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With AI, Google is becoming more important than ever

Joseph Kerschbaum & Chris Brook
Joseph Kerschbaum & Chris Brook
SVP, Search & Growth Labs & VP, Google Marketing Platform
14 min read
28 June 2023

In a world where context and consolidation are king, AI is killing the keyword.

Unsurprisingly, artificial intelligence was front and center at Google’s IO & Marketing Live summits. While the “AI revolution” may already sound cliche, it’s evident our industry is still very much in its infancy — cautiously optimistic for change while simultaneously aware of AI’s role in ensuring a positive user experience and ensuring brand safety, compliance, and privacy. 

Though Google seemingly lost the AI first-mover advantage to Microsoft, many of its recent announcements suggest it is distinctly positioned to lead the charge from a marketing and advertising perspective.

However, can there truly be a first-mover advantage to AI in marketing when the rate of evolution is so heavily reliant on broader data inputs? 

In this piece, we’ll review:

  • How we’ve arrived at a point of increased consolidation
  • Discuss why context is now king
  • Share how any brand or advertiser can compete in an AI-driven world
water reflection

The slow decline of the keyword

Keywords have been the foundation of marketing for years. Without other signals, the keyword was the closest cue we had to intent. Google’s search engine has remained marketers’ primary channel to research and leverage keywords to ensure content was seen by the people most likely to purchase. 

But the marketing world is changing. Search engines are increasingly more sophisticated, serving results more relevant to the user’s intent, even if the keywords in a query are not explicitly used. Why? Because AI is being trained to understand the context of a query, not the keywords themselves. 

Generative search experiences built on AI/large language models (LLM) across search, shopping, and other channels, for example, will provide answers to your queries that one website might not answer. Instead of a dozen links, “snapshots” of information from multiple sources are summarized in one place. Of course, this has huge implications for consumers. But as marketers, we’re even more excited for the possibilities it enables to offer revolutionary experiences and boost brand relevance. 

Here’s what a generative search example might look like in action.

Say I have an upcoming vacation and need a new suitcase. I search for “What are the best large, durable suitcases with wheels?” Historically, I may have been categorized as an “in-market” individual for new luggage and received a list of sponsored ads and links to ranking pages.

However, the context behind my search is much more idiosyncratic and tells a fuller story. 

Previous searches may indicate I have a family of four, and we’re leaving on a trip to Florida next week. I might not need one suitcase, I might need three, and I might need them at my door two days before we leave so we have time to organize and pack. I may not have organized transportation, parking at the airport, nor a rental car upon arrival. My hotel is booked, but we still need to buy tickets to Universal and Disney and plan a beach day.

Based on this context alone, how many opportunities did you recognize for brands to leverage this generative experience to show up at a relevant moment in time? 

  • Luggage brands like Away or Rimowa use reviews and lifestyle imagery to show me a “large, durable suitcase with wheels.” Seemingly equal in quality and size, Away offers me free rush shipping. My flight is next week, so time is of the essence.
  • Based on my zip code, Uber, Lyft, or my local car service knows approximately the amount of time it takes to get to the airport and shows me a relevant ad about timing and convenience. Uber knows I’m a long-time, cost-conscious customer, so it outbids the other two, shows me a relevant message about being the lowest-cost option, and remains the only ad I see above the fold. 
  • Hertz shows me the minivan options at Orlando International Airport. AAA offers me the same rental cars, at a lower price, with messaging about peace of mind that my paid membership will keep me and my family safe in an emergency. I book the same car through AAA. 
  • My five-night hotel stay was chosen for convenience and cost; I have no hotel loyalty. However, the hotel continued to stay relevant before my stay by offering me discounts to Universal and Disney and providing recommendations for local beach attractions while promoting their kid’s program in both its paid advertising and organic content. I ultimately sign up and become a lifetime loyalty member. 

Away luggage wins on rush shipping alone, I make two Uber XL pre-bookings, AAA gets ~10% of my car booking in affiliate revenue, and the hotel chain now has lifetime value out of my family of four. Where all else is equal, context consistently wins out in all of these scenarios. 

Search engines can now personalize advertising based on the user’s search history and other factors. Google is set to own the race for contextual relevance based on its enormous 1B+ user device graph that it leverages to deliver a seamless consumer experience across channels and formats. That free rush shipping I got from Away? Google may have interpreted that my flights (which I booked through Google Flights) were less than a week away because I was logged in across both properties. 

Context will be king in the future of marketing.

Marketers can now use natural language processing to understand the meaning of the content users are searching for, opening the door for any brand to appear in the right place at the right time. Teams that have historically interpreted the context of their audience’s needs and wants using data have a leg up (for now).

