Add plain-English sales doc
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docs/sales/instagram_dm_revenue_plan_plain_english.md
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docs/sales/instagram_dm_revenue_plan_plain_english.md
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# Instagram DM Revenue Plan (Plain English)
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**Inbox:** `@socialmediatorr`
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**Time zone used in this document:** CET
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**Purpose:** turn more DM interest into paid outcomes, without lowering trust
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---
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## The Short Version
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Most people who write to you are asking for one of a small set of things. The biggest one is the book.
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If we answer those repeat questions fast, clearly, and in the person’s language, you should get more sales and more booked calls from the same message volume.
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We can prove this safely by running a “draft only” test first: the new system writes the reply, but never sends it. We then compare it to what the current system actually sent.
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---
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## What We Measured (From Your Export)
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This is what was in the Instagram export we scanned. It is a count of patterns, not a money ledger.
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| Item | Count |
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|---|---:|
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| Total messages | 54,069 |
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| Messages you sent | 43,607 |
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| Messages people sent you | 10,462 |
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| Messages that look like a question or request | 2,715 |
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| Time window covered | 2024-10-20 → 2025-12-22 |
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---
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## The Main Business Signal
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People keep asking for the same thing. The biggest topic is the book.
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| Rank | What people ask for (simple wording) | Count |
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|---:|---|---:|
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| 1 | “Book” (often just one word) | 1,857 |
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| 2 | “What is this?” | 203 |
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| 3 | “Send the video” | 189 |
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| 4 | Other question | 118 |
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| 5 | “Can you help me?” | 74 |
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This is the split:
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```mermaid
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pie title Questions/Requests: Book vs Everything Else
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"Book" : 1893
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"Everything else" : 822
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```
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Why this matters: if a person writes “book”, they are already close to taking a next step. Slow or unclear replies at this moment lose money.
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---
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## When Replies Arrive (CET)
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Most inbound messages arrive in two time blocks. This is when fast replies matter most.
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| Time block (CET) | Messages from people |
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|---|---:|
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| 00:00–05:59 | 2,113 |
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| 06:00–11:59 | 1,274 |
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| 12:00–17:59 | 2,333 |
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| 18:00–23:59 | 4,742 |
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```mermaid
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pie title Messages From People by Time of Day (CET)
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"00:00-05:59" : 2113
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"06:00-11:59" : 1274
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"12:00-17:59" : 2333
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"18:00-23:59" : 4742
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```
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Practical meaning: if you can cover **12:00–17:59** and **18:00–23:59**, you will catch most of the “book” and “link” requests while they still care.
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---
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## Language Reality (What People Actually Write In)
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People mostly write in English and Spanish, with some French and a small amount of Catalan.
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Many messages are too short (one word, emoji, or “book”), so we do not try to guess the language for those.
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Rule we should follow:
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- Reply in the same language the person used in their last clear message.
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- If the message is too short to tell, keep the last known language in that thread.
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- If still unclear, ask one short question: “English or Spanish?” (or include French/Catalan if needed).
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---
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## What We Will Change (In Simple Terms)
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This is not “more messages”. It is better answers at the moments that matter.
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### 1) Fast answers for the Top 20 questions
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We already wrote 20 ready-made answers (English/Spanish/French/Catalan) for the most common questions.
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Result: fewer people asking twice, and fewer “where is it?” messages.
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### 2) A clean “book” path
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When someone says “book”, the system should:
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1) confirm what they want (book link or video first)
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2) send the correct link
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3) ask one short next question
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Result: more people finish the step instead of stalling.
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### 3) A simple follow-up when people go silent
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If there is no reply after 24–48 hours, send one short follow-up (not a long paragraph).
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Result: you recover sales that would otherwise die in silence.
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---
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## What We Will Not Automate
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This protects trust and reduces risk.
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- Anything that looks like crisis or self-harm.
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- Anything that needs clinical nuance in DMs.
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- Anything involving sensitive personal data.
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- Anything that turns into an argument.
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In these cases, the system should stop and ask you to take over (or move it to a call).
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---
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## How We Prove It Without Replying to Real Clients
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We run a “draft only” test first.
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The new system writes a draft reply, stores it, and does not send it. Then we compare it to what the current system actually sent.
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```mermaid
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flowchart LR
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A1["A new DM arrives"] --> A2["Current system sends its reply (unchanged)"]
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A1 --> B1["New system writes a draft reply (not sent)"]
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A2 --> C1["We save the real reply"]
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B1 --> C2["We save the draft reply"]
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C1 --> D1["We compare both in a table"]
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C2 --> D1
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```
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The comparison table can include:
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- time (CET)
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- message topic (book / link / price / video / other)
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- the reply the person actually got
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- the draft reply we would have sent
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- a simple quality score (clear? short? correct language? correct next step?)
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---
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## The Money Question (A Clean Way to Estimate It)
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We should not guess revenue from message keywords alone. The right way is:
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1) tag links we send (so we can see if they were used)
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2) match payments/bookings back to the DM thread (so we can see what actually worked)
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Until we wire that up, here is a conservative estimate framework you can fill in:
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| Scenario | Extra people who buy/book per month | Average value per sale/call (€) | Extra revenue per month (€) |
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|---|---:|---:|---:|
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| Conservative | 10 | | |
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| Expected | 30 | | |
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| Strong | 70 | | |
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How to fill it in:
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- “Extra people who buy/book” should come from the “draft only” test + a small controlled rollout.
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- “Average value” depends on whether the outcome is mainly book sales, calls, or a mix.
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---
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## What I Need From You (To Finish This Properly)
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- the exact book link you want to use
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- the “video link” you want to use
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- the pricing you want quoted in DMs (if any)
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- whether you want “book” to go to book-first, video-first, or a choice
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- what you consider a “win” (book sale, call booked, paid program, etc.)
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@ -61,7 +61,7 @@ function detectDiagramType(code) {
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async function main() {
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const args = process.argv.slice(2);
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const roots = args.length ? args : ["reports"];
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const roots = args.length ? args : ["reports", "docs"];
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let ok = true;
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let total = 0;
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