How conversational AI is changing the economics of paid search

Microsoft Copilot is transforming search advertising by turning everyday conversations into intent-rich signals advertisers can act on. 

ROAS increases 13-fold when users engage with Copilot before performing a search, according to Microsoft.

Drawing from billions of first-party audience insights across Microsoft’s consumer ecosystem – including Bing, Edge, Xbox, LinkedIn, and Activision – Copilot identifies high-value audiences using deterministic data built from search intent, web activity, and profile information. 

This allows advertisers to reduce wasted impressions and stretch budgets further.

The mechanics of intent-rich search 

The core proposition of conversational search is that users provide significantly more context to a chatbot than to a traditional search bar. 

Instead of a fragmented keyword, users are increasingly asking detailed questions.

When a user submits a complex query – such as asking for specific product comparisons or local recommendations – the AI triggers multiple backend searches across reviews, specifications, and availability to construct an answer. 

For the advertising industry, this behavior change offers a potential goldmine of data. 

By interpreting these longer queries, platforms can identify “high-intent” buyers more accurately, turning a single conversation into multiple, precise ad opportunities.

Applying conversational intent to a real-world campaign

To understand how these metrics translate into strategy, consider a recent test I conducted for a well-known California-based university tasked with recruiting high school seniors for their hands-on engineering and architecture STEM programs.

The challenge

The university historically relied on broad keywords like “best engineering schools.”

This resulted in high competition and wasted spend on students looking for art programs or out-of-state options they couldn’t afford.

The conversational approach

Using Copilot’s intent signals, the campaign shifts.

A prospective student might ask Copilot:

• “Find me a university with a strong robotics program, under $30,000 tuition, located on the West Coast.”

The results

Applying Microsoft’s reported benchmarks to this scenario reveals significant efficiency gains:

• Slashed waste: The university realizes a 32% reduction in wasted impressions because ads are not shown to students whose conversational context indicates irrelevant intent.
• Budget efficiency: By targeting intent rather than broad volume, the campaign drives a 48% decrease in cost per acquisition (CPA) compared to search alone.
• Higher engagement: Because the ad appears as a helpful solution to a specific question, engagement lifts by 153%.

Action plan: Transitioning to intent-based advertising 

For advertisers seeking to replicate these results, the shift necessitates more than simply enabling a new setting. 

It requires a strategic overhaul of how campaigns are structured to capture “conversational” demand.

Phase 1: Foundation and data (The signal layer)

Audit service offerings and solution data

Ensure your site’s structured data is rich with details on specific methodologies and industry specializations. 

AI assistants rely on this semantic depth to answer prospective queries about “competency, case studies, and communication options.”

Prioritize first-party data

Integrate customer data to train the model. 

Microsoft’s ecosystem leverages data points from LinkedIn to Xbox to refine targeting.

Advertisers must supply their own truth data to match this precision.

Phase 2: Campaign structure (The capture layer)

Embrace long-tail queries

Move away from strict exact-match keywords.

The UI overhaul of Copilot encourages users to ask “longer, more detailed questions,” meaning broad match modifiers are necessary to capture these natural language phrases.

Optimize for answers, not just clicks

Structure landing page content to answer specific questions. 

Since Copilot acts as a “companion” guiding users through tasks, your ad content must align with helping them make a decision, not just selling a product.

Phase 3: Cross-channel integration (The scale layer)

Implement cross-device strategy

With 90% of Gen Z adults in the U.S. using the web while watching TV, campaigns must run across multiple platforms, including mobile, PC, and console, to capture their split focus.

Bridge the authenticity gap

For younger demographics, leverage integrations like Snapchat’s My AI. 

This places ads within “conversational flows” rather than interrupting them, a key factor in engaging Gen Z.

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The Gen Z challenge: Authenticity vs. algorithms 

Bridging the gap with Gen Z remains a hurdle for most ad platforms, which often struggle with perceptions of inauthenticity. 