Those leveraging AI to understand and interpret the context of their audience’s needs will be the next market leaders. 

Google concept abstract

Feed the machine: Google’s ever-consolidating ecosystem

Context is only as useful as the data that feeds the tools we use to reach our consumers.

As such, platform and campaign consolidation is an important part of Google’s strategy to drive effective, scalable AI across traditional channels and emerging formats. By bringing together cross-channel buying and measurement capabilities into AI-driven campaign types like PMAX, Google aims to automate the targeting and delivery of campaign creative to reach users at the right moment in time. 

Video View Campaigns are a prime example of this consolidation, combining various tactics within video and Discovery for upper funnel targeting. These campaigns use a variety of formats, including skippable in-stream ads, in-feed ads, YouTube Shorts, among others, to reach a wider audience and drive more views. According to Google, advertisers using Video View campaigns have seen an average of 40% more views than those who use traditional cost-per-view campaigns.

Why? Because advertisers are using a more intelligent, automated approach to ad delivery across screens and formats. 

Similarly, new Demand Generations Campaigns affect channel segmentation in the Google ecosystem by combining YouTube shorts, in-stream, feeds, Discover, and Gmail targeting. Like Video View Campaigns, Demand Gen campaigns use AI to help advertisers increase reach, improve targeting, and boost conversions. 

Recent trends paved the way for this consolidation, including the Smart Shopping  upgrade into Performance Max in April 2022 and Google’s announcement that Dynamic Search Ads (DSA) will fold into Performance Max over the next 12 months. Another core match type for search, Phrase Match, is likely to be retired in the next 12 to 18 months.

This shift to cross-channel, multi-touch automation has been in the making for years. By introducing data-driven attribution (DDA) as the default attribution model in 2021, Google signaled its intent to move toward a world where brands and advertisers can better understand the impact of various marketing channels throughout the customer journey. Instead of assigning credit to last touch, marketers can and should optimize for impact and experience across the full journey by establishing historical credit to each touchpoint. 

Still, organizations continue to ask the question: How do our strategists, planners, and buyers leverage cross-channel insights provided by DDA to ensure they deliver a relevant user experience in real time?

It feels like an endless task to track, measure, and deliver an experience to the millions of possible variations of a customer journey. Google’s consolidated campaign type announcements simply suggest we should let the computers do it. As a result, brands will have fewer optimization levers to pull. Instead, AI-based optimization will require teams to put greater emphasis on data and measurement, audience management, and implementation to effectively “feed the machines.”

We have remaining questions about how these new consolidated campaigns types will work alongside/not bid against products with access to the same inventory within the Google ecosystem. But it’s clear that an emphasis on continued consolidation, and a foundation built on first party data, will allow Google to improve effectiveness and give its advertisers a competitive advantage.

Shifting mindsets: marketing with Google is a Profit Center, not a cost

Google’s consolidation announcements shift focus away from its search product and toward a more full-funnel marketing ecosystem. They are also indicative of a larger scale desire to shift brand and advertiser mindsets about the role marketing plays. To prove the effectiveness of the new cross-channel tools, Google is introducing ways to measure product profitability and help brands understand how their advertising is driving sales and profits.

Google wants to flip the script: Marketing with Google isn’t a cost, it’s a profit center, and knowing a product’s profitability can enhance AI-driven marketing strategies. 

One of the newly introduced features, Product Performance Insights, provides retailers with insights into how individually advertised products are performing across different channels, including search, display, and YouTube. Retailers can use this information to optimize campaigns and make sure they are reaching the right customers with the right messages.

Another new feature is called Margin Reporting, which helps retailers to make better product pricing decisions and improve media effectiveness by identifying and eliminating products that are not profitable from marketing efforts. Finally, the Product Attribution Model allows retailers to attribute sales to different marketing channels, including advertising, social media, and organic search. This helps retailers to understand which marketing channels are driving the most sales and to allocate their marketing budget accordingly.

We’ve spoken at length about cross-channel automation and the value AI can bring to marketing effectiveness. Powered by additional data like product margins, marketers can make more informed decisions about what tactics drive true incrementality, eliminate media waste, and identify diminishing returns.

ROI-focused business data informing trillions of bids every day? Yes please. 

Leveling the playing field

Out of all the updates announced at GML, we couldn’t help but opine that the biggest winners are small-to-medium-sized companies looking to scale. 

Between campaign creation using conversational AI, dynamically created images in Product Studio, and generative search experiences, gone are the days of needing hordes of specialties to manage the many aspects of a nascent marketing program: strategy, media buying, creative, and measurement, to name a few. Google has made a statement of intent with their recent announcements.

