Summary
Google has outlined an approach to lead-generation advertising that uses first-party data and sales outcomes to guide its AI bidding systems. The company also described an expansion of Data Manager, a lead intent scoring pilot and journey-aware bidding for longer sales cycles.
Google is positioning stronger first-party data as the foundation for lead-generation campaigns that optimise toward completed sales rather than only initial enquiries.
In a September 18, 2026 post on its Ads & Commerce Blog, Google described an Ads Decoded episode recorded at its Rethink ROI event in New York City. Senior Product Manager Stephen Chang, Product Manager Natalia Böhm and Ads Product Liaison Ginny Marvin discussed how advertisers can provide Google’s AI with data needed to bid toward “closed-won” revenue—the sales that have ultimately been completed.
The discussion is aimed at a common challenge in lead generation: the useful outcome often occurs well after a person submits a form or makes an initial contact. A business may need to connect that early marketing interaction with later sales information before an advertising system can distinguish promising leads from low-value enquiries.
Four parts of Google’s approach
Google’s post highlights four areas of its lead-generation strategy.
Building stronger data connections
The company describes “data strength” as the ability to provide advertising systems with the first-party signals needed to understand campaign outcomes. First-party data is information collected directly by an organisation through its own customer, sales or marketing interactions.
For lead campaigns, that can include the progression from an initial enquiry to a qualified opportunity and eventually a completed sale. Google says this type of information can give its AI a more useful target than a lead submission alone.
Expanding Data Manager
The episode discusses an expansion of Data Manager to centralise first-party workflows. The post presents this as part of the infrastructure needed to bring relevant business and customer signals into campaign measurement and optimisation.
The announcement does not describe the expansion’s specific interface, supported integrations, pricing or release schedule. It does, however, place Data Manager within Google’s broader effort to make sales-outcome data more usable for advertising systems.
Testing lead intent scoring
Google also identifies a new lead intent scoring pilot. The stated purpose is to help prioritise leads with higher potential, giving advertisers a way to distinguish among enquiries instead of treating every submitted lead as equivalent.
The feature is described as a pilot, so its current form and availability may change. Google provides no performance figures for the pilot in this post.
Accounting for delayed sales cycles
The episode covers “journey-aware bidding”, which Google says is intended to train Smart Bidding on delayed sales cycles. Smart Bidding refers to Google Ads systems that automatically adjust bids using signals associated with the likelihood of a desired advertising outcome.
When the outcome arrives weeks or months after the initial interaction, optimisation based only on immediate conversions can misrepresent campaign value. Journey-aware bidding is presented as a way to account for the longer path from lead to sale, although the post does not detail the model’s implementation or supported time frames.
Why the data matters
The announcement reflects a shift in how lead-generation campaigns can be evaluated: from counting contacts to connecting advertising activity with revenue-producing outcomes. That distinction matters particularly for businesses whose sales teams qualify leads manually or close deals through processes outside an advertising platform.
Google’s post is a product and strategy discussion rather than a results report. It identifies the tools and workflows the company is developing, with lead intent scoring still in pilot form, but it does not provide measured conversion gains, revenue improvements or comparative campaign results.
For advertisers, the practical requirement is a reliable connection between marketing interactions and later sales records. Without that connection, an automated bidding system has less information about which leads became valuable customers. With it, Google says its AI can be trained against the outcomes that matter to the business.