cohort-analysis

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Segment customers into cohorts — by acquisition period, channel, first behavior, or revenue tier — and produce a color-coded retention matrix, overlaid retention curves, LTV-by-cohort comparisons with LTV:CAC where cost data exists, best/worst cohort rankings with hypothesized drivers, stabilization-point analysis, and intervention recommendations for underperformers. Analyzes and recommends; it launches nothing. Triggers on "/digital-marketing-pro:cohort-analysis", "are newer customers retaining better than older ones", "which channel produces the highest-LTV customers", "build a retention matrix", "when does our churn stabilize". Pulls customer data from connected CRM and analytics MCPs, reads the brand profile for business-model context, and saves the summary as a campaign-tracker.py insight for trend tracking.

AI & Automation 809 stars 134 forks Updated 4 days ago MIT

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# /digital-marketing-pro:cohort-analysis ## Purpose Perform customer cohort analysis to understand lifecycle patterns, retention, and value over time. Segment customers into cohorts by acquisition date, channel, behavior, or value tier, then track retention curves, compare cohort performance, and identify which acquisition sources produce the highest-value customers. This analysis reveals whether the business is acquiring better or worse customers over time, which channels drive long-term value versus one-time transactions, and where lifecycle interventions (onboarding improvements, re-engagement campaigns, loyalty programs) would have the greatest impact on retention and revenue. ## Input Required The user must provide (or will be prompted for): - **Cohort type**: `time-based` (customers grouped by acquisition week, month, or quarter — the standard cohort analysis showing retention evolution over time), `channel-based` (customers grouped by acquisition source — paid search, organic, social, email, referral — revealing which channels produce the most durable customers), `behavioral` (customers grouped by first action taken — e.g., product category purchased, feature used, content consumed — identifying which entry points lead to highest retention), or `revenue-tier` (customers grouped by initial purchase value — low, medium, high, enterprise — showing how starting value correlates with lifetime retention and expansion) - **Time period and granularity**: The analysis wind...

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

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