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Join host Ginny Marvin as we unpack the strategic essentials for high-performing retail ads across Search, YouTube and AI-powered surfaces.
Episode overview
Conversational shopping in AI Mode. Virtual try-on. Agentic checkout. Business Agent on Search. Google Lens in YouTube Shorts. Shoppable CTV.
The secret powerhouse behind them all? Your product data. It’s no longer just a list of SKUs; it’s the high-resolution infrastructure for the next era of retail.
In this episode of Ads Decoded, host Ginny Marvin is joined by Firas Yaghi, Global Product Lead for Retail Solutions, and Nadja Bissinger, Group Product Director for Retail on YouTube. They separate the hype from the strategic essentials, discussing the practical steps retailers can take now to deliver high-performing ads across Search, YouTube and new AI-powered experiences.
In this conversation:
- The product feed as foundation: Enriching your data with lifestyle images and rich descriptions to power multi-modal discovery.
- From competing to compounding: Why it’s time to stop viewing campaign types as competitors and use a surround sound-style approach.
- The signal shift: How Lookalikes have changed from rigid targets to multi-dimensional signals that give the AI the flexibility to find your next best customer.
Want Ginny’s key takeaways and tips from this conversation? Subscribe to the Ads Decoded newsletter.
Additional resources
Powering modern commerce with Merchant Center
Discover best-in-class feed practices and enable advanced features.
Create and capture demand with Demand Gen
Get best practices from product experts in this Ads Academy webinar.
What’s new in Google Ads for retail
See the latest retail ads innovations from Think Retail 2025.
Join Ads Product Liaison Ginny Marvin as we unpack the practical steps retailers can take now to deliver high-performing ads across Search, YouTube, and new AI-powered experiences.
Transcript
Nadja: People aren’t one dimensional. They’re multi-dimensional. So this change from using more of a rigid target setup towards more of like a signal perspective, is really allowing the algorithms to do its best: looking at your users and your audience in a more multi-dimensional way and finding the right audiences for your targets that you’ve set in the campaign, the KPIs and the overall bidding strategy that you want to pursue.
Ginny: I’m Ginny Marvin, Ads Product Liaison at Google, and this is Ads Decoded. Today, we’re going to be talking retail advertising, why your product data is only going to get more important for powering high-performing ads across search, YouTube, and new AI experiences, what Demand Gen with product feeds enables, more behind the Lookalikes change from target to signal, plus shoppable CTV formats and a whole lot more. Joining me are Firas Yagi, Global Product Lead for Retail Solutions and Nadia Bissinger, a Group Product Director focused on retail ads on YouTube.
I asked how retail advertisers should be thinking about the portfolio of campaign types from standard Shopping to PMax to demand gen. And as we’re in the very early days of agentic commerce, I wanted to know
What advertisers can be doing now to prepare? Whether you’re an e-commerce only direct to consumer brand or an omni-channel retailer, this conversation is packed with insights and tips to drive more conversions from your site, your app or your stores. Let’s get into it.
Nadja and Firas, welcome and thanks so much for joining Ads Decoded. We are going to talk all things retail advertising.
Product data is where I want to start this conversation. Product data supplied in Google Merchant Center has long been foundational underpinning of Shopping ads in Search, of course - but where else is product data being used? What formats are being enabled, et cetera, that people may not be aware of?
Firas: The Merchant Center feed, it’s the backbone that empowers both ads and organic experiences. So with AI and agentic experiences becoming more and more prominent, ensuring merchants and retailers are submitting their most robust product data is really critical to increase brand and product discoverability.
So a few examples, Ginny, of where the product feed information is currently being used beyond shopping ads would be free listings, shopping experiences on Gemini and AI Mode, virtual try-on, Google Lens, and then Business Agent and brand profile. So quite a few places.
Ginny: I’m glad you touched on the free listings. Obviously, that’s also powered through Merchant Center. Also on that: are there attributes that people are overlooking or any common mistakes that people are making in their feed optimization efforts that you see regularly or want to make sure that people are aware of?
