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winback-engine

Find dormant subscribers, segment by historical value, draft personalized winback emails per tier, and identify suppression candidates

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Winback Engine

Turn dormant subscribers back into customers. This skill scans your connected ESP for inactive subscribers, segments them by historical value, drafts a personalized winback series per tier, and flags who you should suppress before they tank your deliverability.

Requires: Cogny MCP + a connected ESP. Sign up

The output is a complete, ready-to-run winback campaign — not a report.

Usage

/winback-engine — standard mode (60-day dormancy threshold) /winback-engine aggressive — 30-day threshold (acts earlier, bigger list) /winback-engine conservative — 90-day threshold (only chronic dormants)

Prerequisites Check

Detect connected ESP (same pattern as subject-line-lab). If none, prompt user to connect at cogny.com.

Steps

1. Define dormancy thresholds

| Mode | Dormant | Deep dormant | Suppress candidate | |------|---------|--------------|-------------------| | aggressive | 30+ days no open | 90+ days no open | 180+ days no open/click, no purchase | | standard | 60+ days | 180+ days | 365+ days no engagement | | conservative | 90+ days | 270+ days | 540+ days |

2. Pull subscriber data

From the connected ESP, pull every active subscriber with:

  • Subscribe date
  • Last open date + last click date (take max as last_engaged)
  • Total historical opens / clicks
  • Signup source / list
  • Tags / segments
  • Email domain (gmail, yahoo, hotmail, corporate)

Purchase / LTV data — Klaviyo only. The cogny-mcp-proxy currently exposes purchase data for Klaviyo (via list_events filtered to the "Placed Order" metric, summing the value field per profile). Mailchimp, Rule, and Get a Newsletter in this proxy are engagement-only — they do not expose:

  • Total spend / LTV
  • Order count
  • AOV
  • Last order date

Per-ESP tool adapter:

| ESP | Subscriber list | Purchase history | Suppression write | |-----|-----------------|------------------|-------------------| | Klaviyo | list_profiles (+ get_profile for detail) | list_events with metric="Placed Order" — use value field per profile for LTV | unsubscribe_profiles | | Mailchimp | tool_list_members (filter status=subscribed) | Not available | tool_update_member (status=unsubscribed) | | Rule | tool_list_subscribers | Not available | tool_update_subscriber / tool_list_suppressions | | Get a Newsletter | tool_list_contacts | Not available | tool_delete_contact or tool_update_contact |

3. Segment into tiers

If Klaviyo (has purchase data): cross-cut by engagement recency × historical value:

| Tier | Criteria | Tone | Offer | |------|----------|------|-------| | A — High value dormant | Dormant + LTV ≥ top quartile OR past purchaser | Personal, apologetic, premium | Strong (20% off, free shipping, exclusive access). This is real money. | | B — Medium value dormant | Dormant + some past engagement or 1 purchase | Warm, value-forward | Moderate (10–15% off, bundle, content) | | C — Low engagement dormant | Dormant + never purchased + low engagement history | Direct, honest | Last-chance offer or content-only | | D — Suppression candidates | Deep dormant + no purchase + no recent engagement | Final email only | "We're removing you unless..." |

If Mailchimp / Rule / Get a Newsletter (engagement-only): tier by engagement depth instead. No purchase data is available from the proxy for these ESPs — LTV-based tiering falls back to:

| Tier | Criteria | Tone | Offer | |------|----------|------|-------| | A — Deep engaged dormant | Dormant now, but historically high open+click rate (top quartile of clicks) | Personal, "we miss you" | Strong offer or exclusive content | | B — Casual dormant | Dormant, moderate historical engagement | Warm, value-forward | Modest offer | | C — Low engagement dormant | Dormant, few lifetime opens/clicks | Direct, honest | Content-only or re-opt-in ask | | D — Suppression candidates | Deep dormant, minimal lifetime engagement | Final email only | Goodbye |

Revenue estimates in the output should be omitted for engagement-only ESPs, or marked estimated_impact_usd: null on findings with a note that the user can pair with their own LTV data to size the opportunity.

Output the tier sizes before drafting:

Winback candidates: <total>
├─ Tier A (high-value dormant):   <count> subscribers (est. revenue: $<X>)
├─ Tier B (medium):               <count>
├─ Tier C (low engagement):       <count>
└─ Tier D (suppression):          <count>  ← do not keep sending to these

Revenue estimate for Tier A/B: count × historical AOV × expected reactivation rate (use 3-5% for Tier A, 1-2% for Tier B).

4. Draft a 3-email winback series per tier

For each tier (A, B, C), generate 3 emails with cadence:

  • Email 1 — day 0, soft reopener ("we noticed")
  • Email 2 — day 4, value + offer
  • Email 3 — day 8, last-chance / decision

Tone by tier:

Tier A (high-value):

  • E1: Personal note, reference last purchase by name if available. No offer yet.
  • E2: Show what they're missing — new products in their category, or customer stories
  • E3: Strongest offer, explicit decision framing ("if we're not a fit anymore, we'll respect that")

Tier B (medium):

  • E1: "We miss you" + social proof
  • E2: Content-led (best of what they missed) + modest offer
  • E3: Offer + deadline

Tier C (low engagement):

  • E1: Direct — "is this still useful?"
  • E2: One-click re-subscribe or one-click unsubscribe (reduce friction)
  • E3: Final send before suppression

For every email produce: subject, preheader, body, CTA. Same format as /welcome-series.

5. Suppression list (Tier D)

Output a block the user can act on:

Suppression recommendation: <count> subscribers

These haven't opened, clicked, or purchased in <threshold> days. Keeping
them on the list:
  • Hurts deliverability (low engagement = more spam placement for everyone)
  • Inflates list size → higher ESP costs
  • Distorts reporting baselines

Recommended action:
  1. Send ONE final "goodbye" email offering resubscribe
  2. After 7 days, suppress non-responders

Sample suppression email is included below.

Then include the goodbye email draft.

6. Write-mode: offer to execute

After presenting the plan, ask:

I can push this into your ESP as:
  [A] Drafts only (you review + launch)
  [B] Full flow installed but paused (you activate when ready)
  [C] Just give me the copy, I'll handle it

Which?

If the user picks A or B and the ESP MCP has write tools available, follow the Write Action Protocol (same as the /cogny skill — present the action plan, wait for approval, then execute one step at a time).

7. Create findings

For each significant tier (A, B with >100 subscribers):

{
  "title": "Tier A winback: <N> high-value dormant subscribers, est. $<X> recoverable",
  "body": "Identified <N> past purchasers with LTV in top quartile who haven't engaged in <days> days. Avg historical AOV: $<Y>. At 4% reactivation, estimated winback revenue = $<X>. 3-email series drafted and ready to install.",
  "action_type": "winback_campaign",
  "expected_outcome": "Reactivate 3-5% of Tier A dormant, generate $<X> over 30 days",
  "estimated_impact_usd": <X>,
  "priority": "high"
}

And one for suppression:

{
  "title": "<N> subscribers recommended for suppression",
  "body": "Deep dormant (<threshold>+ days) with no purchases. Keeping them hurts deliverability and inflates ESP cost. Recommend goodbye email + suppression after 7 days.",
  "action_type": "list_hygiene",
  "expected_outcome": "Improve overall open rate by 1-3 pp, lower ESP tier cost",
  "estimated_impact_usd": <monthly_esp_savings>,
  "priority": "medium"
}

Notes

  • Never suppress before a winback attempt. Give Tier D subscribers one last chance to re-engage.
  • Apple MPP means "last open" is noisy for iPhone users. Weight clicks and purchases higher than opens when ass
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