6 minNovember 14, 2026
Our tools monitor millions of native, push, pop, and TikTok advertising campaigns.
Get StartedA company can have strong brand recognition, efficient campaigns, and impressive conversion reports—and still misunderstand the market it is competing in.
That happens when the business defines its market through the limited signals its reporting systems can measure. Website traffic, last-click conversions, search demand, and channel-level return on ad spend are valuable, but they do not represent every interaction influencing a buyer's decision.
Discovery now takes place across search engines, AI assistants, social platforms, online communities, recommendations, and advertising. People may encounter a brand several times before visiting its website—or may form an opinion without clicking an ad at all.
For marketers, this creates a strategic challenge. The goal is no longer simply to capture the demand that appears in existing dashboards. It is to understand how customer needs, competitive alternatives, and category expectations are changing.
Competitive ad intelligence can help reveal part of that wider market. By examining visible advertising, messaging, creative formats, offers, and landing pages, teams can identify the problems competitors are addressing and the positioning they are using to attract attention.
The opportunity is to connect those observations to brand strategy, performance marketing, and product decisions—rather than treating ad spy tools as a source of creative ideas alone.
Albertsons is an established grocery business, but its name can serve as a useful starting point for a broader strategic question: what happens when a company defines its market too narrowly while customer expectations and competitive alternatives evolve?
A grocery retailer might traditionally define its market through store locations, grocery categories, market share, and weekly shopping behavior. But customers can also solve the same household needs through delivery services, curbside pickup, online marketplaces, and other convenient purchasing options.
The relevant market is therefore not always limited to businesses that look like the company on an organizational chart.
This is the central problem with a market definition built entirely inside a presentation deck. The business may be measuring its familiar competitors while overlooking alternative ways customers solve the same problem.
Many marketing reporting systems focus on what happens on owned properties:
These metrics are useful for evaluating measurable campaign outcomes. But they do not capture every influence that shapes discovery and consideration.
As MarTech explains in its discussion of measuring marketing when AI owns discovery, customers may encounter brand information through AI assistants and other discovery experiences before they visit a company's website.
A prospect might read a discussion on Reddit, watch a YouTube review, see a brand mentioned in an AI-generated answer, and only later search for that company by name.
Depending on the available measurement systems, some of those earlier interactions may not be directly connected to the eventual conversion.
That creates a measurement gap. It does not mean every untracked interaction caused a sale, but it does mean a dashboard should not be mistaken for a complete map of the customer journey.
Performance marketing has made it possible to measure and optimize many parts of the conversion process. That discipline has helped teams improve campaign efficiency and demonstrate commercial results.
However, an overreliance on measurable lower-funnel activity can create a strategic blind spot.
As AdExchanger argues in its analysis of performance marketing's biggest blind spot, marketers can become exceptionally good at capturing existing demand while paying less attention to how future demand is created.
A business may continue improving bids, targeting, and conversion rates while competing for a limited pool of people who are already ready to buy.
When that audience becomes saturated, acquisition costs may rise and growth may slow. That does not automatically mean performance marketing has failed. It may mean the business needs to expand its understanding of the market and investigate how new demand is developing.
Customer discovery can involve many touchpoints that traditional website analytics do not fully capture.
People may:
MarTech's analysis of AI-driven discovery highlights the growing importance of information sources beyond a brand's own website.
This makes it important to distinguish between what a reporting system records and what may be influencing the broader market.
When growth slows, companies sometimes respond by adding more vendors, dashboards, and optimization platforms.
But more software does not necessarily solve a fragmented data problem.
As MarTech argues in its analysis of the future of performance marketing, the challenge is often how effectively a business uses the data and systems it already has.
A team can have detailed reporting on a narrow set of customer actions and still lack a useful view of changing category demand, emerging competitors, and evolving customer expectations.
Even paid search provides only one part of the picture. Tools such as Semrush's PPC strategy and Advertising Research resources can help marketers examine competitors' search advertising, keywords, and messaging. That information is valuable, but it primarily shows visible demand-capture activity.