Firas: Yeah, I usually recommend starting just with the basics like clearing account disapprovals, adding GTINs. I know this sounds obvious to some, but these are often the biggest hurdles we tend to see. And I would say, you know, as I mentioned earlier, in this age of AI-driven shopping, you also need to make sure you’re enriching the feed with as much product content as possible.
So that would mean providing more images, lifestyle images specifically, rich titles, descriptions,
product type, product highlights - anything that could help our multimodal experiences. And then finally, it’s really important to not forget to highlight your differentiators. So think of ratings, fast shipping, and exclusive promos and pricing.
Ginny: I was just gonna say the product differentiation piece is so important and also the asset breadth. Again, we talked about that a lot, but not necessarily in the context of product data.
Nadja: Yeah, I just wanted to add a little bit to that too, that with all of these use cases expanding and our ad formats, for example, and organic experiences being available on different surfaces on different devices, Firas’ point about the quantity of the data is really important.
But I want to also stress that the quality matters too. So for example, if you legacy-wise started running a Google Merchant Center feed for just like Search experiences per se. Think about how these assets today show up on larger surfaces such as CTV. Think about the resolution that it may have and what your tracking setup needs to look like from a link perspective too of where you’re linking your products to.
So those are important considerations as well when you’re adding your attributes and where we see people having trouble sometimes to expand to the full range of all the possible use cases.
And then there are some really new and cool features as well, such as checkout links that some of the surfaces are supporting already that you can just easily set up there to provide a very seamless experience for your users that ends up in your checkout or cart experience with only a few clicks from the ad’s impression.
Ginny: Really good point on thinking about the, not just the different formats, but the surfaces that your ads can be appearing on and also like thinking ahead and new checkout experiences, all these new experiences that AI is also enabling: super important.
I wanna talk about tools a little bit. Merchant Center for Agencies, I just wanna quickly note that We just announced that launch. It’s currently available in the US and Canada. Very exciting if you’re working with multiple clients: this is a new interface to monitor account health, item level issues and opportunities across your entire client portfolio from a single login.
I also want to talk about Merchant Advisor and the agentic tooling here. Firas, if you have anything to share on where we currently stand on Merchant Advisor?
Firas: We’re very excited about Merchant Advisor. And for those who don’t know, it’s a new AI-powered chatbot directly in the Merchant Center UI. So think of it like a personalized AI growth partner for merchants who are trying to get timely, proactive help. And it supports them to unlock business success. I do want to note that it’s still really early and it’s currently only available for a limited group. But stay tuned. as it’s gonna scale much faster and further.
Ginny: We announced Ads Advisor, Analytics Advisor at GML last year. In terms of Merchant Advisor, any early things you can share in terms of how you’re seeing people using this or when they’re going to get access to this, what kinds of ways that they should be thinking about using these tools? Merchant Advisor specifically, but agentic more generally.
Firas: Yeah, I think the idea behind it is to make it as easy as possible for merchants. To your questions where we were talking about what you should do from a feed perspective. The idea is to just have an always on helper there with you to help you throughout the journey and make it extremely easy and simple for merchants to have success on our platforms.
Ginny: Great. So a helper. I want to talk a bit about now campaign types. And we get a lot of questions about how advertisers should be thinking about using standard Shopping. PMax, Demand Gen, these campaign types together. And so I want to start there. How should advertisers be thinking about the portfolio of opportunities from a campaign type perspective?
Firas: I think the role of each campaign really depends on your high-level objective. So whether you’re prioritizing cross-channel efficiency, granular channel control, or hybrid approach that balances top-line sales with OKRs like specific search coverage.
So for example, for organizations that are focused on maximizing performance across the entire Google ecosystem, we recommended a performance-first approach. So that’s the tree of PMax, AI Max for Search and Demand Gen.
If your strategy requires more surgical precision and you feel like you need manual levers to prioritize. to prioritize different goals, we recommend AI Max with standard Shopping and with that Demand Gen with channel controls. That way you could use specific placement settings to granularly manage how often your brand appears on Display, on YouTube.
