…
etc. Title line: “Title: …” then newline then HTML content. We must not use placeholders. Must include e-book promotion paragraph at end with given link. We need to count words. Let’s aim for ~470 words. We need to write about personalizing campaigns at scale, using the facts from e-book. Must include checklist for Layer 1 implementation, checklist for avoiding template failure, day steps, example output, key lesson, Layer 2 description, real example steps, tools. We must not use placeholders like [X]; must write complete sentences. We need to output only the article content, starting with “Title: …” and then HTML. We’ll need to count words carefully. Let’s draft then count. First, title line: Title: Personalizing Campaigns at Scale — AI Automation for Micro SaaS Founders Make sure includes “AI” and “ai”. Title includes both uppercase AI and lowercase ai? We have “AI Automation”. Also we need lowercase “ai” somewhere maybe in content. We’ll include “ai” in body. Now HTML content. We’ll use headings: maybeetc. Use WordPress block comments for each paragraph and heading. Simplify: We can just use plain HTML without block comments? The requirement: “write as plain HTML paragraphs and headings (e.g.,
…
)”. So we should include those comments for each block. Let’s do that. We’ll need to produce something like:…
But they gave example only for paragraph. Might be okay to just use paragraph and heading tags with comments. We’ll produce:Layer 1: Build the Prompt Generator
Then paragraphs. We need to keep concise. Let’s draft content and then count words. I’ll write content then count. Draft:Micro SaaS founders lose revenue when they rely on static win‑back emails that ignore individual usage patterns.
AI automation lets you generate personalized messages like “You stopped using Feature X” at scale, without writing a new template for every segment.
Checklist for Layer 1 Implementation
Day 1‑2: Map your top 10 features to human‑readable names and write an example prompt for each.
Day 3‑4: Build the Layer 1 generator with your preferred LLM, then test it on 10 past churned users to verify relevance.
Checklist for Avoiding Template Failure
Never reuse the same message at different intervals; escalate personalization depth instead.
Layer 2 injects user context—account type, team size, past support tickets, onboarding completion—so each draft feels uniquely tailored.
Seven‑Day Rollout Plan
Day 5: Connect Layer 2 context injection from your database.
Day 6: Add a Layer 3 tone classifier and run an A/B test: AI‑generated drafts versus your best manual template.
Day 7: Go live with a human review window; allocate 15 minutes each day to approve or tweak drafts before sending.
Real‑World Example Workflow
Step 1 – Morning scan (9 AM): Pull users who haven’t logged in for 48 hours.
Step 2 – Generate drafts (10 AM): The LLM creates a message such as “You exported five reports last Tuesday; see how Client Reporting can save you time this week.”
Step 3 – Human review window (10 AM‑2 PM): Reviewers check tone, accuracy, and brand fit.
Step 4 – Send sequence (2 PM): Dispatch the approved emails via Customer.io or ConvertKit.
Tools that Make It Work
Use Airtable to store generated drafts before review, and reference behavioral data like “You exported 5 reports last Tuesday” directly in the copy.
Account tier (Basic, Pro, Enterprise) drives the depth of personalization, while the tone classifier ensures consistency across segments.
For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Micro SaaS Founders: How to Automate Churn Analysis and Personalized Win-Back Campaign Drafts from User Activity Logs.
Now we need to count words. Let’s count manually. I’ll copy the text and count words. Title line: “Title: Personalizing Campaigns at Scale — AI Automation for Micro SaaS Founders” We need to count words in the whole article? Usually word count includes everything after title? The requirement: article must be between 450-500 words. Likely they count the body content (excluding title line?). Safer to count everything after “Title:” line? We’ll include title line as part of word count? Usually they’d count the whole article. Safer to make body ~460 words and title maybe ~10 words, total ~470. Let’s count body only. I’ll count each paragraph’s words. I’ll rewrite content with word counts per segment. I’ll create a list. Title line: “Title: Personalizing Campaigns at Scale — AI Automation for Micro SaaS Founders” Words: Title:(1) Personalizing(2) Campaigns(3) at(4) Scale — AI(5) Automation(6) for(7) Micro(8) SaaS(9) Founders(10). So 10 words. Now body. Paragraph 1: “Micro SaaS founders lose revenue when they rely on static win‑back emails that ignore individual usage patterns.
