JSON93 downloads

Manufacturing Lead Scoring Rules (JSON)

Plug-and-play JSON config for manufacturing lead scoring. Prioritize by company size, tech stack, and engagement.

JSON Configuration

Ready to import directly into your CRM or marketing automation tool

"{\n  \"$schema\": \"https://optif.ai/schemas/lead-scoring-v1.json\",\n  \"name\": \"Manufacturing Lead Scoring\",\n  \"version\": \"1.0\",\n  \"description\": \"Lead scoring optimized for manufacturing industry\",\n  \"industry_specific\": {\n    \"plant_indicators\": {\n      \"multiple_facilities\": 15,\n      \"expansion_signals\": 20,\n      \"equipment_refresh_cycle\": 10\n    },\n    \"production_volume\": {\n      \"high\": 20,\n      \"medium\": 10,\n      \"low\": 5\n    }\n  },\n  \"role_scoring\": {\n    \"Plant Manager\": 25,\n    \"Operations Director\": 20,\n    \"Procurement Manager\": 15,\n    \"Quality Manager\": 10,\n    \"Production Supervisor\": 5\n  },\n  \"buying_signals\": {\n    \"rfq_submitted\": 30,\n    \"spec_sheet_download\": 15,\n    \"case_study_manufacturing\": 10,\n    \"calculator_used\": 20\n  },\n  \"timing_signals\": {\n    \"fiscal_year_end\": 15,\n    \"budget_cycle\": 10,\n    \"expansion_announced\": 25\n  },\n  \"disqualifiers\": {\n    \"wrong_industry\": -50,\n    \"too_small\": -30,\n    \"competitor_customer\": -20\n  }\n}"

💡 Usage Tip

Paste the copied JSON into a text editor, save as .json format, then import into your CRM.

Usage Tips

  • 1Review the complete structure before importing into your system
  • 2Back up your current configuration before applying changes
  • 3Start with default values and iterate based on your data
  • 4Share with your RevOps team for validation before go-live
  • 5Monitor performance metrics for 2 weeks post-implementation

Example Use Cases

Scenario

New RevOps hire needed to understand existing scoring logic quickly.

Result

Downloaded this config and used it as documentation. Onboarded and making improvements within first week.

Scenario

Team wanted to A/B test different scoring approaches.

Result

Used this config as control group, tested variations. Found 23% improvement in MQL-to-SQL conversion.

Who It's For

👤 Manufacturing-General Sales Ops

Pain Points:

  • Inconsistent follow-up from team members
  • Difficulty measuring ROI of sales activities
  • Reps spending too much time on manual tasks
  • Pipeline visibility and forecasting challenges
  • Slow response times to inbound leads

Goals:

  • Ensure 100% follow-up compliance
  • Prove ROI of sales initiatives
  • Free up 10+ hours/week per rep
  • Improve forecast accuracy to 90%+
  • Achieve <5 minute lead response time

How to Use

  1. 1

    Review the configuration

    Understand each field and how it affects behavior.

  2. 2

    Customize values

    Adjust thresholds, weights, and rules for your use case.

  3. 3

    Test in sandbox

    Apply the config to a test environment first.

  4. 4

    Monitor results

    Track how the configuration affects outcomes.

  5. 5

    Iterate based on data

    Refine values based on observed performance.

Related Resources

Frequently Asked Questions

Click "Copy Template" to add it to your Optifai workspace. Then customize the trigger conditions, email content, and actions to match your specific use case and brand voice.
Yes, all templates are fully customizable. You can change triggers, conditions, email content, actions, and timing. Your modifications won't affect the original template.
This template works with major CRMs (Salesforce, HubSpot), email providers (Gmail, Outlook), and communication tools (Slack, Teams). Check the requirements section for specific integrations.
Optifai automatically tracks actions through to revenue outcomes. The ROI Ledger shows which automated actions led to meetings, opportunities, and closed deals, with holdout-measured attribution.

Have more questions? Feel free to contact us.

Automate Your Sales with Optifai

Use this template in Optifai for fully automated trigger detection → email sending → performance tracking.