How Job Change Intent Scorer Tracks Champion Moves

Your support team closed 340 tickets last week. Average resolution time: 4.2 hours. But 23 of those tickets were escalations that sat for 6+ hours before anyone noticed the SLA clock. A team lead spends 1–2 hours daily pulling reports from Hubspot, Slack, cross-referencing with CRM data, and writing up findings. By the time the analysis is done, the queue has moved on.

The result is reactive support instead of proactive operations. SLA risks surface after the breach. Routing problems persist because nobody has time to audit the rules. Agent coaching happens based on gut feel, not pattern data. Job Change Intent Scorer automates the intent scoring and contact management workflow, delivering structured analysis from Hubspot, Slack data without manual report-building.

Three Agents. Two Phases. Zero Missed Champions.

The Job Change Intent Scorer pipeline runs 3 agents in sequence. The Tracker pulls data from Hubspot and Slack, and The Briefer delivers the output. Here is what happens at each stage and why it matters.

• The Tracker (Tier 1 Reasoning): Detects job change signals via web_search — new employer, title, start date, public announcements.
• The Analyst (Tier 1 Reasoning): Scores Re-engagement Potential (RPS 1–10) across 4 weighted criteria: relationship warmth (30%), new company ICP fit (25%), timing signal (25%), and new buying power (20%).
• The Briefer (Tier 1 Reasoning): Drafts an actionable re-engagement brief and writes 6 custom HubSpot contact properties — new company, new title, RPS score, brief summary, change detected date, and last scored timestamp.

When the pipeline completes, you get structured output that is ready to act on. The blueprint bundle includes everything needed to deploy, configure, and customize the workflow:

• 20-node n8n workflow (.json) — you own it
• 3 tested agent system prompts (ITP-validated)
• 8 SDC inter-agent schemas
• Error handling matrix (34 failure modes documented)
• HubSpot custom field setup guide (6 fw_ properties)
• Dependency matrix with ITP-measured costs
• README setup guide (10 minutes)

SLA thresholds, escalation rules, and routing logic are configurable in the system prompts — customize for your ticket volume and priority structure. This means Job Change Intent Scorer adapts to your specific process, terminology, and integration requirements without forking the entire workflow.

Understanding how the pipeline works helps you customize it for your environment and troubleshoot issues when they arise. Here is a step-by-step walkthrough of the Job Change Intent Scorer execution flow.

Step 1: The Tracker

Tier: Tier 1 Reasoning

The pipeline starts here. Detects job change signals via web_search — new employer, title, start date, public announcements. Confirms or rules out a change with a confidence score. No change detected? Pipeline terminates here. Zero Phase 2 cost.

This stage ensures all downstream agents receive clean, validated input. If this step returns incomplete data, every downstream agent works with a degraded picture.

Step 2: The Analyst

Tier: Tier 1 Reasoning

Scores Re-engagement Potential (RPS 1–10) across 4 weighted criteria: relationship warmth (30%), new company ICP fit (25%), timing signal (25%), and new buying power (20%). Only runs when a job change is confirmed.

Why this step matters: This is where the pipeline applies judgment — not just data retrieval, but analysis.

Step 3: The Briefer

Tier: Tier 1 Reasoning

This is the final deliverable — what lands in your inbox or dashboard. Drafts an actionable re-engagement brief and writes 6 custom HubSpot contact properties — new company, new title, RPS score, brief summary, change detected date, and last scored timestamp. Optional Slack alert for high-RPS contacts.

The entire pipeline executes without manual intervention. From trigger to output, every decision point follows a documented path. Every execution produces a traceable audit trail.

All nodes have been validated during Independent Test Protocol (ITP) testing on n8n v2.7.5. The error handling matrix in the bundle documents the recovery path for each failure mode.

We spent a week getting the classification modelto output exactly 3 sentences. Polite instructions like "please write 3 sentences" got ignored. LLMs do not treat polite instructions the same as system constraints. The fix was emphatic constraint language with enforcement: "OUTPUT MUST CONTAIN EXACTLY 3 SENTENCES. If output contains more or fewer than 3 sentences, the response is INVALID."

— ForgeWorkflows Engineering

All values below are from ITP testing — not estimates, not projections. Measured across 20 test contacts spanning confirmed job changes, no-change scenarios, and edge cases.

The primary operating cost for Job Change Intent Scorer is the per-execution LLM inference cost. Based on Independent Test Protocol (ITP) testing, the measured cost is: Cost per Contact (Full Pipeline): $0.29/contact (full pipeline) | $0.21/contact (no change). This figure includes all API calls across all agents in the pipeline — not just the primary reasoning step, but every classification, scoring, and output generation call.

To put this in context, consider the manual alternative. A skilled team member performing the same work manually costs $40–60/hour for a support team lead’s analysis time at a fully loaded rate (salary, benefits, tools, overhead). If the manual version of this workflow takes 1–2 hours daily, the per-execution cost in human labor is significant. The blueprint executes the same pipeline for a fraction of that cost, with consistent quality and zero fatigue degradation.