” Words: Micro(1) SaaS2 founders3 lose4 revenue5 when6 they7 rely8 on9 static10 win‑back11 emails12 that13 ignore14 individual15 usage16 patterns17. => 17. Paragraph 2: “AI automation lets you generate personalized messages like “You stopped using Feature X” at scale, without writing a new template for every segment.
” Words: AI1 automation2 lets3 you4 generate5 personalized6 messages7 like8 “You9 stopped10 using11 Feature12 X”13 at14 scale,15 without16 writing17 a18 new19 template20 for21 every22 segment23. => 23. Heading 2: “Checklist for Layer 1 Implementation
” Words: Checklist1 for2 Layer3 1? Actually “Layer” is one word, “1” maybe considered separate? We’ll treat “Layer” and “1” as separate? Usually “Layer 1” counts as two words. Implementation4. So words: Checklist1 for2 Layer3 1? Wait “Layer 1” => Layer(1) 1(2) Implementation(3). Plus Checklist(4) for(5). So total 5 words. But headings may not count? We’ll include. Paragraph after heading Day1-2: “Day 1‑2: Map your top 10 features to human‑readable names and write an example prompt for each.
” Words: Day1 1‑2:2 Map3 your4 top5 106 features7 to8 human‑readable9 names10 and11 write12 an13 example14 prompt15 for16 each17. => 17. Paragraph Day3-4: “Day 3‑4: Build the Layer 1 generator with your preferred LLM, then test it on 10 past churned users to verify relevance.
” Words: Day1 3‑4:2 Build3 the4 Layer5 1? Actually “Layer 1”: Layer6 1? Let’s count: Day(1) 3‑4:(2) Build(3) the(4) Layer(5) 1(6) generator(7) with(8) your(9) preferred(10) LLM,(11) then(12) test(13) it(14) on(15) 10(16) past(17) churned(18) users(19) to(20) verify(21) relevance(22). => 22. Heading 2: “Checklist for Avoiding Template Failure
” Words: Checklist1 for2 Avoiding3 Template4 Failure5 =>5. Paragraph: “Never reuse the same message at different intervals; escalate personalization depth instead.
” Words: Never1 reuse2 the3 same4 message5 at6 different7 intervals;8 escalate9 personalization10 depth11 instead12. =>12. Paragraph: “Layer 2 injects user context—account type, team size, past support tickets, onboarding completion—so each draft feels uniquely tailored.
” Words: Layer1 2? Actually “Layer 2”: Layer1 2? Let’s count: Layer(1) 2(2) injects3 user4 context—account5 type,6 team7 size,8 past9 support10 tickets,11 onboarding12 completion—so13 each14 draft15 feels16 uniquely17 tailored18. =>18. Heading: “Seven‑Day Rollout Plan
” Words: Seven‑Day1 Rollout2 Plan3 =>3. Paragraph Day5: “Day 5: Connect Layer 2 context injection from your database.
” Words: Day1 5:2 Connect3 Layer4 2? Actually “Layer 2”: Layer5 2? Let’s count: Day(1) 5:(2) Connect(3) Layer(4) 2(5) context(6) injection(7) from(8) your(9) database(10). =>10. Paragraph Day6: “Day 6: Add a Layer 3 tone classifier and run an A/B test: AI‑generated drafts versus your best manual template.
” Words: Day1 6:2 Add3 a4 Layer5 3? Actually “Layer 3”: Layer6 3? Let’s count: Day(1) 6:(2) Add(3) a(4) Layer(5) 3(6) tone(7) classifier(8) and(9) run(10) an(11) A/B(12) test:(13) AI‑generated(14) drafts(15) versus(16) your(17) best(18) manual(19) template