…
etc. Also can use headings like…
. We need to embed the facts from e-book: flags, fixes, journalist profile, outlet authority, recency/frequency, tone & narrative alignment, topic resonance, social sentiment flags, identify journalists who have written about postpartum fitness, wearable tech in health, mental recovery, surface narrative preferences. We need to include sections: Red Flags & How to Fix Them, Step 1: Input the “Seed” – Your Client’s Story Angle, Step 2: Activate Your AI-Augmented Database, Step 3: Generate the Ranked Media List, Output Example maybe. We need to promote e-book at the end with given paragraph. Word count: 450-500 words inclusive. Need to count words. Let’s draft about 470 words. We must not include any thinking process. Just output. Let’s craft. First line: Title: Hyper-Personalization in Action: From Story Angle to Ranked Media List in Minutes Then newline. Then HTML content. We’ll use headings and paragraphs. Let’s draft content ~470 words. We need to count words manually. I’ll write then count. Draft:Why Generic Pitches Fail
Generic compliments like “I love your work” get ignored. The fix is to require article‑specific praise that includes a brief “why.”
Referencing a story from five + years ago signals laziness. Set your AI’s recency parameter to prioritize coverage from the last 12‑18 months.
Journalist Profile Example
Consider a reporter who covers hard climate policy and finance. To rank them, evaluate:
- Outlet Authority & Client Fit – does the outlet’s audience mirror your client’s target demographic?
- Recency & Frequency – are they actively covering this beat now?
- Tone & Narrative Alignment – does their writing style match your story’s format (investigative, trend‑piece, how‑to)?
- Topic Resonance – how closely do past coverage keywords align with your angle?
Red Flags & How to Fix Them
Flag journalists whose recent social sentiment (mined from X/LinkedIn) shows frustration with generic “fitness tech” pitches. Replace vague flattery with a concrete reference to their latest piece on, say, postpartum fitness or wearable tech in health, explaining why it resonated with you.
We need to continue. Let’s rewrite more systematically, counting words. I’ll write the full article then count. I’ll produce in a text editor mentally. Let’s start. Title line: “Title: Hyper-Personalization in Action: From Story Angle to Ranked Media List in Minutes” Then newline. Now HTML. I’ll write:Why Generic Pitches Fail
Generic compliments like “I love your work” get ignored. The fix is to require article‑specific praise that includes a brief “why.”
Referencing a story from five + years ago signals laziness. Set your AI’s recency parameter to prioritize coverage from the last 12‑18 months.
Journalist Profile Example
Consider a reporter who covers hard climate policy and finance. To rank them, evaluate:
- Outlet Authority & Client Fit – does the outlet’s audience mirror your client’s target demographic?
- Recency & Frequency – are they actively covering this beat now?
- Tone & Narrative Alignment – does their writing style match your story’s format (investigative, trend‑piece, how‑to)?
- Topic Resonance – how closely do past coverage keywords align with your angle?
Red Flags & How to Fix Them
Flag journalists whose recent social sentiment (mined from X/LinkedIn) shows frustration with generic “fitness tech” pitches. Replace vague flattery with a concrete reference to their latest piece on postpartum fitness, wearable tech in health, or mental recovery, explaining why it resonated with you.
Use the AI to surface each journalist’s narrative preference: do they favor data‑driven stories, personal‑journey profiles, or expert roundups? Tailor the pitch angle accordingly.
Step 1: Input the “Seed” – Your Client’s Story Angle
For a climate‑tech client (e.g., a new carbon‑sequestration startup), the seed might be: “Our startup uses enhanced rock weathering for carbon removal.”
Step 2: Activate Your AI‑Augmented Database
Feed the seed into the AI‑enhanced media database. The system applies the recency filter (12‑18 months), scores outlet authority, checks topic resonance, and pulls each journalist’s recent X/LinkedIn sentiment and narrative preference.