And then finally, so if you’re trying to do a mix of scale and stability, think a blend of campaign types allows you for a complimentary surround sound strategy. Think of it that way. So use AI Max plus DG, leverage PMax and Shopping for different use cases.
Ginny: I like that framing. And it also highlights that you can build this to your needs and your approach can be custom tailored to what your goals are, what your budgets are, what your scope is, and make it work for you in whatever kind of, combination that makes sense.
Nadja: Just to weigh in, Ginny, on that very quickly. So I think the best summary I’ve heard as well was to stop viewing them as competing campaign types, but more as an integrated ecosystem and really dialing based on your strategy between compounding loop of awareness, intent, and conversion goals as well. And so it really brings your strategy to life. And it’s more of like a tool set that brings it to life than like a rigid, this is how you need to combine all of them.
Ginny: Yeah, and that’s a really good point. Also, you hit on the key concept that you typically are going to have more than one goal. Yes, ultimately, sales are going to be that goal. But you’re going to have people who are still in the discovery phase or research phase. And being in all those places with the right creative, the right time is how you can use the portfolio in ways to maximize that impact across the spectrum.
And also, Firas, I thank you for bringing up Search, because oftentimes I’m in the headspace of Shopping ad formats and image-focused formats. And obviously, text ads play a big role in the discovery and conversion cycle as well for merchants.
Thinking about Performance Max and standard Shopping, we changed the trumping logic last year from strictly prioritizing your PMax campaign when you have both campaign types in the same account targeting the same product inventory. Now the campaign with the highest ad rank will serve. I’m just wondering, is there anything in terms of how advertisers should be thinking about using the campaign types together?
Firas: Yeah, definitely. So now you could use both campaigns together to create a more nuanced strategy. So for example, you could route specific traffic between brand and non-brand or use one campaign as a catch-all strategy. So some top of mind considerations.
One, from an account level perspective, as you know, spend could shift between the two campaigns. So I highly recommend always evaluating total performance at the account level.
Performance is expected to be neutral to positive at the account level, right? So it’s meant to provide flexibility, not to impact ROI.
Inventory overlap management is important. So if you want total certainty on which campaign serves, the best practice is to ensure no product overlap. So for example, having specific skews only in one campaign type.
Finally, bidding is your control here. is your control lever. Your tROAS, tCPA targets are your primary tools to help you route that traffic. So a more aggressive target in one campaign will cause it to win more auctions and you’ll have more traffic against the other one. So these are like some different top of mind things we’ve been sharing with lots of folks.
Ginny: Yeah, that was often a question of what, when we’re talking ad rank, what are we actually talking about? Well in this case, it’s the bid and your targets that are going to be that control mechanism. Thanks for highlighting that.
Demand Den with product feeds: what does this enable? And why should merchants really be proactively thinking about incorporating their product feeds in their Demand Gen campaigns?
Nadja: Product feeds are enabled by adding your Google Merchant Center into the Demand Gen campaign. And it’s pulling rich product assets directly into your formats that serve on the different surfaces that Demand Gen is supporting today. And it really unlocks a lot of performance gains on average for advertisers that do that. I think the number that we have communicated in the past about this is 33 % of conversion uplift.
So it’s a really strong performance lever for you and it turns your ads into a digital storefront as well and brings your products at the forefront of your messaging too. The strategies that it enables are very wide. it’s a range of different creative strategies that you can enable with it. Like tailoring exactly that product to a product that’s shown in a video, for example, on another marketing asset to just letting it run on its own and let our AI algorithms figure it out, like which product should be matched with what user.
Ginny: And I just want to quickly recap that DemandGen runs on YouTube, Gmail, Discover, Display Network. What else am I missing? Maps, I guess less so for the product feed standpoint. Is there any product feed integration with Maps at this point?
Nadja: There are some considerations, yes.
Ginny: Ok, all right. Ok. The other recent change on Demand Gen that I want to touch on, Lookalikes are now used as a signal instead of a hard target. Is there anything you can share on why we made this change and what it might mean for merchant advertisers?