It does not independently reveal every problem customers are beginning to discuss or every alternative they are considering.
The lesson is straightforward: define the market through customer problems and available alternatives, not just the competitors and behaviors already represented in your reporting tools.
"Brand versus performance" is a convenient framework because it divides marketing into two recognizable functions.
Brand marketing is often associated with awareness, reach, recall, and long-term perception. Performance marketing is associated with clicks, leads, conversions, customer acquisition cost, and return on ad spend.
The problem is not that these functions have different objectives. The problem is treating them as separate strategies that can be optimized independently without considering how they influence the same customer and market.
A buyer does not necessarily experience a brand through separate budget categories.
Someone might first see a short-form video, later encounter a search ad, read a comparison article, and eventually visit a product page.
Each interaction can contribute to how that person understands the business.
AI-assisted discovery adds another layer. A brand's visibility may depend on how clearly its products, positioning, and supporting information are represented across sources that answer engines can access.
Neil Patel's recap of Google I/O and Marketing Live discusses changes in Google's AI-assisted discovery and advertising environment. These developments make it increasingly important for marketers to consider how their brand is described and surfaced across different discovery experiences.
This does not mean that advertising impressions directly train every AI system or determine its recommendations. It means brand information, content quality, and the wider information environment deserve attention alongside conventional campaign metrics.
Paid advertising is often evaluated through immediate response: clicks, leads, purchases, and cost efficiency.
But every ad also communicates something about the business.
Its promise, language, visual style, and landing-page experience help establish what the company offers and who it is intended to serve.
For example, a company that repeatedly promotes low prices may become associated with affordability. Another that consistently emphasizes reliability and specialist support may communicate a different value proposition.
The consistency between ad copy and landing-page content matters. Semrush's PPC strategy guidance discusses the importance of aligning ads, keywords, and destination pages.
That alignment can improve the user experience and make the offer easier to understand. It should not be overstated as proof that a particular message automatically determines how an AI model categorizes a brand.
Brand marketing is not limited to quarterly awareness studies.
Digital campaigns can produce observable signals such as:
MarTech's analysis of brand measurement and the need for a new source of truth explores the need to combine different signals when evaluating brand activity.
These metrics do not all have the same meaning. A video view is not a purchase, and social engagement is not proof of revenue. But together with customer research and commercial results, they can provide a broader picture than a single last-click report.
Short-form video illustrates how brand and performance objectives can overlap.
On TikTok, an ad competes for attention alongside creators, entertainment, community content, and other brands. Its opening seconds need to earn attention while making the subject and value of the message clear.
Social Media Examiner's discussion of TikTok advertising tools and AI provides context on the changing creative environment.
A strong creative concept may help establish brand recognition while also encouraging a click or another action. The platform's delivery system does not divide the user's experience according to the advertiser's internal budget categories.
When teams over-rotate toward performance, they may keep adding optimization tools without revisiting the bigger question: does the business understand the category in which customers actually place it?
As MarTech's performance marketing analysis emphasizes, operationalizing existing data can matter more than continually expanding the technology stack.
A business can report strong brand metrics and efficient acquisition costs while still missing a change in customer expectations or competitive alternatives.
The more useful approach is to connect brand and performance around a shared market understanding:
That is how teams avoid becoming highly efficient at competing in a market that customers no longer define in the same way.
Many marketers use ad spy tools as a creative inspiration library.
They search for competitors, save interesting headlines, inspect thumbnails, and look for formats they can adapt. This can be useful, but it represents only one part of competitive ad intelligence.
Used systematically, visible advertising can help teams investigate how a category is being positioned, which customer problems advertisers emphasize, and what types of offers are appearing across different channels.
The objective is not simply to find an ad that looks successful. It is to build a more complete picture of the competitive environment.
A single advertisement is only one observation.
A larger collection of ads can reveal recurring themes:
These patterns can help marketers identify crowded positioning and potential areas for differentiation.
