Add detailed DM history report
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@ -135,6 +135,12 @@ After analysis, generate a single Markdown report:
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- `python3 -m sergio_instagram_messaging.generate_dm_report --analysis-dir /root/tmp/socialmediatorr-agent-analysis`
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### Plain-English deep report (Mermaid diagrams)
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Generate the deeper “no raw quotes” report directly from an Instagram export folder:
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- `python3 -m sergio_instagram_messaging.generate_dm_report_detailed --export-input /path/to/export-root --out /root/tmp/dm_history_report_en_detailed.md`
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## Webhooks (new messages → auto-reply)
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Meta webhooks are two steps:
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reports/socialmediatorr/dm_history_report_en_detailed.md
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reports/socialmediatorr/dm_history_report_en_detailed.md
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# Socialmediatorr Instagram DM History : Plain-English Deep Report
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## DM History Deep Report
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**Subject:** Instagram direct messages for `@socialmediatorr`
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**Version:** v1.0 (STYLE BIBLE EN 3.0GM)
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**Date:** 2025-12-24
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**Status:** REVIEW REQUIRED
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**Citation:** `if://report/socialmediatorr/instagram/dm-history/`
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**Author:** Danny Stocker | InfraFabric Research
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### How This Report Was Made
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> This is an automated count of patterns. It is not a therapy note and it is not a sales ledger.
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This document was generated by reading an Instagram data export and counting repeat patterns over time. It avoids quoting private client messages and it avoids storing personal identities.
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---
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**Context:** This inbox contains a high-volume message-and-reply system over 429 days.
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> Your messaging system is working as a volume engine. The weak point is consistency at the moments where people ask to buy or book.
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The purpose of this report is practical: define what to keep, what to remove, and what to automate safely—without damaging trust.
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### What Happened
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> This export shows a dense campaign period, not a quiet inbox.
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Across the observed window, you sent a very large number of messages and you received a smaller number of replies back. That is normal when you are messaging lots of people: many contacts, fewer responders.
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| Metric | Value | Source |
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|---|---:|---|
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| Observation window (earliest → latest) | 2024-10-20 → 2025-12-22 | Instagram export |
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| Total messages | 54,069 | Instagram export |
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| Messages you sent | 43,607 | Instagram export |
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| Messages people sent you | 10,462 | Instagram export |
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| Messages that look like a question or a request | 2,713 | Instagram export |
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| System messages about new followers (auto text in the inbox) | 8,081 | Instagram export |
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### What You Need to Know (In Plain English)
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> If you only read one section, read this one.
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This inbox has a few dominant patterns. They tell you what an auto-reply system must be good at, and where it must hand off to a human.
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| Thing to know | Why it matters |
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|---|---|
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| Most activity happened in **2025-12** | 47,315 messages in one month (87.5% of all messages in this export) |
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| The #1 question/topic is **Just one word: book** | 1,857 times (68.4% of all questions/requests) |
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| Questions/requests cluster on **Thursday, Friday** | Those two days contain most of the asking in this export |
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| Most messages arrive in **18:00-23:59, 12:00-17:59 (UTC)** | If you are present in those blocks, reply rates usually improve |
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| Repeat messages make up **67.6%** of your text messages | Fast replies are often repeats; custom replies are where delays happen |
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| Language used by people (approx) | Spanish 25.7%, English 18.1%, Unknown 56.2% |
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| Language used in your messages (approx) | Spanish 63.8%, English 29.2%, Unknown 7.0% |
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| Custom-reply slow end | 90% of custom replies are faster than **16h 36m** |
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### Key Patterns Over Time
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> The month-by-month shape is uneven. There are clear bursts.
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To avoid guesswork, we start with 3-month blocks (a simple way to smooth noise), then we go month-by-month.
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| 3-month block | Messages from people | Messages you sent | Questions/requests |
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|---|---:|---:|---:|
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| 2024 Oct-Dec | 14 | 0 | 0 |
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| 2025 Jan-Mar | 21 | 0 | 0 |
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| 2025 Apr-Jun | 93 | 100 | 16 |
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| 2025 Jul-Sep | 622 | 879 | 88 |
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| 2025 Oct-Dec | 9,712 | 42,628 | 2,609 |
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Same data as charts:
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```mermaid
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pie title Messages From People by 3-Month Block
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"2024 Oct-Dec" : 14
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"2025 Jan-Mar" : 21
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"2025 Apr-Jun" : 93
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"2025 Jul-Sep" : 622
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"2025 Oct-Dec" : 9712
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```
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This shows when people replied most. A spike here usually means you posted something or you asked people to DM you.
