Audit advanced Works with: Any AI

Google Ads Lead Quality Audit Using Offline Outcomes

Table output Has example

• 🔍 Google Ads

Compare ad-reported leads with leads that became customers.

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[VERIFY] is an editorial flag, not a blank to fill. Wherever the prompt shows [VERIFY], check that item against a reliable source before you use it.

Audit lead quality for Google Ads. Data by campaign or ad group for the last quarter: clicks, cost, reported conversions, and from the business records how many leads were qualified and became customers, with revenue if known: [LEAD_OUTCOME_DATA]. Sales process and who qualifies leads: [SALES_PROCESS].

Calculate cost per reported lead, cost per qualified lead (cost divided by qualified leads), cost per customer, qualification rate and close rate for each campaign, showing arithmetic: 40 reported leads, 18 qualified (45 percent) and 6 customers (15 percent of leads) on a cost of 1,200 gives 30 per lead, 66.67 per qualified lead and 200 per customer. Identify campaigns that look cheap on reported leads but poor on customers, and the opposite. Small numbers of customers make cost per customer unstable, and where records are incomplete say so and ask for the missing data before changing budgets.

Return a table by campaign ranked by cost per customer, and hypotheses for poor quality (broad keywords, a form without qualifying questions, unclear price, location mismatch) with a test for each. Close the loop monthly: export leads with the source campaign, mark each qualified, unqualified or won in the business system, return the counts by the 5th, and review in 30 minutes using columns Campaign, Leads, Qualified, Won, Revenue, Cost and Cost per won customer. Decide the qualification definition in advance and record the reason for unqualified leads. Suggest bringing offline results back into the platform where possible [VERIFY the current import options] after settling privacy points. Keep claims modest: results vary and the platform's current rules decide what is allowed [VERIFY].

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Invented campaign data. Campaign A: cost 1,200, reported leads 40, qualified 18, customers 6. Cost per lead = 1,200 / 40 = 30. Cost per qualified lead = 1,200 / 18 = 66.67. Cost per customer = 1,200 / 6 = 200. Campaign B: cost 900, reported leads 60, qualified 12, customers 5. Cost per lead = 15, per qualified lead = 75, per customer = 180.
Reading: B looks cheaper on leads but its qualification rate is 20 percent against A's 45 percent. Customers are few, so treat the 180 against 200 as unproven.

• google-ads