Nadja: People aren’t one dimensional. They’re multi-dimensional. There are a lot of different signals and reasons why a user would want to convert. So this move from a more rigid look-alike targeting only to taking it into account as a very strong signal that our algorithms are using to find the right user for you.
Because if you think about it from a perspective of the cutoff point that you’ve defined before for your Lookalike audience. If there’s still a very likely good user for you that you could acquire and find with your campaign, but it’s outside of that Lookalike range, you would still want to go after that.
So this change from using a more of a rigid target setup towards more of like a signal perspective, is really allowing the algorithms to do its best and looking at your users and your audience in a more multidimensional way and finding the right audiences for your targets that you’ve set in the campaign, the KPIs and the overall bidding strategy that you want to pursue.
Ginny: It gets at a broader theme of using indicators data as signals versus targets. This is a big change, even though we’ve been on this for a while thinking about things like keywords. Would you say that this change speaks to that, this shift of really letting the algorithms and AI find the best customer in the right context, specific moment versus having the advertisers say, is exactly who I want to go after.
Nadja: Yeah, I definitely think so. I want to be very clear that we still want the advertisers to control and give input of who they want to go after. But it’s not always that one dimensional, that it’s only one signal or one indicator of why a user could be suitable for the audience.
We know there are very strong segments that work very well and advertisers really know their audience best too. But it’s more about like leveraging the full potential and also the knowledge of like, the signals that advertisers can provide us, like what we’re seeing in terms of context as well. And it may not just be that I’m interested in suitcases in the past, or I look very similar like somebody who was interested in suitcases in the past. It’s also more about like, I am watching maybe a video on YouTube right now that talks about travel and suitcases and that’s the right moment to really like, show that ad.
And these moments you would have missed before by just like really relying on what we know about the user already. So there’s a lot like that we can learn and iterate from.
Ginny: Yeah, OK. And so we can look at the universe of signals versus a singular prescriptive direction. And that then gives the system the flexibility it needs to be able to find more of the people that you are looking to reach.
I think that’s really important because this is a question that we get a lot. Actually, I just had a conversation with a group of practitioners this morning. This exact thing came up about the idea of, how do I work with these systems and these new campaign types to make sure that I’m giving the system the right signals that I know about my business. and my goals and my audiences.
Nadja: And if that question comes up of like, what can advertisers really do to steer that telling the system what a good user looks like and how they perform in terms of like, setting up your conversion tracking really robustly, right? Having a really good understanding of like, the deeper experiences that you want to create and want users to take on your website. I think that’s really important.
So I can’t stress enough, like this is not necessarily taking the control away in some way. It’s really about just shifting towards what we think really matters from your business perspective to give to the campaign and input as well. The Lookalike signals are very valuable, conversion data points - making sure that all works together is really guaranteeing the success of your company.
Ginny: It’s not that there aren’t knobs. The knobs just might be different now than they have been in the past.
We’ve covered measurement and bidding in depth in previous episodes. I recommend checking out those if you have missed them. But I want to be sure to highlight some of the specific features and tools for retail or advertisers. Let’s start with measurement. features that should be on merchants’ radar.
We’ve talked about conversion data sharing, obviously, but what else should be top of mind for retailers in terms of measurement?
Nadja: We hear a lot of feedback about cross-selling. The product that gets people in the door is not always the final product that’s being purchased. So last year in 2025, conversions with cart data have actually expanded to Demand Gen and are now visible there as well on a product level.
So that’s a really valuable setup for advertisers to understand when cross-sell happens and how it happens because it’s something natural. We don’t always like, go into a physical store either with the intent to only buy one specific product, but product discovery and creating some intent is very important here too. So that’s number one.
We also had product-level reporting in Demand Gen. We also have it in PMax and other campaign types, but we’re starting to adjust a little bit more to like the different format attributes that we’re seeing. A PLA [Product Listing Ad] ad g.com [Google.com] doesn’t look the same as an in-stream ad on YouTube or even like an ad on Maps.