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```mermaid
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pie title Messages You Sent by 3-Month Block
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"2025 Apr-Jun" : 100
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"2025 Jul-Sep" : 879
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"2025 Oct-Dec" : 42628
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```
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This shows when you sent the most messages. A spike here is effort; the question is how many people replied back.
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### Month by Month (The Real Shape)
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> One big month dominates. Treat earlier months as less reliable.
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This month-by-month table is the clearest view of how the inbox changed over time in this export.
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| Month | Messages from people | Messages you sent | Questions/requests | Questions answered within 48 hours |
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|---|---:|---:|---:|---:|
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| 2024-10 | 3 | 0 | 0 | n/a |
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| 2024-11 | 4 | 0 | 0 | n/a |
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| 2024-12 | 7 | 0 | 0 | n/a |
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| 2025-01 | 14 | 0 | 0 | n/a |
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| 2025-02 | 2 | 0 | 0 | n/a |
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| 2025-03 | 5 | 0 | 0 | n/a |
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| 2025-04 | 8 | 5 | 0 | n/a |
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| 2025-05 | 48 | 28 | 8 | 12.5% |
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| 2025-06 | 37 | 67 | 8 | 87.5% |
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| 2025-07 | 145 | 319 | 36 | 63.9% |
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| 2025-08 | 193 | 230 | 28 | 50.0% |
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| 2025-09 | 284 | 330 | 24 | 20.8% |
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| 2025-10 | 787 | 1,190 | 64 | 17.2% |
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| 2025-11 | 854 | 2,194 | 149 | 46.3% |
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| 2025-12 | 8,071 | 39,244 | 2,396 | 89.6% |
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The busiest month was **2025-12** with **47,315** messages total (87.5% of everything in this export). That single month dominates the shape of the data.
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### Days People Reply
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> The best day to follow up is the day people already reply.
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Use this to time follow-ups and first messages. Do not spread effort evenly across the week.
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| Day of week | Messages from people | Messages you sent | Questions/requests |
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|---|---:|---:|---:|
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| Monday | 1,627 | 8,547 | 140 |
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| Tuesday | 1,952 | 9,622 | 189 |
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| Wednesday | 1,242 | 5,396 | 155 |
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| Thursday | 2,349 | 7,126 | 1,340 |
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| Friday | 1,610 | 5,494 | 728 |
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| Saturday | 840 | 3,579 | 88 |
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| Sunday | 842 | 3,843 | 73 |
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Same data as a chart:
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```mermaid
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pie title Messages From People by Day of Week
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"Monday" : 1627
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"Tuesday" : 1952
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"Wednesday" : 1242
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"Thursday" : 2349
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"Friday" : 1610
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"Saturday" : 840
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"Sunday" : 842
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```
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### Time of Day People Reply
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> Most replies happen in a few time blocks.
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Time zone here is UTC (a standard clock). If you work in another time zone, shift the blocks before you schedule.
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| Time of day (UTC) | Messages from people | Messages you sent |
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|---|---:|---:|
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| 00:00-05:59 | 1,885 | 8,304 |
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| 06:00-11:59 | 1,374 | 6,889 |
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| 12:00-17:59 | 3,092 | 12,937 |
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| 18:00-23:59 | 4,111 | 15,477 |
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Same data as a chart:
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```mermaid
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pie title Messages From People by Time of Day (UTC)
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"00:00-05:59" : 1885
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"06:00-11:59" : 1374
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"12:00-17:59" : 3092
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"18:00-23:59" : 4111
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```
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### Reply Speed (Why It Matters)
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> Speed changes the feeling of safety.
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When someone asks a question, the clock starts. A short, direct acknowledgment often beats a perfect answer that arrives too late.
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One caution: “fast replies” are often repeat messages. This section shows overall speed, then splits it into repeat messages vs custom messages.
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| Metric | Value | Source |
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|---|---:|---|
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| Typical time to reply | 4 seconds | Instagram export |
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| Slow end (90% are faster) | 34 seconds | Instagram export |
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| Typical time to reply to questions/requests | 2 seconds | Instagram export |
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| Slow end for questions/requests (90% are faster) | 4 seconds | Instagram export |
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| Messages from people answered within 48 hours | 7,467 (71.4%) | Instagram export |
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| Questions/requests answered within 48 hours | 2,278 (84.0%) | Instagram export |
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Breakdown by message type (repeat messages vs custom messages):
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| Type of message you sent (text only) | Count | Typical reply speed | Slow end (90% are faster) |
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|---|---:|---:|---:|
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| Repeat messages | 18,860 | 4 seconds | 32 seconds |
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| Custom messages | 9,040 | 12 seconds | 16h 36m |
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| No text (media/reactions) | 15,707 | n/a | n/a |
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| Type of message you sent (questions only) | Typical reply speed |
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|---|---:|
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| Repeat messages | 2 seconds |
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| Custom messages | 9 minutes |
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### Language Mix (What Language People Use)
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> Matching the other person’s language increases trust and reduces back-and-forth.
