Google Ads automation runs on the conversion data you feed it. If that data stops at form submissions or raw revenue, automated bidding optimizes toward the wrong outcome.
Why this matters more under automation
Google Ads has automated most of the decisions advertisers used to manage directly: bidding, search-to-ad matching, ad placement, budget allocation, and even creative combinations. Advertisers still control two things Google can’t decide for them: what a campaign optimizes toward, and how the business defines success. Those two inputs carry more weight because automated systems use them to make thousands of decisions per day that no pay-per-click (PPC) team can review individually.
Form submissions aren’t qualified leads
Choose the right conversion goal, then verify it beyond the platform. A lead generation campaign that optimizes bidding toward form submissions treats every submission as equally valuable, but submissions differ in value. A campaign might report a strong conversion rate and low cost per lead while sales finds that a large share of those leads are spam or unqualified. Google Ads shows the submission and doesn’t show the outcome.
Revenue and ROAS miss the full picture
The same gap applies to ecommerce. Revenue and return on ad spend (ROAS) don’t account for profit margin, new-versus-returning customers, or which products the business wants to prioritize. Two purchases can look identical in Google Ads and carry very different value to the business.
Closing the data gap
Closing that gap requires data Google Ads doesn’t have. PPC teams need to understand how leads get qualified, what happens after a lead enters the customer relationship management (CRM) system, which products carry stronger margins, and which conversions produce revenue. That usually means coordinating with the sales, analytics, or ecommerce teams that own the downstream data. When that data is available, Google recommends passing qualified or converted leads back into Google Ads as the conversion goal. Enhanced conversions for leads can connect offline outcomes to the ad interaction that produced them.
Measuring beyond platform metrics
Cost per lead (CPL) might be the primary key performance indicator (KPI) inside a Google Ads account, but qualified lead rate and customer acquisition cost (CAC) show whether those leads produced results. Ecommerce advertisers need profitability, new customer acquisition, and repeat purchase behavior alongside ROAS.
The LSA to PMax test case
The Local Services Ads (LSA) to Performance Max (PMax) migration is the immediate test case. Before accounts move into PMax, advertisers running LSA need to document performance: conversion volume, conversion rate, and cost per conversion; qualified lead rate and cost per qualified lead; appointment, booking, or close rates; revenue, conversion value, and ROAS where it applies; and performance by segment, such as location or service category. Capture that baseline over a period long enough to account for normal week-to-week fluctuation in lead volume and cost.
After the migration, check more than whether Performance Max produces more leads or a lower cost per lead. Check whether lead quality, booked jobs, and close rates move in the same direction. Give the PMax campaign enough time and data before drawing conclusions, and compare the same metrics before and after the migration to separate normal variation from a real change.
A documented baseline can’t stop performance from changing. It lets you identify what changed and whether the change reached actual business results beyond platform metrics.