Email List Segmentation

Email List Segmentation

Overview

Sending the same email to your entire list is one of the most expensive mistakes in ecommerce marketing. ISPs throttle senders with low engagement, unsubscribe rates spike for irrelevant content, and you leave revenue on the table by treating a VIP customer identically to someone who has never purchased. Klaviyo (for Shopify/BigCommerce) and AutomateWoo or Mailchimp (for WooCommerce) include RFM and behavioral segmentation built-in — no custom code needed for most stores.

When to Use This Skill

• When email open rates drop below 20% and you suspect low engagement dragging deliverability
• When migrating from blast-and-pray sends to targeted campaign logic
• When building a suppression system to protect high-value subscribers from over-messaging
• When launching a win-back program and need to identify the "at-risk" vs. "lapsed" cohort
• When analyzing email revenue and needing to attribute performance to specific segments

Core Instructions

Step 1: Choose the right segmentation tool

Step 2: Set up the essential segments — start with five

Build these five segments first. They cover 80% of the value. Add more only when you have campaigns ready for each.

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Klaviyo (Shopify, BigCommerce, or API-connected)

Segment 1: Champions (VIP buyers) 1. Go to Klaviyo → Segments → Create Segment 2. Conditions:

• Has placed order at least 5 times ever
• AND Total Customer Value greater than $500
• AND Opened email in the last 60 days

3. Save as "Champions — VIP Buyers"

Segment 2: Active buyers (engaged, not yet VIP) 1. Create segment with conditions:

• Has placed order at least 2 times ever
• AND Has placed order in the last 90 days

2. Save as "Active Buyers"

Segment 3: At-risk (used to buy, now quiet) 1. Create segment using Klaviyo's predictive analytics:

• Predicted Churn Risk equals High
• AND Has placed order at least 1 time ever

2. Save as "At-Risk Customers"

Segment 4: Email-engaged non-buyers 1. Create segment with conditions:

• Has placed 0 orders
• AND Opened email in the last 30 days

2. Save as "Engaged Subscribers — No Purchase"

Segment 5: Unengaged (to suppress) 1. Create segment with conditions:

• Has NOT opened email in the last 180 days
• AND Has NOT clicked email in the last 180 days
• AND Email subscription status is Subscribed

2. Save as "Unengaged — Suppression List"

Suppress the unengaged segment before every campaign send:

• When creating a campaign in Klaviyo, under Excluded Segments, add "Unengaged — Suppression List"
• This protects deliverability by not sending to cold contacts who will mark you as spam

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WooCommerce with Mailchimp or Klaviyo

Mailchimp: 1. Go to Mailchimp → Audience → Segments → Create Segment 2. Mailchimp's ecommerce segments require the WooCommerce + Mailchimp plugin for purchase data sync 3. Common conditions: "Purchased" / "Has not purchased" / "Total spent is greater than" / "Last purchase was more than 90 days ago"

AutomateWoo (for WooCommerce automation, not campaigns): 1. Go to AutomateWoo → Reports → RFM Analysis to view your customers plotted on an RFM grid 2. Export customer lists from each RFM quadrant and import into your ESP for targeted campaigns

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WooCommerce with Klaviyo plugin

1. Install the Klaviyo plugin from the WordPress plugin directory 2. In Klaviyo, all WooCommerce orders and customers sync automatically 3. Follow the same segment setup as the Shopify/Klaviyo instructions above

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Step 3: Set up behavioral segments for campaign targeting

Beyond RFM, create segments that map to specific campaigns:

Sale shoppers (always use discounts — suppress from full-price launches):

• Klaviyo condition: Has used a discount code more than 2 times ever

High AOV customers (show premium products):

• Klaviyo condition: Average Order Value greater than $150

Category-specific buyers (for product launches):

• Klaviyo condition: Ordered product in category = "Skincare" (use your Shopify product type or Klaviyo collection data)

Recently subscribed non-buyers (in welcome flow):

• Klaviyo condition: Subscribed to email in the last 30 days AND Has placed 0 orders