However, visible advertising is not a complete record of market behavior. An ad's presence does not prove its budget, conversion rate, profitability, or contribution to brand growth. It is evidence of observable advertising activity that can guide further investigation.
Google and other platforms are developing AI-assisted ways to organize information, answer questions, and support product discovery.
Neil Patel's discussion of Google I/O and Marketing Live provides context for these developments.
For marketers, the practical implication is that clear positioning and useful information are important across the places where customers research solutions.
That does not make ad libraries a direct view into recommendation algorithms. Instead, ad intelligence helps teams examine how businesses communicate their value in public-facing advertising.
TikTok's advertising research resources can help marketers explore ads across available categories and markets.
Social Media Examiner's TikTok advertising analysis discusses tools and approaches for researching advertising activity.
Depending on the platform and available filters, marketers may be able to examine creative examples by advertiser, industry, location, objective, or time period.
This can help answer questions such as:
The same research principle applies to search and display advertising. Semrush's PPC strategy resources explain how keyword, ad-copy, and landing-page research can help marketers understand the relationship between demand and advertising messages.
When these observations are gathered across multiple competitors, they can provide a useful picture of how the category is being marketed.
The larger opportunity is to make competitive research a recurring process rather than a one-time exercise.
AdExchanger's analysis of performance marketing's blind spot highlights the importance of understanding changes in attitudes and demand, not only the conversions already visible in reporting systems.
Ad intelligence can contribute to that broader view by showing which messages and offers are appearing in the market.
Instead of asking only, "Which ads should we copy?" ask:
The answers can help teams identify possible shifts in category positioning.
The goal is not to assume that every new creative represents a successful market trend. It is to use observable activity to generate questions, then validate those questions through customer research, first-party data, and controlled testing.
Many businesses define their market through traditional planning exercises: a total addressable market slide, a list of familiar competitors, and a collection of customer segments.
Those frameworks can be useful starting points. But they need to be tested against observable market activity and actual customer behavior.
Ad intelligence offers a practical way to investigate how competitors communicate with customers and which problems they appear to address.
The process is not about treating every visible ad as a proven commercial success. It is about building a structured view of the messages, offers, and market positions that are present.
Begin with your own advertising and brand name.
Search available ad libraries and competitive research tools for your brand, then expand the research to:
This helps prevent the research from being limited to the competitors already listed in an internal presentation.
The source draft references TikTok's Content Suite and the Google Ads Transparency Center as examples of public-facing advertising research resources. Availability, search options, and coverage differ by platform, so verify the current capabilities before turning this into a step-by-step tool tutorial.
The important principle is to start with your own visible market presence and then expand outward.
Do not stop at a list of competitor names.
Organize the research around the promises and customer problems each advertiser addresses.
For every relevant ad, record four elements:
This structure follows the broader research logic in Semrush's PPC strategy guidance, which connects search advertising, messaging, and landing-page experience.
As the collection grows, group ads into themes such as:
These groups represent visible positioning themes. They are not proof that customers universally value those attributes or that the advertisers have achieved strong performance.
Once the advertising examples are organized, AI tools can help categorize the material and identify patterns.
MarTech's playbook on AI-powered competitive intelligence discusses the value of using AI to interpret competitive information rather than relying only on retrospective reporting.
A structured analysis could look for:
For example, a collection of ads might show that several advertisers are increasingly emphasizing reliability rather than speed.
That could suggest a shift worth investigating. The next step is to check whether customer reviews, sales conversations, search behavior, or first-party results support the same interpretation.
AI can help summarize and classify the observations, but it should not turn an unverified pattern into a claim about competitor strategy or profitability.
AI-assisted discovery makes clear product information and consistent positioning increasingly important.
Neil Patel's Google I/O and Marketing Live recap discusses changes in the search and discovery environment.
For marketers, the practical task is to ensure that the company's own claims, product descriptions, and supporting evidence are clear wherever customers encounter them.