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This is an approximate language guess based on the text itself. Short one-word messages are harder to classify and may show up as “Unknown”.
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| Language | Messages from people | Messages you sent (text only) |
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|---|---:|---:|
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| Spanish | 2,662 (25.7%) | 17,804 (63.8%) |
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| English | 1,870 (18.1%) | 8,136 (29.2%) |
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| Unknown | 5,814 (56.2%) | 1,961 (7.0%) |
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Language split chart (messages from people):
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```mermaid
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pie title Language Split (Messages From People)
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"Spanish" : 2662
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"English" : 1870
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"Unknown" : 5814
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```
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Language split chart (messages you sent):
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```mermaid
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pie title Language Split (Messages You Sent)
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"Spanish" : 17804
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"English" : 8136
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"Unknown" : 1961
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```
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### Top 20 Things People Ask or Type (Ranked)
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> People repeat the same questions. This is the easiest thing to standardize.
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This list is grouped by meaning (not by exact wording). It includes very short requests (sometimes a single word).
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| Rank | Topic (plain English) | Count | Share of all questions/requests |
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|---:|---|---:|---:|
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| 1 | Just one word: book | 1,857 | 68.4% |
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| 2 | What is this? | 206 | 7.6% |
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| 3 | Can you send the video? | 191 | 7.0% |
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| 4 | Other question | 120 | 4.4% |
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| 5 | Can you help me? | 74 | 2.7% |
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| 6 | Can you send the link? | 61 | 2.2% |
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| 7 | What does it cost? | 53 | 2.0% |
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| 8 | Is this therapy? | 44 | 1.6% |
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| 9 | Where do I get the book? | 36 | 1.3% |
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| 10 | I can’t find it / it didn’t arrive | 26 | 1.0% |
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| 11 | How do I book a call? | 11 | 0.4% |
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| 12 | How do I start? | 10 | 0.4% |
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| 13 | Can we talk on WhatsApp? | 7 | 0.3% |
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| 14 | How does it work? | 5 | 0.2% |
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| 15 | What are the steps? | 4 | 0.1% |
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| 16 | Is this real? | 4 | 0.1% |
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| 17 | Is it free? | 2 | 0.1% |
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| 18 | Can I get a refund? | 1 | 0.0% |
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| 19 | How long does it take? | 1 | 0.0% |
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In plain terms: **1,893** of **2,713** questions/requests are about the book (69.8%).
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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" : 820
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```
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### Content Patterns (What You Mention When You Sell)
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> Content is not random. It leaves fingerprints in the inbox.
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We track certain words over time that usually show up when you are giving someone a next step (book, video, WhatsApp, call, payment, etc). This lets you see what dominated each period, without reading private conversations.
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| Word found in your messages | Mentions | Source |
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|---|---:|---|
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| Book | 1,915 | Instagram export (message text) |
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| Ebook | 1,912 | Instagram export (message text) |
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| Video | 1,124 | Instagram export (message text) |
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| Workshop | 383 | Instagram export (message text) |
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| Call | 32 | Instagram export (message text) |
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| Course | 31 | Instagram export (message text) |
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| YouTube | 18 | Instagram export (message text) |
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| Training | 13 | Instagram export (message text) |
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| Calendly | 7 | Instagram export (message text) |
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| WhatsApp | 6 | Instagram export (message text) |
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```mermaid
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flowchart LR
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Q_2024_Q4["2024 Oct-Dec: No signals"]
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Q_2025_Q1["2025 Jan-Mar: No signals"]
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Q_2025_Q2["2025 Apr-Jun: Video (4), Training (3), Course (2)"]
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Q_2025_Q3["2025 Jul-Sep: Video (28), Training (6), Platform (1)"]
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Q_2025_Q4["2025 Oct-Dec: Book (1915), Ebook (1912), Video (1092)"]
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Q_2024_Q4 --> Q_2025_Q1
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Q_2025_Q1 --> Q_2025_Q2
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Q_2025_Q2 --> Q_2025_Q3
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Q_2025_Q3 --> Q_2025_Q4
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```
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This diagram is a high-level view of what you talked about most in each period (based on those words).
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### Follow-Ups (When People Do Not Reply)
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> Silence is where most conversations die.
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This section measures the time between two messages you sent in a row **when the other person did not reply in between**. Very short gaps are usually multi-part scripts. Longer gaps are true follow-ups.
