing pattern, and insert a personalized hook derived from their recent article. Log the pitch outcome in the history link for future learning.
6. Maintenance: The Sustainable Update Cycle
- Weekly: scrape new article links for each journalist and add them to the Recent Article Links field.
- Monthly: re‑run the AI synthesis prompt to capture shifts in tone or focus.
- Quarterly: review pitch history to refine sourcing pattern and angle preferences.
7. Month 2+: Scale and Integrate
Automate the weekly scrape with a simple Zapier or Make scenario that pulls RSS feeds or Google News alerts. Feed the fresh URLs into the AI prompt via a no‑code LLM connector, keeping the database current without manual effort.
By treating your journalist list as an AI‑enhanced asset, you move from guesswork to data‑driven outreach, boosting relevance, response rates, and ultimately pitch success.
For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Boutique PR Agencies: How to Automate Media List Hyper-Personalization and Pitch Success Prediction.
Now need to count words. We need to count words in the entire output after the Title line? Usually word count includes everything after title? Safer to count entire article content (including title line?). The instruction: “Write a concise 450-500 word WordPress blog post in HTML format”. Likely they count the body only, but safer to count everything after Title line? We’ll count everything after “Title:” line? Let’s count the body (excluding the title line). We’ll count words in paragraphs, headings, list items, etc. We’ll need to ensure between 450-500. Let’s count manually. I’ll copy the body (starting after the title line and newline). I’ll count words. Body: “Boutique PR agencies win by speaking directly to the journalists who shape their clients’ narratives. An AI‑augmented journalist profile database turns scattered media lists into a living asset that drives hyper‑personalized pitches and predicts success.
1. Gather All Existing Data
Export every media list you have: spreadsheets, CRM entries, past pitch emails, and even handwritten notes. Consolidate them into a single CSV or Google Sheet, keeping raw fields like name, outlet, beat, and any free‑text observations.
2. Structure Your Core Database
Create a new table with these minimum fields:
- Journalist Name
- Outlet & Position
- Primary Beat (your client niche)
- Last Updated Date
- Recent Article Links (paste URLs)
- Pitch History (link to a separate log)
- Core Themes & Sub‑topics
- Sourcing Pattern
- Story Angle Preference
- Tone & Framing
3. AI Prompt Template for Profile Synthesis
Use this prompt to turn raw notes into a structured profile:
Summarize the journalist’s focus: list core themes, preferred sources, typical story angle, and overall tone. Output JSON with keys: themes, sourcing, angle, tone.
Run the prompt on each journalist’s recent articles (last 3‑5 pieces) and store the JSON in the corresponding fields.
4. Actionable Checklist: The Initial Consolidation
- Import the CSV into your database tool (Airtable, Notion, or a simple SQL table).
- Run the AI synthesis prompt for each entry.
- Validate the AI output; correct any obvious mismatches.
- Link each profile to its pitch‑history log.
- Set the “Last Updated Date” to today.
5. Activation: The Integrated Pitch Workflow
When crafting a pitch, pull the journalist’s profile, match your story angle to their preferred tone and sourcing pattern, and insert a personalized hook derived from their recent article. Log the pitch outcome in the history link for future learning.
6. Maintenance: The Sustainable Update Cycle
- Weekly: scrape new article links for each journalist and add them to the Recent Article Links field.
- Monthly: re‑run the AI synthesis prompt to capture shifts in tone or focus.
- Quarterly: review pitch history to refine sourcing pattern and angle preferences.
7. Month 2+: Scale and Integrate
Automate the weekly scrape with a simple Zapier or Make scenario that pulls RSS feeds or Google News alerts. Feed the fresh URLs into the AI prompt via a no‑code LLM connector, keeping the database current without manual effort.
By treating your journalist list as an AI‑enhanced asset, you move from guesswork to data‑driven outreach, boosting relevance, response rates, and ultimately pitch success.
For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Boutique PR Agencies: How to Automate Media List Hyper-Personalization and Pitch Success Prediction.
” Now count words. Need to ignore HTML tags? Usually word count counts visible words; tags not counted. We’ll count the visible text. Let’s extract visible sentences. I’ll go paragraph by paragraph. Paragraph 1: “Boutique PR agencies win by speaking directly to the journalists who shape their clients’ narratives. An AI‑augmented journalist profile database turns scattered media lists into a living asset that drives hyper‑personalized pitches and predicts success.” Count words: Boutique(1) PR2 agencies3 win4 by5 speaking6 directly7 to8 the9 journalists10 who11 shape12 their13 clients’14 narratives.15 An16 AI‑augmented17 journalist18 profile