Infrastructure costs are separate from per-execution LLM costs. You will need an n8n instance (self-hosted or cloud) and active accounts for the integrated services. The estimated monthly infrastructure cost is $5–15/month, depending on your usage volume and plan tiers.

Quality assurance: Blueprint Quality Standard (BQS) audit result is 12/12 PASS. ITP result is 18/20 PASS (90%). These are not marketing claims — they are test results from structured inspection protocols that you can review in the product documentation.

All cost and performance figures are ITP-measured — tested against real data fixtures on n8n v2.7.5 in March 2026. See the product page for full test methodology.

9 files. Workflow, 3 system prompts, error handling, and complete documentation.

When you purchase Job Change Intent Scorer, you receive a complete deployment bundle. This is not a SaaS subscription or a hosted service — it is a set of files that you own and run on your own infrastructure. Here is what is included:

• CHANGELOG.md — Version history
• LICENSE.md — License terms
• README.md — Setup and configuration guide
• dependency_matrix.md — Third-party service dependencies
• error_handling_matrix.md — Error handling reference
• job_change_intent_scorer_v1.json — n8n workflow (main pipeline)
• system_prompts/analyst.txt — Analyst system prompt
• system_prompts/briefer.txt — Briefer system prompt
• system_prompts/tracker.txt — Tracker system prompt

Start with the README.md. It walks through the deployment process step by step, from importing the workflow JSON into n8n to configuring credentials and running your first test execution. The dependency matrix lists every required service, API key, and estimated cost so you know exactly what you need before you start.

Every file in the bundle is designed to be read, understood, and modified. There is no obfuscated code, no compiled binaries, and no phone-home telemetry. You get the source, you own the source, and you control the execution environment.

Job Change Intent Scorer is built for Customer Success teams that need to automate a specific workflow without building from scratch. If your team matches the following profile, this blueprint is designed for you:

• You operate in a customer success function and handle the workflow this blueprint automates on a recurring basis
• You have (or are willing to set up) an n8n instance — self-hosted or cloud
• You have active accounts for the required integrations: HubSpot CRM, Slack workspace
• You have API credentials available: Anthropic API, HubSpot API, Slack Bot Token
• You are comfortable importing a workflow JSON and configuring API keys (the README guides you, but basic technical comfort is expected)

This is NOT for you if:

• Does not access LinkedIn API — uses web_search to surface publicly available job change signals (no LinkedIn OAuth required)
• Does not send outreach emails — output is HubSpot property updates + optional Slack alert
• Does not monitor contacts in real time — runs on-demand per contact
• Does not create new HubSpot contacts — enriches existing records only

Review the dependency matrix and prerequisites before purchasing. If you are unsure whether your environment meets the requirements, contact support@forgeworkflows.com before buying.

Every pipeline has boundaries. These are intentional design decisions, not oversights — understanding them helps you deploy with the right expectations and plan for edge cases in your environment.

Does not access LinkedIn API — uses web_search to surface publicly available job change signals (no LinkedIn OAuth required)

This is intentional. We default to human-in-the-loop for actions that carry reputational or financial risk. Once your team has validated output accuracy over 20+ cycles, you can adjust the pipeline to auto-execute — the workflow JSON supports it, but the default is conservative.

Does not send outreach emails — output is HubSpot property updates + optional Slack alert

We scoped this boundary after ITP testing revealed inconsistent results when the pipeline attempted this. The agents handle what they handle well — extending beyond this scope requires custom prompt engineering specific to your data shape.

Does not monitor contacts in real time — runs on-demand per contact

This keeps the pipeline focused on a single workflow. Adding this capability would introduce branching logic that varies by organization, and the tradeoff between complexity and reliability was not worth it for a reusable blueprint. Fork the workflow JSON if your use case demands it.

Deployment follows a structured sequence. The Job Change Intent Scorer bundle is designed for the following tools: n8n, Anthropic API, HubSpot. Here is the recommended deployment path:

1. Step 1: Create 6 HubSpot custom contact properties. Create 6 custom contact properties with the fw_ prefix in HubSpot: fw_new_company, fw_new_title, fw_rps_score, fw_reengagement_brief, fw_change_detected_at, fw_last_scored_at.
2. Step 2: Configure webhook and credentials. Import the n8n workflow JSON and configure your Anthropic API key, HubSpot Private App token, and optional Slack Bot token.
3. Step 3: POST contact data and receive RPS + brief. Send contact data to the webhook endpoint. Receive either a no_change_detected response or a full RPS score with re-engagement brief.

Before running the pipeline on live data, execute a manual test run with sample input. This validates that all credentials are configured correctly, all API endpoints are reachable, and the output format matches your expectations. The README includes test data examples for this purpose.

Once the test run passes, you can configure the trigger for production use (scheduled, webhook, or event-driven — depending on the blueprint design). Monitor the first few production runs to confirm the pipeline handles real-world data as expected, then let it run.

For technical background on how ForgeWorkflows blueprints are built and tested, see the Blueprint Quality Standard (BQS) methodology and the Inspection and Test Plan (ITP) framework. These documents describe the quality gates every blueprint passes before listing.

Ready to deploy? View the Job Change Intent Scorer product page for full specifications, pricing, and purchase.