The Essential AI Toolkit for Lifecycle Marketers

## How this toolkit is structured - **Strategic framing:** 4-page executive narrative that explains why AI investments belong in lifecycle programs today—not next FY. - **Technical depth:** Data models, prompt packs, and QA flows vetted by Engage Evolution’s managed Automation Flight Deck. - **Proven stories:** Annotated case studies show the metrics, staffing, and guardrails that separated successful pilots from stalled experiments. ## Sections ### 1. Executive Summary + Readiness Checklist - Defines the three AI roles (sense, decide, act) in lifecycle marketing and how to justify each in board decks. - Readiness questionnaire with scoring to identify blockers before budget season. - Conversation starters you can use with Finance, Legal, and the CMO to align on funding thresholds. ### 2. Tooling + Architecture Field Guide - Comparative review of SFMC Einstein, Braze Sage, Iterable Captivate, plus neutral AI services. - Recommended stack diagrams with integration notes, logging requirements, and cost callouts. - API payload samples for syncing product, support, and billing data into each platform. ### 3. Implementation Sprints - Step-by-step plans for three sprints: personalized onboarding, lifecycle nudges, and AI-powered win-back flows. - Includes RACI tables, dependencies, and QA automation prompts so RevOps/Marketing know exactly who owns what. - Change-management checklist (briefing deck, office hours, retro) so new AI workflows stick after the pilot. ### 4. Case Studies & KPI Library - Two anonymized client stories detailing telemetry (lift, LTV, CAC payback) and the instrumentation used. - KPI cheat sheet linking each metric to dashboards and data sources—complete with SQL/Looker snippets. - Sponsor-ready highlights showing how we monetize the learnings through newsletters, kits, or managed retainers. ## Readiness Diagnostic (sample) We included a 30-question audit that scores four pillars on a 1–5 scale. Below is a subset so you can preview how it works: | Pillar | Sample Criteria | What "5" Looks Like | | --- | --- | --- | | Data Readiness | Clean identity graph, behavioral signals land within 15 minutes | Cross-channel IDs stitched, 95% of journeys see <15 min latency | | Guardrails | Human-in-the-loop approvals, tone guardrails documented | Legal + Brand signed a single policy accessible to all copilots | | Measurement | KPIs tied to lifecycle stage, instrumentation deployed | Every AI experiment feeds the same Looker board with lift + margin | | Talent | Ops + RevOps know how to update prompts and audit logs | Playbooks exist for shift handoffs and “break glass” rollback plans | Each pillar feeds a radar chart and prebuilt backlog, so your team can immediately translate low scores into sprint tickets. ## Architecture Deep Dive The field guide section includes: - Layer diagrams for a composable lifecycle stack (data warehouse → decisioning → channel orchestration → telemetry). - A bill of materials with cost ranges for Segment, Hightouch, Braze Sage, SFMC Einstein, Iterable Captivate, and open-source LLMs. - Observability checklist covering data contracts, webhook retries, and “AgentFlight” runbooks modeled after our Passive Revenue Lab. ## Implementation Sprints in Detail 1. **Personalized Onboarding** - Goal: shrink time-to-value for new customers. - Assets: onboarding sequence templates, predictive scoring notebook, QA prompts for legal/compliance review. - KPI Targets: 12% lift in onboarding engagement, 8-point increase in NPS for cohort B. 2. **Lifecycle Nudge Lab** - Goal: drive expansion and upsell motions using AI-generated “micro plays.” - Assets: playbook storyboard, lookback analysis script, funnel instrumentation pack. - KPI Targets: 15% increase in expansion pipeline influenced, <2% opt-out impact. 3. **Win-Back Copilot** - Goal: recover dormant subscribers with AI-personalized offers. - Assets: GPT prompt library, deliverability stress test, guardrail checklists for incentives. - KPI Targets: 5% reactivation, ROI payback < 60 days. Each sprint is tied to a Notion template (link inside the PDF) that contains backlog items, Loom prompts for SMEs, and sponsor messaging if you decide to monetize the play as a lead magnet. ## Case Studies & KPI Library - **Agentic Fintech**: shows how nightly AI release notes from Salesforce turned into a sponsor-ready newsletter and a $12K/mo managed services retainer. Includes instrumentation mappings and sponsor outreach scripts. - **Global EdTech**: details how Braze Sage + Iterable Captivate were combined to drive weekly insights for 4 personas with a single ops pod. We include the exact telemetry queries. The KPI library covers Acquisition, Onboarding, Retention, Monetization, and Passive Revenue metrics with: - Definitions, formulas, and recommended targets. - Example dashboards (Looker + Hex) with what-good-looks-like screenshots. - Alerting logic so your ops team gets notified when AI outputs drift. ## Apply it immediately 1. **Fill out the readiness checklist** with your cross-functional team to align on scope and investment. 2. **Select the sprint** that matches your most pressing lifecycle gap, then copy the backlog items into your project tool. 3. **Deploy prompts and QA scripts** exactly as provided to keep AI-generated output compliant and on-brand; the toolkit references where each prompt lives. 4. **Share the KPI library** with leadership so every experiment reports impact the same way and funnels into your Passive Revenue Lab ledger.