01Define a useful outcome before choosing a metric
For a local service, a qualified enquiry might be a real person requesting an offered service within the service area. For an online shop, a completed order is more meaningful than a click on the checkout button. Agree how spam, duplicates, cancellations and enquiries outside coverage will be handled.
02Record the baseline and the changes
Choose a comparison period that fits the business’s seasonality and volume. Record known outages, campaigns and opening-hour changes. Keep a release log with the page, change, intended customer benefit and publication date. When several things change together, explain that the data cannot isolate the effect of each one.
03Read search data in context
Use Search Console to investigate queries and pages, subject to the data available in the account. Separate searches for the business name from broader service discovery when practical. A higher average position with fewer relevant enquiries is not automatically success. Check landing pages and customer fit before deciding to repeat a tactic.
04Describe AI visibility evidence precisely
For a manual observation, record the tool, date, question and cited page. Treat that record as a sample. If a visit includes an identifiable AI referral, compare it with the landing page and subsequent customer action; missing referral information limits attribution. Google’s Search Console reporting includes its AI search features within overall Web search performance, so do not label every Web click an AI click.
05Finish each report with a decision
Use the evidence to choose a next action: repair a page, clarify a service, improve the contact route, or gather more data. Give the action an owner and a completion check. When data is too sparse to support a conclusion, say what remains uncertain and what observation would change the decision.