THE LEAD SCORING ENGINE 2027

• Collect enquiry information through a suitable form, webhook or connected source.
• Validate required fields and normalize incoming data.
• Identify incomplete records and possible duplicates.
• Preserve the original enquiry information for later review.

• Use OpenAI to interpret relevant unstructured enquiry text.
• Extract evidence related to Fit, Intent and Engagement.
• Use structured output and a defined JSON schema.
• Treat incoming lead text as untrusted data, never as instructions that override the workflow.

• Apply the defined point values to the available evidence.
• Calculate Fit, Intent and Engagement using deterministic rules.
• Apply relevant penalties only when supported by confirmed information.
• Keep missing information distinct from confirmed negative evidence.
• Record the scoring version and supporting evidence.

• Use the configured score thresholds to assign a priority category.
• Route uncertain or contradictory cases to human review.
• Apply follow-up actions appropriate to the business process.
• Do not treat a high score as automatic marketing consent.

• Map scoring information to suitable CRM fields.
• Record lead status, score, evidence and relevant timestamps.
• Configure notifications and follow-up processes.
• Monitor errors, retries, duplicate handling and workflow activity.

• Make.com modules, interfaces and integration options can change over time.
• OpenAI API usage is billed separately from a ChatGPT subscription.
• API costs and automation-platform costs depend on actual usage and selected plans.
• Google Sheets can be useful for prototypes and controlled testing, but should not automatically be treated as a public customer database.
• Production workflows need suitable error handling, retry logic and duplicate protection.
• Check current provider documentation, pricing and terms before implementation.

• Fit, Intent and Engagement scoring categories.
• Defined point allocations and penalty structure.
• Hot, Warm and Cold priority thresholds.
• Review handling for low-confidence or contradictory information.
• Guidance on missing data, evidence and avoiding double counting.

• Structured prompt design for lead information analysis.
• Guidance for extracting evidence from unstructured text.
• JSON output structure and validation considerations.
• Separation between AI interpretation and numerical score calculation.

• Workflow architecture and module planning.
• Lead capture and data validation stages.
• Scoring, routing and notification logic.
• Error handling, retries and recovery considerations.

These are instructional build sheets and implementation references. They are not represented as a ready-to-import Make.com scenario bundle.

• Lead identification and qualification fields.
• Score, category, evidence and review status.
• Follow-up status and relevant timestamps.
• Reporting and performance monitoring considerations.

• Common workflow and data validation problems.
• Duplicate handling and idempotency considerations.
• Testing, calibration and continuous improvement.
• Launch preparation and ongoing maintenance tasks.