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| Time gap between two messages you sent | Count | Share |
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|---|---:|---:|
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| Under 1 minute | 23,579 | 85.6% |
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| 1-10 minutes | 2,656 | 9.6% |
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| 10-60 minutes | 266 | 1.0% |
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| 1-6 hours | 18 | 0.1% |
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| 6-24 hours | 899 | 3.3% |
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| 1-3 days | 15 | 0.1% |
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| Over 3 days | 128 | 0.5% |
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Same data as a chart:
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```mermaid
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pie title Time Between Two Messages You Sent (No Reply In Between)
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"Under 1 minute" : 23579
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"1-10 minutes" : 2656
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"10-60 minutes" : 266
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"1-6 hours" : 18
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"6-24 hours" : 899
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"1-3 days" : 15
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"Over 3 days" : 128
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```
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### Recommended Actions (Concrete, Ranked)
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> Most improvements are not “more messages.” They are better timing and cleaner answers.
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These actions are intentionally practical. Each one can be implemented without changing your tone.
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| Priority | Action | Why it matters | How to check it worked |
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|---:|---|---|---|
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| 1 | Write 20 ready-made answers for the Top 20 list | Stops delays and confusion on the most repeated questions | Fewer follow-up messages like “I can’t find it” and faster replies on question days |
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| 2 | Fix the book steps from start to finish (book → link → 1 question) | The book dominates the inbox; this must have no extra steps | People stop asking twice; fewer broken-link complaints |
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| 3 | Add one short “I saw this” reply for busy hours | Keeps the person engaged even if you are not ready to write a full reply | More conversations continue instead of going silent |
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| 4 | Be present on the top question days | Most questions cluster on a small set of days | Higher question reply rate on those days |
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| 5 | Mirror language by default (English ↔ Spanish) | Reduces misunderstandings and builds trust | Fewer “What?” / “Explain” messages; more smooth back-and-forth |
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| 6 | Add a follow-up rule when people go silent | Many sales die in silence; a simple follow-up rescues them | More replies after 24–48 hours; fewer dead threads |
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| 7 | Add a “can’t find it / didn’t arrive” reply | This moment often creates distrust | Complaints resolve quickly; fewer repeated requests |
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| 8 | Keep deep clinical discussion out of DMs | DMs are a bad place for nuance and risk | Shorter DM threads; more booked calls when needed |
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| 9 | Add hard safety rules (crisis → human) | Automation must never handle high-risk situations | Fewer risky back-and-forth messages; clear handoff |
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| 10 | Connect real outcomes (payments/bookings) to a tracker | Without it, you can’t tell what actually worked | You can answer: “Which messages lead to paid outcomes?” |
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### What You Do Not Need to Know
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> Privacy keeps you safe. You do not need to memorize people to help them.
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To run an auto-reply system safely, you only need patterns, not personal identities. Storing extra detail increases risk without improving results.
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| Do not store | Why it is unnecessary |
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|---|---|
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| Names, handles, phone numbers, emails | Not needed for pattern-based replies; increases privacy risk |
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| Full message transcripts for every thread | You only need grouped question themes + best replies |
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| Photos/videos/audio attachments | High risk and high storage cost; not required for reply style |
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| One-off edge cases | Build rules for repeated situations; escalate rare cases to a human |
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### Decision Map for Safe Auto-Replies
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> A safe system is not smart. It is consistent.
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This is a simple decision map that an auto-reply system can follow. The goal is to answer quickly, stay consistent, and avoid risky situations.
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```mermaid
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flowchart LR
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A1["New message"] --> A2{"What is it?"}
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A2 -->|"Price"| B1["Answer the price; ask if they want the link or a call"]
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A2 -->|"Link"| B2["Send the link; ask one yes/no question"]
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A2 -->|"Book"| B3["Explain what the book is; ask what they want to change"]
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A2 -->|"Book a call"| B4["Send booking steps; ask their time zone"]
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A2 -->|"Trust"| B5["Give one simple proof; suggest a call if needed"]
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A2 -->|"Other"| B6["Short answer; ask one clarifying question"]
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B1 --> C1["If no reply: follow up on a high-reply day"]
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B2 --> C1
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B3 --> C1
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B4 --> C1
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B5 --> C1
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B6 --> C1
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```
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A system like this can cover most conversations without pretending to be a therapist in DM.
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1244
sergio_instagram_messaging/generate_dm_report_detailed.py
Normal file
1244
sergio_instagram_messaging/generate_dm_report_detailed.py
Normal file
File diff suppressed because it is too large
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Reference in a new issue