/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 window and cohort size — weekly cohorts for the past 3 months (high resolution, best for fast-cycle businesses), monthly cohorts for the past 12 months (standard for most businesses), or quarterly cohorts for multi-year analysis (best for long-cycle B2B or subscription businesses). Granularity determines both how cohorts are defined and the retention interval measured
• Metrics to track: Which outcomes to measure across cohorts — retention rate (percentage of cohort still active at each interval), revenue (cumulative and per-period revenue per customer), LTV (cumulative lifetime value with projected future value), engagement (login frequency, feature usage, content consumption), or multiple metrics simultaneously for a comprehensive lifecycle view
• Data source: Where to pull customer data — CRM (deal data, customer records, lifecycle stages), analytics (website behavior, conversion events, session data), product analytics (feature usage, activation events, engagement metrics), or a combination of sources merged on customer identifier

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Extract business model (SaaS, eCommerce, B2B), typical customer lifecycle length, key retention metrics, and churn definition for the industry. Check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Define cohorts based on selected type: Segment the customer base into cohorts. For time-based: group customers by the week, month, or quarter they were first acquired (first purchase, account creation, or first meaningful interaction). For channel-based: group by the acquisition source attributed to their first conversion (UTM source, referral path, or CRM lead source field). For behavioral: group by the first significant action taken (first product category purchased, first feature activated, first content type consumed). For revenue-tier: group by initial transaction value bucketed into tiers (define thresholds based on the business's order value distribution — e.g., bottom 25%, middle 50%, top 25%).