Step 4: Apply segments to campaigns

In Klaviyo, when creating a campaign: 1. Under Recipient Selection, choose your target segment (e.g., "Active Buyers") 2. Under Excluded Segments, always add "Unengaged — Suppression List" 3. Preview the segment size before sending — avoid sending to segments under 200 contacts (can trigger spam filters on some providers)

Frequency rules by segment:

• Champions: max 2 emails/week (they are engaged; maintain relationship but don't over-message)
• Active Buyers: 1–2 emails/week
• At-Risk: 1 email/week (retention focus)
• Engaged Subscribers (no purchase): 1–2 emails/week (nurture toward first purchase)

Step 5: List hygiene — run quarterly

1. In Klaviyo, go to Segments → [Unengaged list] and check the size 2. If unengaged segment exceeds 20% of your total list, run a sunset campaign:

• Send one final email: "We've noticed you haven't engaged lately — still want to hear from us?"
• Wait 7 days
• Suppress anyone who did not open the sunset email

3. Never delete unsubscribed contacts — suppress them. Klaviyo retains the unsubscribe signal to prevent accidental re-subscription.

Best Practices

• Start with 5 segments, not 50 — champion, active, at-risk, engaged non-buyer, unengaged is enough to see 80% of the benefit
• Suppress before you send — always build a suppression list (unengaged + unsubscribed) and apply it to every campaign
• Protect champions — never include your champions segment in sales/discount campaigns unless you want to train VIPs to wait for promotions
• Engagement tier beats purchase history for deliverability — for inbox placement, behavioral engagement signals matter more than how much someone has spent
• Process unsubscribes immediately — Klaviyo handles this automatically via webhooks; ensure your ESP is connected to your platform's unsubscribe events
• GDPR: for EU contacts, track consent separately and never rely on legitimate interest for marketing emails — require explicit opt-in

Common Pitfalls

Related Skills

• @email-marketing-automation
• @win-back-reactivation
• @lifecycle-marketing-automation
• @customer-retention-engine
• @first-party-data-collection

Pre-Campaign Audience Validation System

Problem Description

A fashion ecommerce brand is launching a 20%-off storewide sale and wants to send the promotional email to their most engaged subscribers. Their list has grown organically over several years and contains a mix of highly loyal customers, occasional shoppers, and contacts who have not interacted in years. The brand also has European customers, which introduces GDPR obligations they must respect.

Before the campaign goes out, the marketing operations team needs a script that takes the full subscriber list and the proposed target segment and produces a clean, validated send list. In past campaigns, they accidentally emailed contacts who had unsubscribed, triggered spam filters due to sending to disengaged contacts, and received GDPR complaints from EU customers whose consent records were not properly validated. They also discovered that their most valuable loyal customers started holding off purchases until promotional emails arrived — a problem they want to avoid repeating.

The team needs a TypeScript (or JavaScript) module that validates and cleans a proposed campaign audience before delivery. It should produce a report showing how many contacts were removed and why, and the final list of addresses approved for sending.

Output Specification

Produce:

• campaign-validator.ts (or .js) — the validation module with the core logic
• validation-report.json — output when the module is run against the provided input, showing suppression counts by reason and the final approved address list (or count)

The following files are provided as inputs. Extract them before beginning.

