Seller Disposition Automation Engine

Business problem

Before this system existed, the team handled every step manually — individually texting leads and moving them through pipeline stages by hand. This didn't scale as the client base grew, and consistency depended entirely on someone remembering to follow up at the right time.

Flow

My role

• Designed and built a 20-stage lead lifecycle state machine driven by a single custom field (seller_disposition).
• Built the sync automation that reads that field and updates tags, pipeline stage, and campaign enrollment accordingly.
• Built the "10 Days in Heaven" high-frequency follow-up sequence, including the if/else branch logic distinguishing new vs. revived leads.
• Built the Revival Campaign variant.
• Integrated Closebot AI with two distinct qualification paths.
• Packaged the entire system as a reusable GHL snapshot for deployment across client accounts.

Technical implementation

• Core mechanism: a single custom field (seller_disposition) acts as the state variable; a sync automation watches it and fans out updates to tags, pipeline stage, and campaign enrollment.
• 20-stage lifecycle covering the full lead journey (individual stage list not yet itemized).
• "10 Days in Heaven" — high-frequency follow-up campaign with branching logic for new-lead and revived-lead paths.
• Revival Campaign — variant for re-engaging stalled/dead leads.
• Closebot AI integration with two qualification paths: DTS (Direct to Sales, for direct inbound leads) and County Record (for niche leads requiring address verification before qualification).
• Deployment mechanism: packaged as a GHL snapshot, deployed across 200+ active client accounts.

Architecture

A single custom field is the source of truth for lead stage. A sync automation reacts to changes in that field and fans out updates to tags, pipeline stage, and campaign enrollment — keeping the whole system consistent off one source of truth instead of scattered per-workflow logic.

Challenges

• Managing the sheer number of if/else branching conditions across a 20-stage lifecycle — every stage needed to connect correctly to the next, with no broken links or missing pieces, as the logic grew more complex.

Solutions

• Broke the logic down into individual per-stage workflows instead of one monolithic automation, making each stage easier to build, test, and debug in isolation.
• Tested every stage thoroughly before considering it complete.
• Gathered client feedback throughout to confirm the system behaved as expected in practice.

Results

Technologies

GoHighLevel (workflows, custom fields, pipelines, campaigns, snapshots) · Closebot AI