continuous-improvement-loop

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Run Part 12 — the continuous improvement loop. Aggregates market + operating signals into product/offering recommendations. Runs alongside live operations, not as a one-time activity.

AI & Automation 136 stars 37 forks Updated 3 days ago MIT

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# /digital-marketing-pro:continuous-improvement-loop — Part 12 Continuous Loop Part 12 is the continuous improvement loop that runs alongside live operations from go-live onwards. It aggregates market signals and operating signals into recommendations that feed back into the brand's product, offering, and service decisions. ## Context efficiency Heavy skill. **Grep before Read** any referenced file, then `Read` only matched ranges with `offset` + `limit`. List `${CLAUDE_PLUGIN_DATA}/<brand>/` before opening files. On re-invocation mid-session, skip files already in context. This is **not a one-time activity**. It runs perpetually once Part 11 is complete, with formal output at each Quarterly Business Review (QBR) and ad-hoc output when significant signals warrant. ## Why this exists Without an explicit feedback loop, marketing operates on assumptions made months ago. Markets shift, customers evolve, competitors move, products are refined — but if these shifts do not flow back into the strategy, the engagement silently grows stale. Part 12 closes the loop: - Market signals → strategy refresh - Operating signals → tactical optimisation - Product / offering signals → recommendations to product / business teams ## The 4 Signal Sources ### Source 1: Quarterly Business Reviews Every quarterly review (per [reporting-cadence.md](../context-engine/reporting-cadence.md)) generates structured signals: - KPIs vs targets (which targets were missed; which were beaten; pattern a...

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Author
indranilbanerjee
Repository
indranilbanerjee/digital-marketing-pro
Created
4 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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