No longer is it enterprise vs. enterprise (Nike v. Adidas, Ford v. Chevy, McDonalds v. Burger King); now any brand has an opportunity to offer a user experience like the world’s biggest advertisers. In a world that is already increasingly competitive, brands are already competing for the same attention from their audiences.

Using Google’s tools with “Your AI” allows you to supersede your category by enhancing targeting, creative, and bidding to meet the right person, with the right message, at the right time.  

While still critical for larger brands with more advanced programs, Google continues to demolish barriers to entry for SMBs. 

small to medium business concept

“Your AI”

“You’re not competing with AI, you’re competing with other marketers using AI.”

This was a common narrative as Google continues to emphasize that there’s no one-size-fits-all AI solution. Google underpinned the need for marketers to have a foundation of first-party data to power the consolidated, AI-driven ecosystem that it’s building (especially when privacy regulations, such as GDPR and CCPA,  make it more difficult for advertisers to collect and use third-party data). 

Our big bet — reinforced by Google — is that combining first-party data with Google’s contextual data and AI tools will drive increased accuracy, relevance, and trust through positive, privacy-safe user experiences. A smaller business that uses AI with a solid first-party data basis will be able to compete with any existing market share leader with a weaker foundation. 

search marketing AI

AI-generated content on the SERP

AI-generated content is now widely available on Bard, which is being rolled out to Google users through its Labs waitlist. Bard’s utility within search is impressive, with capabilities that include: 

  • Generating different creative formats of text content, like poems, code, scripts, musical pieces, emails, letters, etc. 
  • Translating languages, which can be helpful if you are trying to find information in a language you do not speak. For example, you can ask Bard to translate a Wikipedia article from French to English.
  • Providing visual responses to your prompts. For example, if you ask Bard, “What are some must-see sights in New Orleans?” you might also get a link to a Google Maps image of the French Quarter.

Google’s inclusion of AI-driven content into the SERP strikes the right balance. The decision to place Bard-generated text at the top for non-commercial searches is wise, as AI will benefit these searches more. AI-generated content may even help progress users toward transaction-based searches. 

Understandably, paid ads will still take precedence for commercial searches. Google’s algorithms can already ingest thousands of signals to determine the intent of each search query. Overall, this approach is a reasonable and effective way of enhancing search results. 

Search marketers will have to monitor these developments closely. The inclusion of AI-driven content fundamentally alters the SERP. However, Google’s incentives are aligned with advertisers. Paid advertising comprised 80% of Google’s revenue in their most recent shareholder statement ($72 billion). Therefore, Google wants to lead AI development while disrupting its most significant business unit. 

These recent AI announcements, mixed with privacy legislation, will aid in the ongoing evolution of paid search. Machine learning and automation have become a cornerstone of SEM strategy. Targeting channels within paid search will see continued consolidation. 

The digital marketing industry is moving away from precision targeting and toward predictive targeting. Precision targeting is becoming increasingly difficult to achieve as users become more privacy-conscious. Instead, advertisers must deliver relevant ads to the right people, even if they don’t have perfect information about those people. AI and machine learning will continue to predict which people are most likely to purchase.

Key tactics and takeaways for 2023

Over the next 12 to 18 months, marketers can expect to grapple with a lot of change. By implementing the following tactics, organizations can stay ahead in Google’s evolving AI environment. 

  • Build a foundation of 1st party data that can be used as signals across all campaign types.
  • Use data-driven attribution to measure the effectiveness of each campaign to drive efficiency and results.
  • Use automation to streamline your workflow so you can focus on more strategic tasks for your clients/brand. 
  • Build reliable measurement frameworks that address signal loss in the face of privacy legislation.
  • Implement broad match with automated strategic bidding for the best results. While there may be some initial friction with Performance Max, advertisers embracing this campaign type will be better positioned to succeed in the evolving digital marketing landscape.

Why Google is more important than ever

AI integration in the marketing and advertising industry has revolutionized the way businesses optimize their campaigns. With Google’s unrivaled dominance in the search engine market, it comes as no surprise that it’s leading the charge in AI-powered marketing solutions

By investing in cutting-edge measurement frameworks and streamlining channels, Google has established itself as an all-in-one destination for advertisers seeking to enhance audience targeting and minimize data loss. 

As we will continue to emphasize: more data = more effective artificial intelligence. As AI technology continues to advance, we can confidently expect Google to remain at the forefront of innovation in the marketing and advertising industry.


SVP, Search & Growth Labs

Joseph Kerschbaum