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Get Started Free →Design lifecycle marketing / CRM journeys across the customer lifecycle. Use when asked to plan onboarding emails, lifecycle/CRM campaigns, drip sequences, re-engagement or winback flows, or a messaging calendar. Produces a lifecycle plan — stage map, the trigger/message/goal for each journey, channel & timing, segmentation, suppression rules, and success metrics.
.claude/skills/mohitagw15856-lifecycle-crm-plan/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 1% | 0% |
| case-04 | ✓→✓ | = Same ✓ | -16% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 9% | 0% |
Lifecycle marketing is the difference between a product people sign up for and one they actually use. This skill maps the customer lifecycle to triggered journeys — each with a clear job — so messaging is behaviour-driven and purposeful, not a batch-and-blast newsletter that trains people to ignore you.
Ask for these only if they aren't already provided:
1. Lifecycle map — the stages and the one behaviour you want at each (signup → activate → habit → expand → renew; with winback for lapsed).
2. Journey table — the core deliverable:
| Journey | Trigger (behaviour, not date) | Audience/segment | Message & goal | Channel | Timing | Success metric | Exit/suppression | |---|---|---|---|---|---|---|---| | Onboarding | signed up, not activated | new, no key action | get to first value | email + in-app | t+0, t+1d, t+3d | activation % | activated → exit | | Winback | inactive 30d | was active | reason to return | email | t+30, t+37 | reactivation % | returned → exit |
3. Segmentation — the few segments that change the message (by behaviour/value, not vanity demographics).
4. Timing & frequency — cadence rules and a global frequency cap / suppression so journeys don't collide or fatigue.
5. Measurement — per-journey metric, holdout group to prove incrementality, and the deliverability guardrails (bounce/spam/unsub watch).
Lifecycle marketing / behavioural CRM practice — trigger-based journeys, segmentation, and incrementality testing with holdouts.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 20,704 | 13,284 | -36% | 1 | 1 | 0% | 3,549 | 2,997 | -16% | 0 | 0 | — |
case-01 | fail→pass | 35,519 | 18,452 | -48% | 1 | 1 | 0% | 5,851 | 3,953 | -32% | 0 | 0 | — |
case-02 | fail→fail | 32,310 | 22,837 | -29% | 1 | 1 | 0% | 5,940 | 4,542 | -24% | 0 | 0 | — |
case-03 | fail→pass | 34,161 | 18,993 | -44% | 1 | 1 | 0% | 6,235 | 4,140 | -34% | 0 | 0 | — |
case-05 | pass→pass | 25,109 | 22,681 | -10% | 1 | 1 | 0% | 3,928 | 4,290 | +9% | 0 | 0 | — |
case-06 | pass→pass | 25,047 | 18,508 | -26% | 1 | 1 | 0% | 4,321 | 3,890 | -10% | 0 | 0 | — |
case-07 | pass→pass | 18,404 | 17,710 | -4% | 1 | 1 | 0% | 3,157 | 3,866 | +22% | 0 | 0 | — |
case-08 | pass→pass | 21,938 | 17,331 | -21% | 1 | 1 | 0% | 3,358 | 3,398 | +1% | 0 | 0 | — |
case-09 | pass→pass | 19,983 | 21,188 | +6% | 1 | 1 | 0% | 3,356 | 4,290 | +28% | 0 | 0 | — |
case-10 | pass→pass | 26,533 | 20,143 | -24% | 1 | 1 | 0% | 3,475 | 4,387 | +26% | 0 | 0 | — |
case-11 | pass→pass | 34,699 | 26,582 | -23% | 1 | 1 | 0% | 6,170 | 5,421 | -12% | 0 | 0 | — |
case-12 | fail→pass | 24,232 | 18,717 | -23% | 1 | 1 | 0% | 3,746 | 3,787 | +1% | 0 | 0 | — |
case-13 | pass→pass | 26,110 | 24,158 | -7% | 1 | 1 | 0% | 4,146 | 4,631 | +12% | 0 | 0 | — |
case-14 | pass→pass | 20,845 | 27,620 | +33% | 1 | 1 | 0% | 3,698 | 5,077 | +37% | 0 | 0 | — |
case-15 | pass→pass | 16,297 | 17,095 | +5% | 1 | 1 | 0% | 2,997 | 3,810 | +27% | 0 | 0 | — |
case-22 | pass→pass | 15,848 | 17,464 | +10% | 1 | 1 | 0% | 3,457 | 4,470 | +29% | 0 | 0 | — |
case-16 | pass→pass | 22,335 | 20,896 | -6% | 1 | 1 | 0% | 3,755 | 4,404 | +17% | 0 | 0 | — |
case-17 | pass→pass | 17,257 | 19,138 | +11% | 1 | 1 | 0% | 3,198 | 4,167 | +30% | 0 | 0 | — |
case-18 | pass→pass | 19,969 | 16,183 | -19% | 1 | 1 | 0% | 3,526 | 3,654 | +4% | 0 | 0 | — |
case-19 | pass→pass | 21,447 | 21,699 | +1% | 1 | 1 | 0% | 3,685 | 4,346 | +18% | 0 | 0 | — |
case-20 | pass→pass | 17,637 | 17,829 | +1% | 1 | 1 | 0% | 4,084 | 4,495 | +10% | 0 | 0 | — |
case-21 | pass→pass | 14,496 | 13,361 | -8% | 1 | 1 | 0% | 2,562 | 3,227 | +26% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.