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AI Lifecycle Marketing

Alchemail

Building an AI lifecycle engine that segments, drafts, tests, and learns with minimal manual overhead.

Status: In DeliveryTimeline: OngoingRole: Product + Workflow DesignTeam: Marketing, Sales Ops, RevOps

Create an AI-native lifecycle system that generates targeted campaigns from CRM signals, runs controlled experiments, and improves messaging quality through feedback loops.

Alchemail automates lifecycle marketing from audience segmentation through campaign iteration, replacing slow manual planning and copy cycles with a continuous test-and-learn workflow.

Lifecycle campaigns were manually segmented, slow to ship, and often too broad to feel relevant. Approvals and handoffs across teams reduced iteration speed and limited experimentation.

My Contribution

  • •Designed and shipped Alchemail for autonomous CRM segmentation and lifecycle campaign execution
  • •Built content-training workflow using web content, case studies, and approved marketing positioning
  • •Integrated Apollo CRM signals to identify high-value account segments and trigger targeted campaigns
  • •Defined experimentation and learning loops for copy and segment performance

Process

Workflow Mapping

Mapped manual lifecycle campaign steps to identify bottlenecks and automation candidates.

Agent Design

Defined goals, context inputs, and decision rules for segmentation, message generation, and campaign sequencing.

System Integration

Connected Apollo CRM data and campaign delivery channels to run continuously with measurable outputs.

Feedback Loops

Incorporated open-rate and engagement signals to refine segment strategy and copy quality over time.

Key Features

Dynamic Segmentation

Builds campaign cohorts from Apollo account signals instead of static list logic.

Autonomous Campaign Generation

Generates and launches targeted lifecycle sequences with reduced manual copy assembly.

Continuous A/B Learning

Runs tests and updates message strategy based on observed engagement outcomes.

Challenges & Solutions

Challenge

Legacy drip campaigns treated most cohorts similarly, reducing relevance.

Solution

Implemented dynamic segmentation using CRM account signals and context-aware message generation.

Outcome

Improved campaign precision and reduced generic, one-size-fits-all messaging.

Challenge

Content production and approvals slowed iteration velocity.

Solution

Trained Alchemail on trusted marketing artifacts and created automated test-and-learn loops.

Outcome

Faster campaign iteration with less dependency on multi-stakeholder copy review cycles.

Results

  • •Established an AI-driven lifecycle campaign system that continuously learns from engagement feedback.
  • •Reduced manual effort across segmentation, content generation, and campaign execution.
  • •Increased iteration velocity by automating campaign setup and test cycles.

Frontend

Next.jsReact

Backend

Apollo CRM integrationAgent orchestration workflows

Tooling

A/B testingCampaign analyticsPrompt and knowledge-base management

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