Step 3: Generate the Ranked Media List
The AI outputs a ranked list, highlighting the top matches. For each, it provides:
- Article‑specific compliment with a brief “why.”
- Suggested pitch angle aligned with the journalist’s preferred format.
- Predicted success score based on tone, recency, and sentiment.
Output Example
Journalist: Maya Lin (Climate Policy Reporter, GreenFinance Times)
Compliment: “Your recent analysis of the EU carbon‑border tax (June 2024) clearly broke down the policy impact on heavy industry – exactly the context our enhanced rock weathering solution needs.”
Pitch Angle: Data‑driven story showing pilot‑scale CO₂ removal metrics paired with policy implications.
Success Score: 87/100.
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 we need to count words. Let’s count manually. I’ll copy the text and count words. Title line: “Title: Hyper-Personalization in Action: From Story Angle to Ranked Media List in Minutes” Words: Title:(1) Hyper-Personalization(2) in(3) Action:(4) From(5) Story(6) Angle(7) to(8) Ranked(9) Media(10) List(11) in(12) Minutes(13). So 13 words. Now we need total 450-500 words including title? Usually they count the whole article. We’ll include title. Now count body. I’ll copy each paragraph and count. I’ll write a simplified version to count. Let’s list body content with word counts. I’ll rewrite body as plain text ignoring HTML tags for counting. Body text: “Why Generic Pitches Fail Generic compliments like “I love your work” get ignored. The fix is to require article‑specific praise that includes a brief “why.” Referencing a story from five + years ago signals laziness. Set your AI’s recency parameter to prioritize coverage from the last 12‑18 months. Journalist Profile Example Consider a reporter who covers hard climate policy and finance. To rank them, evaluate: Outlet Authority & Client Fit – does the outlet’s audience mirror your client’s target demographic? Recency & Frequency – are they actively covering this beat now? Tone & Narrative Alignment – does their writing style match your story’s format (investigative, trend‑piece, how‑to)? Topic Resonance – how closely do past coverage keywords align with your angle? Red Flags & How to Fix Them Flag journalists whose recent social sentiment (mined from X/LinkedIn) shows frustration with generic “fitness tech” pitches. Replace vague flattery with a concrete reference to their latest piece on postpartum fitness, wearable tech in health, or mental recovery, explaining why it resonated with you. Use the AI to surface each journalist’s narrative preference: do they favor data‑driven stories, personal‑journey profiles, or expert roundups? Tailor the pitch angle accordingly. Step 1: Input the “Seed” – Your Client’s Story Angle For a climate‑tech client (e.g., a new carbon‑sequestration startup), the seed might be: “Our startup uses enhanced rock weathering for carbon removal.” Step 2: Activate Your AI‑Augmented Database Feed the seed into the AI‑enhanced media database. The system applies the recency filter (12‑18 months), scores outlet authority, checks topic resonance, and pulls each journalist’s recent X/LinkedIn sentiment and narrative preference. Step 3: Generate the Ranked Media List The AI outputs a ranked list, highlighting the top matches. For each, it provides: Article‑specific compliment with a brief “why.” Suggested pitch angle aligned with the journalist’s preferred format. Predicted success score based on tone, recency, and sentiment. Output Example Journalist: Maya Lin (Climate Policy Reporter, GreenFinance Times) Compliment: “Your recent analysis of the EU carbon‑border tax (June 2024) clearly broke down the policy impact on heavy industry – exactly the context our enhanced rock weathering solution needs.” Pitch Angle: Data‑driven story showing pilot‑scale CO₂ removal metrics paired with policy implications. Success Score: 87/100. 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. I’ll count line by line. I’ll write each sentence and count. I’ll use a systematic approach. First paragraph: “Why Generic Pitches Fail” Words: Why(1) Generic(2) Pitches(3) Fail(4) => 4 Next sentence: “Generic compliments like “I love your work” get ignored.” Words: Generic(1) compliments2 like