And so the product-level reporting is starting to become more flexible. There’s an exciting upcoming launch there too where you can find new metrics, new dimensions on how you can explore how your products are performing in the mountain.
So those are two of the more product specific features. And then when it comes to conversion strength and measurements, I alluded to it earlier, like the more information you can give us of what good performance and success for users looks like, the better the outcome of the campaign. And that also matches with different ways of how people are purchasing and where they’re purchasing.
So if you, for example, a business that also has an app versus just a website, thinking about reporting these app conversions, using them in optimization in your Demand Gen campaign, a PMax campaigns, like across like all like your web efforts too, can really make sure that we see the full value that this campaign drove and we can actually optimize in that way for you too.
So it’s a very, very important recommendation to think about like, not just the website on its own, but if particularly your business is spanning across different purchase points to bring that in, including offline conversions, which I think Firas can tell us a little bit more about, but yeah.
Firas: I just cannot stress how critical it is to have an offline strategy. The vast majority of sales still occur in stores. I think in the majority of countries, it’s still above 80%. So the first step there is to start with a strong measurement foundation with store visits and store sales. And once you have the measurement in place, I would recommend a dual approach.
The first one is to drive combined online and offline results using omnichannel bidding across Search, PMax and Demand Gen and also leveraging local inventory ads which drive both your online and offline sales.
I think a second one is to tap specifically into offline traffic and drive increased sales there. So one side I’d like to share is that every month we have over a billion people visiting Google Maps and Search. to find new businesses, to plan trips and you know find a destination mid-journey. and we have a great product for advertisers looking to tap into that local intent and drive store traffic called PMax for Store Goals. It’s very easy to use, it allows advertisers to optimize towards offline goals across search, YouTube, Google Display Network and Maps.
We also are promoting a new version called PMax on the go which runs exclusively local formats across Google Maps and Waze and Search to drive even more targeted impact.
Ginny: Great. Anything on the bidding front that should be top of mind as well.
Firas: I think on bidding, one thing I also would love to highlight is for retailers looking to acquire new customers. For that, we have two very specific levers that are really helpful.
The first one is, you know, activating new customer acquisition goals in Google Ads. So you could, you could choose either a bid higher for new customers or only a bid for new customers. And the second lever is to leverage first-party data. So that would be uploading your existing customer lists. This allows Google’s AI to build a lookalike profile of your best buyers and more importantly exclude your current customers from seeing acquisition focused ads.
Ginny: The new customer acquisition reporting has also been updated with new columns to be able to actually see that contribution, making it much easier, much more intuitive to actually set that value for the new customer and be able to see the incremental impact.
Anything else to note in terms of optimizing for both online and offline store experiences and conversions?
Firas: I think our products, our design, and we’ve seen time and time again where they lift all boats, right? For local inventory ads, we have seen when advertisers are using it, they’re able to drive not just online store impact, but also offline. Especially with the pickup in store format.
Nadja: So yes, definitely use your local inventory ad listings, your product feed as well to really connect to your omnichannel bidding too. Because the visual and what we show the viewer that this is available close by to them as well really matters to drive the performance impact.
Ginny: Let’s turn to YouTube specifically. We talked about Demand Gen, and PMax. And we are going to dive deeper into YouTube partnership ads and working with creators in our very next episode, actually. So we’ll touch more on those. tools and features next time.
But YouTube is obviously very well known for driving brand metrics, attention, awareness. But how has YouTube evolved as a performance driver?
Nadja: YouTube has fundamentally transformed from more of a top of funnel awareness platform, at least how it was perceived by the advertising industry into a full funnel AI driven performance engine. And this was impacted by a few developments over time.
All of you have heard at this point about Demand Gen campaigns, the evolution from traditional campaigns via Video Action to Demand Gen campaigns has really allowed Google’s algorithm to optimize towards a different set of goals as well across different inventory and really optimizing the ROI that you can get out of these campaigns.
