Marketo lead scoring model design methodology with two-dimensional scoring (behavior + demographic). Use when designing scoring models, creating behavioral trigger campaigns, building demographic batch campaigns, implementing score decay, setting MQL thresholds, or auditing scoring effectiveness.
From opspal-marketonpx claudepluginhub revpalsfdc/opspal-commercial --plugin opspal-marketoThis skill is limited to using the following tools:
behavioral-scoring.mddecay-patterns.mddemographic-scoring.mdthreshold-design.mdDesigns and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
LEAD SCORE = BEHAVIOR SCORE + DEMOGRAPHIC SCORE
Behavior Score (Engagement) Demographic Score (Fit)
├── Email engagement ├── Job title match
├── Web activity ├── Industry match
├── Form submissions ├── Company size
├── Content downloads ├── Geography
├── Event attendance └── Technology stack
└── Social engagement
Typical Range: 0-100 each
MQL Example: Behavior >= 50 AND Demographic >= 40
| Field | Type | Purpose |
|---|---|---|
Score | Score | Overall lead score (built-in) |
Behavior Score | Integer | Engagement tracking |
Demographic Score | Integer | Fit tracking |
Lead Status | String | Lifecycle stage |
See supporting files:
behavioral-scoring.md - Engagement scoring rulesdemographic-scoring.md - Fit scoring rulesdecay-patterns.md - Inactivity decay rulesthreshold-design.md - MQL threshold configuration