AI call scoring helps contractors classify enquiries and connect conversations to later business outcomes. Start with a small, explainable rubric, link each call to the customer record, and review uncertain classifications before using the results to change marketing spend or coach staff.
Choose the questions the score must answer
Separate service fit from call handling. Service fit covers the requested trade, service area, availability and whether this is new work or an existing job. Call handling covers whether the team captured contact details, explained the next step and assigned follow-up. A customer requesting warranty help may be a poor new-sales lead but an important service call. Combining both dimensions into one number hides that distinction and can reward the wrong behavior.
Keep the source attached to the conversation
Store a stable call ID, customer ID, source label, timestamp and any permitted recording or transcript. Preserve the original source when another employee updates the record. A homeowner might discover the company through search, return through an advertisement and call twice. Report known touchpoints without claiming that one channel caused the job. Use an unknown category when tracking is missing; asking the model to infer attribution creates confident-looking errors.
Build a rubric from real examples
Review a permitted sample of calls and label them manually before introducing AI. Define categories such as qualified enquiry, wrong service, duplicate, existing customer and unclear. For example, a request for an exterior repaint inside the service area can qualify even when the caller has not chosen a start date. Include short calls, accented speech, poor audio and multiple requests. Require the classifier to show supporting transcript text and allow it to abstain.
Join scores to estimates and completed work
Use the CRM to record appointment, estimate, accepted proposal and completed-job states separately. A booked visit is not booked revenue, and invoice value is not collected cash or gross profit. Reconcile duplicate customer records before reporting conversion. If the team cannot reliably update outcomes, start with follow-up completion instead of revenue attribution. Give unassigned enquiries a named owner and deadline so the report produces an operational action, not just another dashboard.
Judge accuracy before changing the budget
Compare model labels with human review and inspect false negatives: qualified enquiries classified as irrelevant. Track classification agreement, missing-source rate, unassigned follow-ups and booked jobs by source using consistent definitions. Review calls from lost opportunities as well as wins. Limit access to transcripts and follow applicable recording and retention requirements. Expand the pilot only when the office can explain a score, correct it and trace the correction into the report.
For a framework for evaluating AI limitations and oversight, consult the NIST AI Risk Management Framework.
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