Simultaneously, we’ve also added more inventory that is really highly engaging, has vertically perfectly suited for certain industries as well, such as Shorts. So Shorts has become a big, big engagement driver on the platform too. And if we’re looking at the metrics and all the improvements in terms of product development as well that were made in the last year, we’re seeing around like more than 26% year-on-year increase when it comes to conversions per dollar spent on Demand Gen. So it’s really evolved as a performance driver overall. And closing the loop on that with product feeds as the creative add-on, you can really turn your ads into a storefront and drive purchases.
We also have a lot of organic initiatives that really support this notion of YouTube as a shopping destination and a platform as well across the different funnel steps.
And so the YouTube Shopping affiliate program was established a couple of years ago. It’s launched in some of our key markets. It’s going to expand in the future even more. And that has really helped even from a viewer perspective to position YouTube more as a stronghold for shopping engagement. And we know that for a lot of viewers YouTube is the number one choice for product reviews and product information when it comes to making purchase decisions. So it’s just allowing to leverage that as well.
Ginny: So it’s both in terms of the formats available and also the way that consumers are using YouTube is also evolving.
Attributed Brand Searches, I’m going to talk about. Can you share a quick overview about what Attributed Brand Searches are, what it helps solve for? And how else should advertisers account for assisted incremental impact of their YouTube advertising?
Nadja: Attributed Brand Searches is a new always-on metric that we announced at GML last year as well. It helps advertisers to understand the impact of the video ads on organic search behavior. And it’s measuring basically the number of searches for brands by users who saw the advertisers video ad.
It’s zero setup on your side. It’s leveraging the direct integrations that we’re having between the platforms as well. And it can really link the top of funnel view to video view with bottom funnel user engagement to close the loop on understanding like, what full funnel experience and performance your campaign drove.
Ginny: It’s essentially giving a number to something that we always knew was happening, but weren’t able to actually show or demonstrate to clients or stakeholders that that’s happening. And it also underscores the conversations around YouTube being under-attributed in terms of performance impact.
All right, shoppable CTV: we talked about rethinking how your ads are showing up, including in the living room.
Nadja: Reaching shoppers on the big screen has become increasingly important. I mentioned earlier that having products in focus and turning your ads into a digital storefront is now available with product feeds. And which bigger screen can you imagine than CTV? It’s now available in Demand Gen campaigns and Performance Max campaigns. So once you adopt your product feed, also automatically just brings product information in view for CTV ads.
initially it wasn’t very interactive for the users. So we’ve definitely you know. Improved that experience overall and increasing in activity that users can have with the ad formats scrolling through different products. And there’s so much more in terms of action they can take with QR codes in view. We see a lot of conversion volume coming from CTV ads overall as well.
Ginny: And so - I’ve gotten questions. - I saw QR codes on YouTube ads, how do I make that happen? And it’s just automatic. There’s nothing you need to do on your end to set that up. We just automatically generate those QR codes.
Nadja: Just tell us where you want them to land, either by filling in the right product link in your Google Merchant Center feed or filling in the final URL in your Google Ads account.
Ginny: All right. I want to talk about some of the new experiences that are coming online. And Direct Offers is one option that’s in pilot in AI Mode. We’ve talked about this in previous episodes, but just a quick recap. This is a way to be able to show up in the moment when somebody is researching a product that you sell. You can show a specific offer that you control in your merchant feed to users in the moment. We are also showing retail ads in AI Mode and AI Overviews.
And there’s a lot happening with conversational and agentic commerce. I want to talk about what’s on the horizon, what advertisers should be doing to be prepared for these new experiences, whether that’s product data quality, asset strategy ,in the near future.
Firas: We like to frame agentic commerce as what you should do now, what you should do next, and then what you should do in the future. So in terms of now, things you could do today to be best prepared is one, ensure your feed has rich product information with high-quality text images and differentiating attributes as we covered earlier.
And be sure to connect to your first-party data and update your tag and data managers and implement conversion with cart data as Nadja was sharing before.