=============== FILE: inputs/subscribers.json =============== [ { "email": "alice@example.com", "rfmSegment": "champions", "engagementTier": "champion", "openRate": 0.75, "lastOpenDate": "2026-03-10", "lastClickDate": "2026-03-08", "totalSent": 24, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": "consent", "region": "EU" }, { "email": "bob@example.com", "rfmSegment": "new-customers", "engagementTier": "active", "openRate": 0.20, "lastOpenDate": "2026-03-01", "lastClickDate": null, "totalSent": 3, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": null, "region": "US" }, { "email": "claire@example.com", "rfmSegment": "loyal", "engagementTier": "active", "openRate": 0.18, "lastOpenDate": "2026-02-20", "lastClickDate": "2026-02-15", "totalSent": 30, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": "consent", "region": "EU" }, { "email": "david@example.com", "rfmSegment": "potential-loyal", "engagementTier": "active", "openRate": 0.22, "lastOpenDate": "2026-02-10", "lastClickDate": "2026-02-05", "totalSent": 15, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": "legitimate-interest", "region": "EU" }, { "email": "eva@example.com", "rfmSegment": "hibernating", "engagementTier": "unengaged", "openRate": 0.00, "lastOpenDate": null, "lastClickDate": null, "totalSent": 10, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": null, "region": "US" }, { "email": "frank@example.com", "rfmSegment": "cant-lose", "engagementTier": "dormant", "openRate": 0.05, "lastOpenDate": "2025-06-01", "lastClickDate": null, "totalSent": 40, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": null, "region": "US" }, { "email": "grace@example.com", "rfmSegment": "at-risk", "engagementTier": "at-risk", "openRate": 0.10, "lastOpenDate": "2025-11-20", "lastClickDate": null, "totalSent": 20, "marketingConsent": false, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": null, "region": "US" }, { "email": "henry@example.com", "rfmSegment": "loyal", "engagementTier": "champion", "openRate": 0.80, "lastOpenDate": "2026-03-05", "lastClickDate": "2026-03-04", "totalSent": 20, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": "consent", "region": "EU" }, { "email": "irene@example.com", "rfmSegment": "lost", "engagementTier": "unengaged", "openRate": 0.02, "lastOpenDate": "2024-01-01", "lastClickDate": null, "totalSent": 8, "marketingConsent": true, "unsubscribedAt": "2025-05-01", "bounced": false, "spamComplaint": false, "gdprLawfulBasis": null, "region": "US" }, { "email": "james@example.com", "rfmSegment": "potential-loyal", "engagementTier": "active", "openRate": 0.35, "lastOpenDate": "2026-03-01", "lastClickDate": "2026-02-25", "totalSent": 18, "marketingConsent": true, "unsubscribedAt": null, "bounced": true, "spamComplaint": false, "gdprLawfulBasis": null, "region": "US" }, { "email": "kate@example.com", "rfmSegment": "hibernating", "engagementTier": "unengaged", "openRate": 0.01, "lastOpenDate": "2023-12-01", "lastClickDate": null, "totalSent": 50, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": true, "gdprLawfulBasis": null, "region": "US" }, { "email": "liam@example.com", "rfmSegment": "champions", "engagementTier": "champion", "openRate": 0.65, "lastOpenDate": "2026-03-11", "lastClickDate": "2026-03-10", "totalSent": 22, "marketingConsent": true, "unsubscribedAt": null, "bounced": false, "spamComplaint": false, "gdprLawfulBasis": "consent", "region": "EU" } ]

=============== FILE: inputs/campaign.json =============== { "name": "Spring Sale 20% Off", "type": "promotional_discount", "targetSegments": ["champions", "loyal", "potential-loyal", "active"], "targetRegions": ["US", "EU"] }

Email Segment Sync Service for Klaviyo

Problem Description

A growing DTC (direct-to-consumer) apparel brand uses Klaviyo as their email service provider. They have recently built an internal segmentation database that classifies subscribers by purchase behavior and email engagement, and now need to keep Klaviyo's list management in sync with their internal data.

Currently, their segments in Klaviyo are stale because updates only happen manually. Campaigns are going to the wrong audiences: lapsed customers are receiving retention emails meant for active buyers, and the internal team cannot trust the Klaviyo segment counts when planning campaigns. Additionally, several customers have complained that they received emails after opting out, because the unsubscribe event was only reflected in Klaviyo the following night during a bulk sync.

The engineering team wants a TypeScript service that: 1. Pushes their classified subscriber lists to Klaviyo on a regular automated schedule so segments stay current 2. Also updates Klaviyo immediately when specific real-time events occur (like a customer completing an order or opting out)

The solution should be built to handle their list size of up to 80,000 subscribers efficiently, and must respect Klaviyo's API constraints.