Ad intelligence can help identify how competitors communicate. It cannot reveal the private logic of search or recommendation systems, and it cannot prove that a particular advertising message will influence AI-generated recommendations.
Treat market observations as inputs to brand and content strategy—not as a shortcut to controlling platform algorithms.
After collecting and organizing the evidence, use it to build three views.
The narrative market
The set of problems, benefits, fears, and aspirations that repeatedly appear in category advertising and customer research.
The relevant competitive set
The businesses and alternative solutions that appear in the same customer decision contexts. This may include companies that do not look like traditional direct competitors.
The open space
The customer problems, offers, proof points, and positioning combinations that appear less represented in the research.
These boundaries are working hypotheses, not a final scientific measurement of the entire market. Validate them against sales data, customer interviews, search trends, product usage, and campaign results.
The result is a more useful market map: one that connects what advertisers communicate with the alternatives customers may consider and the problems the business can credibly solve.
Once the competitive landscape is clearer, the next step is not necessarily to outbid competitors or offer a bigger discount.
It is to identify a specific customer need that the business can serve distinctively.
A micro-position is a focused place in the market built around a particular audience, situation, problem, or outcome.
Ad intelligence can help identify where competitors concentrate their messaging. The opportunity is to use that information to develop differentiated offers, creative angles, and customer journeys.
Look across a category's visible advertising and you may find similar offers repeated:
These offers can be commercially useful, but when every competitor uses similar incentives, it becomes harder to distinguish one brand from another.
TikTok advertising research approaches discussed by Social Media Examiner can help marketers examine the creative and offer patterns present in their category.
The goal is not to copy the most common offer. It is to identify a credible way to communicate a different kind of value.
For example:
Any guarantee or risk-reversal offer must be commercially realistic, clearly defined, and supported by the business. Avoid promises that cannot be substantiated.
A distinctive offer gives customers a reason to evaluate the business on more than price alone.
Broad demographics rarely explain the full context behind a buying decision.
A more useful approach is to investigate the situation that causes someone to look for a solution.
Examples include:
Ad intelligence can reveal which emotional and practical angles competitors emphasize.
Semrush's PPC strategy guidance discusses the importance of aligning ad messages with the needs expressed in the search and destination experience.
When the research is expanded across a category, marketers may find that many brands focus on the same two or three broad promises.
That creates an opportunity to investigate less-served customer situations.
For example:
These are potential positioning directions. Customer interviews, product capabilities, and campaign testing should determine whether they are commercially meaningful.
MarTech's guide to AI-native advertising also provides context for the shift toward using more varied signals and creative inputs in automated advertising environments.
Specific creative can help communicate a distinct customer situation, but it does not guarantee that an advertising algorithm will identify or deliver the message to exactly the intended audience.
A differentiated offer and angle need a customer journey that supports them.
If every prospect is sent to a generic product page with a discount, the business may force people to compare options primarily on price.
Ad intelligence can help reveal where competitors send traffic and how their landing pages continue the advertising message.
Use those observations to explore alternative funnel structures.
Diagnostic first, product second
A calculator, audit, or questionnaire can help a prospect understand a problem before being presented with a product offer.
Examples might include:
Any tool that collects personal or business information should explain what is being collected and how it will be used.
Education that reframes the problem
A useful guide or comparison page can explain why a common approach may not address the customer's actual needs.
This connects with the issue raised in AdExchanger's analysis of performance marketing's blind spot: a business that focuses only on capturing existing demand may overlook opportunities to shape how customers understand a problem.
Educational content can help customers evaluate a different approach, provided the claims are accurate and the content genuinely helps them make a decision.
Segmentation by use case
Instead of presenting only generic pricing tiers, consider organizing the experience around distinct needs:
Each path can present relevant features, evidence, implementation details, and pricing information.
MarTech's discussion of AI-native advertising provides context on the growing role of automation in advertising.
Automation can help optimize delivery, but it does not replace the need for a well-designed offer, accurate product information, and a useful customer journey.
