Engagement Scoring System for List Quality
A scoring model that classifies each contact by email engagement level, fueling segmentation decisions and list hygiene.
Build a continuous scoring system that monitors email list health in real time, identifying disengagement trends before they impact deliverability and triggering preventive actions.
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Prompt objective
Build a continuous scoring system that monitors email list health in real time, identifying disengagement trends before they impact deliverability and triggering preventive actions.
Real use case
FitPro Gym, a fitness network with an app and 38,000 emails in their database, has never cleaned their list. Their open rate has dropped progressively from 28% to 11% over 18 months. They need a system to identify who is engaged and act proactively.
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Prompt
Design an engagement scoring system for the email database at [COMPANY NAME], with [NUMBER] contacts and [NUMBER] emails sent per month. Context: - Platform: [PLATFORM: ActiveCampaign/HubSpot/Mailchimp/Klaviyo] - Current open rate: [PERCENTAGE]% - Current click rate: [PERCENTAGE]% - Unsubscribe rate: [PERCENTAGE]% - Spam complaint rate: [PERCENTAGE]% - Last list cleanup: [DATE or NEVER] **1) Engagement score model (0-100):** **Positive scoring (actions that add points):** - Opened email: +[X] points (decays over time) - Opened today: +[X] | Opened 7 days ago: +[X] | Opened 30 days ago: +[X] - Clicked a link: +[X] points - Replied to email: +[X] points - Forwarded email: +[X] points - Converted (purchased/booked): +[X] points - Updated preferences: +[X] points **Negative scoring (actions that subtract points):** - Email not opened: -[X] points per ignored email - Soft bounce: -[X] points - Marked as spam: -[X] points (critical) - No interaction in 30 days: -[X] points - No interaction in 60 days: -[X] points - No interaction in 90 days: -[X] points **Time decay:** - Score reduces [X]% every [PERIOD] without interaction - Formula: Current score × 0.9 every 15 days without an open **2) Engagement tiers:** | Score Range | Label | Tier | Est. % | Action | |-------------|-------|------|--------|--------| | 80-100 | Super Engaged | Champions | [X]% | VIP program | | 60-79 | Engaged | Active | [X]% | Maintain frequency | | 40-59 | Warm | At Risk | [X]% | Reduce frequency | | 20-39 | Cold | Needs Attention | [X]% | Reactivation | | 0-19 | Inactive | Removal Candidate | [X]% | Win-back or cleanup | **3) Automations based on score:** - Score dropped from Engaged to Warm → Trigger: High-value content - Score dropped to Cold → Trigger: Reactivation sequence - Score reached 0 → Trigger: Goodbye email + removal in 7 days - Score rose from Warm to Engaged → Trigger: Welcome back + offer - Score hit 80+ for the first time → Trigger: VIP program invitation **4) List health dashboard:** **Weekly KPIs:** - List distribution by engagement tier (pie chart) - Tier migration (how many moved up/down) - Average list score (trending up or down) - Net growth rate (new - removed) **Automated alerts:** - Average score dropped 5+ points this month → Investigate - Inactive tier exceeds 25% of list → Urgent cleanup needed - Spike in spam complaints → Pause sends and audit **5) Step-by-step implementation:** - How to set up scoring custom fields in [PLATFORM] - Recalculation workflows (frequency: daily or per send) - CRM integration so sales can see the score - Monthly review and weight adjustment process - Implementation timeline: 4 weeks
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- 1Replace the key placeholders first: COMPANY NAME, NUMBER, PLATFORM: ActiveCampaign/HubSpot/Mailchimp/Klaviyo, PERCENTAGE.
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