Output Specification

Produce:

• klaviyo-sync.ts — the sync service implementation including scheduled sync and event-triggered sync functions
• webhook-handler.ts — an HTTP handler (Express route or equivalent) that processes incoming ESP/platform webhook events and triggers the appropriate sync actions
• README.md — brief explanation of how to configure and run the service, including required environment variables and schedule

The service should use the following pre-classified subscriber data. Extract the input file before beginning.

=============== FILE: inputs/classified-subscribers.json =============== [ { "email": "alice@example.com", "rfmSegment": "champions", "engagementTier": "champion", "openRate": 0.75, "totalRevenue": 1450.00, "avgOrderValue": 120.83, "orderCount": 12, "tags": ["frequent-buyer", "high-aov"], "marketingConsent": true, "unsubscribedAt": null }, { "email": "bob@example.com", "rfmSegment": "new-customers", "engagementTier": "active", "openRate": 0.20, "totalRevenue": 85.00, "avgOrderValue": 85.00, "orderCount": 1, "tags": ["new-subscriber-30d"], "marketingConsent": true, "unsubscribedAt": null }, { "email": "claire@example.com", "rfmSegment": "loyal", "engagementTier": "active", "openRate": 0.18, "totalRevenue": 620.00, "avgOrderValue": 88.57, "orderCount": 7, "tags": ["category-apparel"], "marketingConsent": true, "unsubscribedAt": null }, { "email": "david@example.com", "rfmSegment": "at-risk", "engagementTier": "at-risk", "openRate": 0.10, "totalRevenue": 540.00, "avgOrderValue": 90.00, "orderCount": 6, "tags": ["sale-shopper"], "marketingConsent": true, "unsubscribedAt": null }, { "email": "eva@example.com", "rfmSegment": "cant-lose", "engagementTier": "dormant", "openRate": 0.05, "totalRevenue": 3200.00, "avgOrderValue": 213.33, "orderCount": 15, "tags": ["high-aov", "frequent-buyer"], "marketingConsent": true, "unsubscribedAt": null }, { "email": "frank@example.com", "rfmSegment": "hibernating", "engagementTier": "unengaged", "openRate": 0.01, "totalRevenue": 45.00, "avgOrderValue": 22.50, "orderCount": 2, "tags": [], "marketingConsent": true, "unsubscribedAt": null }, { "email": "grace@example.com", "rfmSegment": "potential-loyal", "engagementTier": "active", "openRate": 0.22, "totalRevenue": 310.00, "avgOrderValue": 103.33, "orderCount": 3, "tags": ["review-submitter"], "marketingConsent": true, "unsubscribedAt": null }, { "email": "henry@example.com", "rfmSegment": "champions", "engagementTier": "champion", "openRate": 0.80, "totalRevenue": 880.00, "avgOrderValue": 97.78, "orderCount": 9, "tags": ["frequent-buyer", "mobile-opener"], "marketingConsent": true, "unsubscribedAt": null } ]

Subscriber RFM Scoring Engine

Problem Description

A mid-size ecommerce retailer has grown their email list to 80,000 subscribers across several countries and currencies, but all campaigns currently go to the entire list. Their email deliverability has been declining and their marketing team suspects they are sending irrelevant content to disengaged subscribers while also over-messaging their most loyal customers.

The engineering team has been asked to build a TypeScript module that takes raw subscriber and order data and classifies each subscriber into a meaningful segment based on their purchase behavior. The module will run as a nightly job to keep segments fresh. A key challenge is that the store operates in USD, EUR, and GBP, so revenue figures in the source data may be in different currencies and must be handled consistently before any value-based classification is applied.

The team wants the classification logic captured as reusable TypeScript functions so it can be reviewed, tested, and integrated into their existing Node.js backend. No specific algorithm is mandated in the brief — use whatever approach produces well-calibrated, fair segmentation across the subscriber population.

Output Specification

Produce a TypeScript implementation file rfm-scoring.ts that:

• Defines the subscriber data model as a TypeScript type or interface
• Implements the scoring and segment classification functions
• Exports a main function (e.g. computeRfmSegments) that processes an array of subscriber records and returns classified results
• Includes a brief README.md explaining the scoring approach, how multi-currency input is handled, and how/when the job should be scheduled

The following files are provided as inputs. Extract them before beginning.