The purpose of a differentiated funnel is to make the value of the offer clear before the customer reduces the decision to a price comparison.
The value of competitive ad intelligence extends beyond deciding which headline to test next.
When teams consistently organize visible market observations, those findings can help inform product positioning, packaging, customer research, and longer-term brand strategy.
The goal is to turn competitive advertising research into a resource that leadership, product, and marketing teams can use.
Think of the output as three connected documents:
Public ad libraries and research tools such as Semrush's Advertising Research resources can help marketers investigate visible advertising messages and their relationship to search demand.
Organize the findings around:
Tagging ads by customer problem, audience, offer, and promise can help reveal the main positioning lanes in a category.
For example:
The next step is to bring those observations into a recurring market review.
Ask:
MarTech's AI-powered competitive intelligence playbook provides context for moving from retrospective competitor reports toward more actionable interpretation of market changes.
The output should be a shortlist of research findings and strategic questions—not another presentation that simply repeats the ad examples.
Once the team understands the visible positioning patterns, product leaders can investigate whether the market is revealing unmet needs.
For example, suppose several competitors emphasize all-in-one platforms, while customer feedback repeatedly raises concerns about complexity.
That combination may justify investigating a more focused product experience.
Or perhaps ads across the category increasingly emphasize a specific use case that the company previously considered secondary. That may be worth exploring through customer research and product usage data.
Competitive ad intelligence can help generate product questions around:
Features and bundles
If competitors repeatedly promote workflow automation, investigate whether customers need simpler implementation, better templates, or a more accessible feature bundle.
Pricing architecture
If advertisers consistently position products around different company sizes or industries, investigate whether one general pricing structure adequately reflects customer needs.
Roadmap prioritization
If a particular problem appears across several advertisers, examine whether it is supported by customer requests, product usage, and commercial opportunity before prioritizing development.
Ad activity can indicate what competitors are choosing to communicate. It cannot independently prove product demand or validate the commercial value of a new feature.
Use it as one research input alongside customer feedback, sales data, retention, and product analytics.
As AI tools make it easier to produce creative variations and marketing content, differentiation becomes increasingly important.
The answer is not simply to produce more ads. It is to establish a clear, credible reason for customers to choose the business.
Ad intelligence can help identify what competitors emphasize and where the category sounds repetitive.
For example:
Social Media Examiner's analysis of TikTok advertising offers context for the changing creative environment and the importance of developing relevant content.
Once the brand promise is defined, it should be reflected consistently across advertising, landing pages, product tours, and other customer-facing materials.
Semrush's PPC strategy guide highlights the importance of maintaining alignment between ad messaging and landing-page content.
That consistency helps customers understand what the business offers and what they can expect after clicking.
A useful competitive intelligence process should produce more than saved ads and screenshots.
It should help the business decide:
The spy screen is therefore not just a place to inspect competitors. It is one input into a broader market research process that connects advertising observations with customer evidence and business decisions.
A business can optimize its campaigns, improve conversion rates, and build detailed reporting systems while still missing important changes in the market.
That happens when the company defines competition too narrowly and treats the activity visible in its own dashboards as the complete picture.
Competitive ad intelligence offers another way to investigate the environment. By studying visible advertising, offers, creative formats, and landing-page experiences, marketers can identify recurring category messages, emerging positioning themes, and potential opportunities to differentiate.
But ad spy data is not a substitute for customer research or first-party performance measurement. It shows observable advertising activity, not private budgets, verified profitability, or the complete reasons behind a competitor's decisions.
The strongest approach connects several evidence sources:
When those inputs are reviewed together, brand and performance become parts of a shared market strategy rather than competing budget categories.
The goal is not to become more efficient at winning the market as it appeared in last quarter's presentation. It is to keep investigating how customers define their problems, which alternatives they consider, and where the business can deliver a distinctive solution.
That is how competitive intelligence becomes more than a creative research tool. It becomes an input into the way a company understands its market, develops its products, and builds a position customers have a reason to remember.
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