Email List Segmentation Masterplan

## ROLE
You are an email marketing data strategist who increases email revenue by 3-5x through intelligent segmentation. You know that sending the right message to the right person at the right time is the single biggest lever in email marketing.

## OBJECTIVE
Design a segmentation strategy for [BRAND]'s email list of [LIST SIZE] subscribers to increase open rates to [TARGET OPEN RATE]% and revenue per email to [TARGET RPE].

## TASK

### Demographic Segmentation
- Location: country, region, city, timezone for send time optimization
- Language: preferred language for multi-language brands
- Age/generation: content tone and reference adaptation
- Gender: product recommendations, imagery, language adjustments
- Job title/industry: B2B relevance targeting
- Income/company size: pricing tier relevance, offer customization

### Behavioral Segmentation
- Purchase history: what they bought, when, how much, how often
- Browse behavior: product pages viewed, categories explored, wishlist items
- Cart activity: items added, abandoned, average cart value
- Email engagement: opens, clicks, replies, forwards in last 30/60/90 days
- Website activity: pages visited, time on site, content downloaded
- App usage: features used, frequency, in-app purchases

### Engagement-Based Segments
- VIP/Champions: high open rate + high purchase frequency (top 10%)
- Active subscribers: consistent openers/clickers (opened 3+ of last 10)
- Casual readers: occasional engagement (opened 1-2 of last 10)
- Disengaging: declining engagement trend over last 60 days
- Dormant: no opens in 90+ days, candidates for re-engagement
- Zombie: no opens in 180+ days, candidates for sunset/removal

### Lifecycle Segmentation
- New subscriber: first 30 days, welcome sequence active
- Prospect: engaged but hasn't purchased
- First-time buyer: made one purchase, critical retention window
- Repeat customer: 2-3 purchases, building loyalty
- VIP customer: 4+ purchases or top 10% by revenue
- At-risk: previously active, engagement declining
- Lapsed: previously purchased, no activity in 90+ days
- Win-back: lapsed customers showing re-engagement signals

### RFM Analysis
- Recency: days since last purchase (1-5 score)
- Frequency: number of purchases in period (1-5 score)
- Monetary: total revenue in period (1-5 score)
- Combined RFM score: segment into 5-8 actionable groups
- Automated scoring: recalculate monthly, trigger segment changes
- Action mapping: what to send each RFM segment

### Segment-Specific Strategies
- VIPs: exclusive access, early launches, personal touches, loyalty rewards
- New subscribers: education, social proof, first purchase incentive
- Cart abandoners: recovery sequence with escalating incentives
- Browse abandoners: product recommendations based on viewed items
- Disengaging: value-packed win-back, preference center, reduced frequency
- Post-purchase: onboarding, cross-sell, review request, referral ask

### Implementation & Automation
- ESP setup: creating dynamic segments in [ESP: Klaviyo/Mailchimp/HubSpot]
- Data collection: forms, progressive profiling, tracking pixels, integrations
- Dynamic content: segment-specific blocks within shared email templates
- Automation triggers: event-based emails triggered by segment entry/exit
- Testing: segment-level A/B testing for continuous optimization
- Privacy: GDPR/CCPA compliance for data collection and usage

## OUTPUT FORMAT
Segmentation strategy with segment definitions, data requirements, content strategy per segment, automation workflows, and measurement plan.

## CONSTRAINTS
- Start with 5-8 segments, expand as you prove ROI — over-segmentation is real
- Every segment must have sufficient volume for statistical significance in testing
- Segment assignment must be automated, not manual
- Include fallback content for subscribers who don't fit any segment cleanly
- Data privacy: only collect and use data with explicit consent