=============== FILE: inputs/subscribers.json =============== [ { "email": "alice@example.com", "customerId": "C001", "orderCount": 12, "totalRevenue": 1450.00, "currency": "USD", "lastOrderDate": "2026-02-15", "totalSent": 24, "totalOpened": 18, "totalClicked": 10, "lastOpenDate": "2026-02-20", "lastClickDate": "2026-02-18", "subscribedAt": "2024-01-10", "marketingConsent": true }, { "email": "bob@example.com", "customerId": "C002", "orderCount": 1, "totalRevenue": 85.00, "currency": "USD", "lastOrderDate": "2026-02-28", "totalSent": 5, "totalOpened": 1, "totalClicked": 0, "lastOpenDate": "2026-02-28", "lastClickDate": null, "subscribedAt": "2026-02-01", "marketingConsent": true }, { "email": "claire@example.com", "customerId": "C003", "orderCount": 7, "totalRevenue": 620.00, "currency": "EUR", "lastOrderDate": "2025-09-10", "totalSent": 30, "totalOpened": 6, "totalClicked": 1, "lastOpenDate": "2025-10-01", "lastClickDate": "2025-09-15", "subscribedAt": "2023-06-01", "marketingConsent": true }, { "email": "david@example.com", "customerId": "C004", "orderCount": 3, "totalRevenue": 210.00, "currency": "GBP", "lastOrderDate": "2025-12-01", "totalSent": 15, "totalOpened": 4, "totalClicked": 1, "lastOpenDate": "2025-12-10", "lastClickDate": "2025-12-10", "subscribedAt": "2024-04-20", "marketingConsent": true }, { "email": "eva@example.com", "customerId": "C005", "orderCount": 0, "totalRevenue": 0, "currency": "USD", "lastOrderDate": null, "totalSent": 10, "totalOpened": 0, "totalClicked": 0, "lastOpenDate": null, "lastClickDate": null, "subscribedAt": "2025-01-15", "marketingConsent": true }, { "email": "frank@example.com", "customerId": "C006", "orderCount": 15, "totalRevenue": 3200.00, "currency": "USD", "lastOrderDate": "2024-06-01", "totalSent": 40, "totalOpened": 35, "totalClicked": 20, "lastOpenDate": "2024-06-10", "lastClickDate": "2024-06-10", "subscribedAt": "2022-01-01", "marketingConsent": true }, { "email": "grace@example.com", "customerId": "C007", "orderCount": 2, "totalRevenue": 95.00, "currency": "GBP", "lastOrderDate": "2026-01-20", "totalSent": 8, "totalOpened": 5, "totalClicked": 3, "lastOpenDate": "2026-02-10", "lastClickDate": "2026-02-10", "subscribedAt": "2025-11-01", "marketingConsent": true }, { "email": "henry@example.com", "customerId": "C008", "orderCount": 9, "totalRevenue": 880.00, "currency": "EUR", "lastOrderDate": "2026-03-01", "totalSent": 20, "totalOpened": 16, "totalClicked": 9, "lastOpenDate": "2026-03-05", "lastClickDate": "2026-03-04", "subscribedAt": "2023-09-15", "marketingConsent": true }, { "email": "irene@example.com", "customerId": "C009", "orderCount": 4, "totalRevenue": 320.00, "currency": "USD", "lastOrderDate": "2025-07-14", "totalSent": 22, "totalOpened": 3, "totalClicked": 0, "lastOpenDate": "2025-08-01", "lastClickDate": null, "subscribedAt": "2024-05-10", "marketingConsent": false }, { "email": "james@example.com", "customerId": "C010", "orderCount": 6, "totalRevenue": 540.00, "currency": "USD", "lastOrderDate": "2025-11-20", "totalSent": 18, "totalOpened": 7, "totalClicked": 2, "lastOpenDate": "2025-12-01", "lastClickDate": "2025-11-25", "subscribedAt": "2023-12-01", "marketingConsent": true } ]