And then we could move to the next phase. So that would be to prepare your infrastructure by upgrading to Merchant API for real-time inventory. As you can imagine, this is very important and critical in an agenetic commerce world. And you can also prototype with UCP open-source. So UCP is our Universal Commerce Protocol. And that will help you prepare for the integrations on Google with things such as checkout. You can learn a lot more on ucp.dev.
For the future phase, you can unlock agentic commerce on your surfaces by using Gemini Enterprise for Consumer Experience to deploy agents on your own website. And then you can also start to think through your AI governance framework, how you want to test and evaluate success metrics on your AI strategy. So lots of things that could be done. And the great news is that you could start doing a lot of that today.
Ginny: And I think also just from an even basic standpoint, we’ve announced things like a dozen of new attributes will be coming to Merchant Center, conversational attributes, I think is what we’re calling them in pilot now. It’s just another reason why having really in-depth, structured product data is gonna be increasingly important.
There’ll be a lot more coming on conversational and agentic commerce in the coming months. So there’s a lot to stay tuned for in that realm.
I want to wrap up with your top takeaway for retailers to make the most of their budgets and data today.
Firas: Yeah, I would say, you know, do everything you can to optimize your feeds. Use our AI-powered campaigns to get the best of Google. Give us the best data you can on your first-party data. And then finally, you know, I highly recommend leveraging insights within Google Merchant Center, specifically price competitiveness, missed opportunity reports, which will offer you very actionable data to refine your merchandising strategy. and it also helps uncover high potential campaign optimization for more growth. Nadja?
Nadja: So I think Firas has got it all. The one point that I wanted to stress is, yes, get your GMC feed ready. Use our AI-powered campaigns. But make sure that you pair them with the product feed as well and turn your ads into an actionable storefront. So that’s really important. And think about this across the funnel. We have product feeds available for all campaign types right now. So leverage it.
Ginny: All right, thank you so much for this conversation. I have learned a lot and I’m sure others will as well.
Nadja: Thank you, Ginny.
Firas: Thank you, Ginny.
Ginny: All right, here are my top takeaways from that conversation. Hopefully help you refine your retail strategy. First, your product data is not just for shopping ads anymore. It’s the backbone of everything from free listings, Lens, virtual try-ons, and new AI-driven experiences in AI Mode and in Gemini and a whole lot more.
So to increase discoverability, you wanna move beyond the basics and include things like high resolution lifestyle images, rich descriptions, differentiating details that will shine across surfaces and formats.
Second, stop viewing campaign types as competitors. I thought this part of the conversation was really interesting. Thinking about your campaign types as part of an integrated strategy to compound your outcomes. And you can use the surround sound style approach to balance automated efficiency with more surgical control. Mapping to your specific goals across the funnel from brand awareness to conversion.
Third, targeting has shifted from rigid targets to multi-dimensional signals. The move to using Lookalikes as signals in Demand Gen, for example, gives the AI the flexibility to find your next best customer. This is a big shift, but you want to support this by providing robust first-party data, ensuring your conversion tracking, including in-app and offline data, clearly tells the system what success looks like for your business.
We’ve talked about this a lot in previous episodes, but again, really focusing on that robust first-party data, making sure your conversion tracking is comprehensive. And certainly if you have an app, make sure that that’s all set up and you can do that in the app hub in Google Ads.
So. Your action item: do a feed and data source audit in Merchant Center this week, specifically focusing on quality and breadth. Check disapprovals and things like GTINs, then go further. Again, look at ways to enrich your data with lifestyle images, richer descriptions to show up better in these new experiences.
As Nadja mentioned, assets that looked fine on a mobile screen might need better resolution to really shine on a 65-inch TV screen. So as retail gets more conversational and agentic, merchants with the most structured, high-quality data foundations will be positioned to win. If you have any questions about retail strategy, ad formats, or merchant center, anything else we talked about in this conversation,
I’d love to hear your questions. Look for the companion Ads Decoded newsletter on the Google Ads LinkedIn page a day or so after this episode airs. Post your questions in the comments or email us at adsdecoded@google.com. Thanks for tuning in and until next time.
