How conversational AI is changing the economics of paid search

Bids and reach are gradually losing their effectiveness. Growing competition, auction automation, and fragmented user intent are leading to situations where budgets are spent, but incremental results remain minimal. Against this backdrop, Microsoft Copilot demonstrates a fundamentally different approach. Conversational search allows advertisers to move from reacting to individual queries to working with the full context of user needs. Instead of short, abstract keywords, the platform analyzes dialogues, clarifications, constraints, and priorities, turning them into high-quality intent signals. As a result, advertising campaigns gain not just a larger volume of data, but a qualitatively different level of targeting. Ads are shown not to “everyone who searches” but to those who are already at the decision-making stage. This significantly reduces wasted impressions, increases message relevance, and directly impacts key business metrics—from CPA to ROAS. Thus, conversational AI ceases to be an experimental channel and becomes a strategic tool for optimizing paid search, changing the very logic of interaction between the brand, the user, and the advertising platform. Microsoft Copilot: Transforming Search Advertising Through Conversational AI Microsoft Copilot transforms search advertising by turning everyday user conversations into intent signals that advertisers can respond to in real time. According to Microsoft, the return on ad spend (ROAS) increases 13-fold when users engage with Copilot before performing a search. Copilot leverages billions of first-party audience data points across Microsoft’s ecosystem—including Bing, Edge, Xbox, LinkedIn, and Activision—to identify high-value audiences based on deterministic data: search intent, online activity, and profile information. For advertisers, this means significantly reduced spending on irrelevant impressions and a greater ability to use advertising budgets more efficiently. The Mechanics of User Intent in Conversational Search The primary advantage of conversational search is that users provide the chatbot with far more context than they would a traditional search bar. Instead of a short keyword, users increasingly formulate detailed questions. When a user submits a complex query—such as comparing specific products or seeking recommendations for local services—the AI performs multiple backend searches—across reviews, specifications, and availability—to construct a comprehensive answer. For the advertising industry, this shift in user behavior opens a genuine “goldmine” of data. By interpreting longer queries, platforms can more accurately identify “high-intent” buyers, turning a single conversation into multiple precise advertising opportunities. Example of Applying Conversational Intent in a Real Campaign To understand how these metrics translate into a real strategy, consider a test campaign for a well-known California university that recruits high school seniors for STEM programs in engineering and architecture. Despite being a different market, the logic of this case directly resonates with the challenges faced by Ukrainian educational, technology, and service brands. The Challenge Previously, the university relied on broad keywords such as “best engineering universities.” This approach created several issues: High competition in the auction; A significant share of irrelevant traffic; Spending on users seeking arts programs or out-of-state options that did not match their financial capabilities. A similar situation is often observed in Ukraine when businesses invest budgets in general queries such as “programming courses,” “marketing agency,” or “best CRM” without having clear control over the actual intent of the audience. The Conversational Approach Using Copilot’s intent signals, the campaign shifts from mass coverage to personalized interaction. Instead of abstract keywords, the platform analyzes users’ full queries. For example, a prospective student might ask Copilot: “Find a university with a strong robotics program, tuition under $30,000, located on the West Coast.” For the Ukrainian market, this is comparable to queries such as: “Online data analytics courses for beginners with certification”; “CRM for small business with integration with Ukrainian banks”; “Master’s program in IT abroad with scholarships.” These queries already contain clear constraints, criteria, and readiness to make a choice. The Results Applying Microsoft’s benchmarks in this scenario demonstrates significant performance improvements: Reduced spending: The university achieves a 32% reduction in irrelevant impressions due to more accurate reading of user intent. Budget efficiency: Focusing on intent rather than volume allows CPA to decrease by 48% compared to traditional search. Higher engagement: When ads appear as a logical response to a specific query, engagement rises by 153%. For Ukrainian marketers, this case is instructive: in conditions of limited budgets and high competition, those who succeed are not the ones who buy more keywords, but those who better work with context, intent, and the moment of decision-making. Action Plan: Transitioning to Intent-Based Advertising To achieve such results, it is not enough to simply enable a new option—strategic restructuring of campaigns is required to capture “conversational” demand. Phase 1: Foundation and Data (Signal Layer) Audit of offerings and solution data: Ensure that your site’s structured data includes details about methodologies and specializations. AI assistants rely on this semantic depth to respond to queries about competencies, case studies, and communication options. Prioritize first-party data: Integrate customer data to train the model. Microsoft’s ecosystem uses data from LinkedIn to Xbox for precise targeting. Advertisers must provide “truthful” data for accuracy. Phase 2: Campaign Structure (Capture Layer) Focus on long-tail queries: Move away from strict exact-match keywords. Copilot encourages users to ask long, detailed queries, so broad match modifiers are essential. Optimize for answers, not just clicks: Create landing page content that answers specific questions. Since Copilot acts as a “companion” in the search, ads must help users make decisions rather than just sell a product. Phase 3: Cross-Channel Integration (Scale Layer) Multi-device strategy: With 90% of Gen Z simultaneously using the web while watching TV, campaigns must cover mobile, PC, and console. Bridging the authenticity gap: For younger audiences, use integrations like Snapchat My AI to place ads within “conversational flows” rather than interrupting them. The Gen Z Challenge: Authenticity vs. Algorithms For Gen Z, the main issue is the perception of “insincerity” in advertising. The industry responds by leveraging behavioral data from unexpected sources, such as gaming platforms like Activision. This allows targeting users based on actual behavior—from play style to in-game purchases—making campaigns feel more relevant. To assess the effectiveness of Copilot in targeting Gen Z, it is necessary to look beyond corporate statements. Microsoft uses a “closed loop” of gaming data, social integration, and conversational signals. Behavioral Alignment: Gen Z writes the longest queries (avg. 5.83 words) and uses full sentences. They treat search as a “helper,” formulating queries like “What is the best…” rather than “best sneakers NYC.” Gaming Data Targeting: Using Activision data for psychographic targeting creates relevance without being intrusive. The Authenticity Paradox The weak point in Microsoft’s strategy is trust in AI. Gen Z easily identifies AI-generated ads and often finds them “boring” or “annoying.” Integration in Snapchat My AI is a double-edged sword: ads appear in a trusted environment but risk violating private space. Conclusion: Technically, Microsoft effectively reaches Gen Z where they live (games, social media, conversational search), but culturally, trust requires overcoming the AI “uncanny valley.” A New Economic Reality Conversational AI creates a “closed loop” where better engagement leads to cost savings. Copilot demonstrates how assistants generate multi-step queries, shifting search economics from a volume game to a precision game. For advertisers, this signals a fundamental transition: from broad “spray-and-pray” strategies to a model where conversational signals drive ROAS. Microsoft Copilot is a vivid case of how conversational AI can increase paid search efficiency, reduce costs, and more accurately track user intent, while opening new avenues for strategic targeting in the marketing landscape of medium and large businesses. Read this article in Ukrainian.