To address this, the industry is seeing a trend toward utilizing behavioral data from unlikely sources. 

By layering in data from gaming ecosystems like Activision, advertisers can target based on real behaviors – from play styles to in-game purchases – ensuring campaigns feel relevant rather than generic.

To legitimize whether Copilot is effectively targeting Gen Z – or just efficiently automating ad delivery – we must look beyond corporate claims. 

Microsoft’s strategy relies on a “closed loop” of gaming data, social integration, and conversational signals.

Does this actually work for a generation that is famously resistant to traditional advertising? 

The answer lies in the tension between utility and authenticity.

The behavioral match

Microsoft’s claim that Copilot “cracks the code” is mechanically sound because it aligns with how Gen Z actually searches.

The shift from keywords to conversation

Data shows that Gen Z users write the longest search queries (avg. 5.83 words) and are the most likely to use complete sentences.

They treat search engines like companions, asking “What is the best…” rather than typing “best shoes NYC.”

Legitimacy verdict: High. Copilot isn’t trying to force a behavior change. It is capitalizing on one that already exists.

By decoding these long, conversational queries, Microsoft captures intent often missed by a keyword approach alone.

‘Gaming data’ targeting 

Using Activision data to target users based on “play styles” is a strong differentiator for Microsoft.

The reality: 90% of Gen Z second-screens (uses a phone while watching/playing on another screen). Traditional demographics (e.g., “Male, 18-24”) are failing because they are too broad.

The legitimacy test: Targeting a user because they play Overwatch (identifying them as team-oriented and strategic) vs. Call of Duty (identifying them as reactive and fast-paced) allows for psychographic targeting that feels “relevant” rather than “intrusive.”

The risk is that there is a fine line between “relevant” and “stalker-ish.” 

While Microsoft’s targeting is effective, 76% of Gen Z actively avoid ads, and privacy concerns are their top barrier to trusting AI platforms. 

That said, the success of this strategy hinges on the ads feeling native to the experience, not like data extraction.

The authenticity paradox

This is the weak point in the strategy. Microsoft claims Copilot helps bridge the “authenticity gap,” but Gen Z is inherently skeptical of AI-generated content.

The conflict: Studies show that Gen Z can easily identify AI-generated ads and often labels them as “annoying” or “boring” compared to human-created content.

The Snap integration: Embedding Copilot ads into Snapchat’s “My AI” is a double-edged sword. While it places ads in a trusted social space, it risks polluting a private sanctuary. 

If “My AI” starts feeling like a corporate shill, users may abandon the feature entirely.

Legitimacy is mixed. The placement is correct (Snapchat, Games), but the content is at risk. 

If advertisers use Copilot to auto-generate generic ad copy, it will fail. Success requires using the AI for targeting but keeping the creative 100% human.

The verdict: Is Microsoft effectively targeting Gen Z?

• Technically: Yes, they have successfully built a mousetrap that catches Gen Z where they live (gaming, social, conversational search).
• Culturally: To be determined. The efficiency is there (lower CPA, higher ROAS), but “legitimizing” the strategy long-term requires overcoming the “uncanny valley” of AI trust.

Dig deeper: How Gen Z is redefining discovery on TikTok, Pinterest, and beyond

A new economic reality 

The narrative from platforms like Microsoft Copilot is that AI-driven targeting creates a “closed loop” where better engagement drives cost savings. 

As conversational AI reshapes how consumers interact with the web, advertising platforms are racing to translate natural language questions into actionable intent. 

Microsoft’s Copilot serves as a prime case study of this shift, demonstrating how emerging assistants generate richer, multi-step queries that potentially reshape search economics from a volume game to one of precision.

For advertisers, this signals a fundamental transition: moving away from the broad “spray and pray” tactics of keyword volume toward a model where conversational signals drive ROAS.

Dig deeper: The future of remarketing? Microsoft bets on impressions, not clicks