10 prompts for competitive analysis in marketing

Competitor analysis using AI works best when you do not treat it as one “magic” prompt, but as a structured process. It is not a trick. In practice, you first gather the source material, then run a series of prompts for specific areas, and finally turn the results into a list of actions. This model saves time because it organises the chaos of SEO, advertising, offers and UX data. The biggest mistake is asking AI to analyse competitors without specific URLs, screenshots, exports and a clear response format. The result is predictable: generalisations instead of material for decision-making. A well-built system of 10 prompts should lead to implementation, not just a description of the market.

What is a system of 10 prompts for competitor analysis in marketing

A system of 10 prompts is a sequence of instructions used as a workflow to compare competitors in marketing. The key point is that this is not about one elaborate prompt, but a series of steps, each taking responsibility for a different area: competitors, offer, SEO, content, ads, UX, trust and action priorities. This means AI does not mix topics together and it is easier to draw conclusions that can simply be implemented.

It all starts with defining who you are comparing and in what context. Without that, the analysis drifts. You will break down local services differently from an online store, and differently again for SaaS or a B2B offer. The scope needs to be defined before you start: country, language, offer type, audience segment, device and funnel stage. The question is: do you want to compare the “market”, or specific customer behaviours at a specific point in the funnel.

The input material for this kind of analysis can be mixed and sometimes even fragmented. It includes a list of domains, search result screenshots, landing page URLs, keyword exports, ad examples, offer sections, user reviews and your own data from GA4 and Search Console. The more specific sources you provide, the less room you leave for guessing and the more space you create for meaningful synthesis.

The output should not be a general “competitor description”, but a working package for further decisions. Most often this includes a competitor map, offer comparison, keyword gaps, messaging patterns, UX issues, assessment of trust elements and a list of tests and implementations. If the result does not end with a backlog of actions, the analysis is incomplete. And that is not a cliché.

This system is useful because it separates observations from interpretation. First AI should list the facts visible in the sources, then the patterns, and only at the end the recommendations. Not “opinion first, evidence later”, but the other way round. This structure reduces false conclusions and makes it easier to check whether a given claim really follows from the data, rather than from a smooth-sounding narrative.

It is also worth remembering that external tools show indicative data, not the full market picture. Estimates of traffic, visibility or ad budgets help set direction, but should not be treated as hard evidence. That is why a good system of 10 prompts always combines tool data with manual review of the SERP, ads and competitor sites. Instead of trust in estimates — verification on screen.

How it works: practical application of 10 prompts

Using 10 prompts in practice is straightforward. Each prompt tackles a different slice of the competition and returns the result in a fixed, comparable format. Instead of one sprawling description, you get a series of structured outputs that can be combined into an action plan without pain. And that makes a difference especially when you are comparing several domains and several channels in parallel.

It is best to run these prompts on a pre-prepared data set. A separate sheet for domains, a separate one for keywords, a separate one for ads and landing pages, and yet another one for UX observations really shortens the path from analysis to decision. What matters most is a repeatable input and a repeatable response. A fixed input format and a fixed response format are more important than the prompt’s “creativity”. The question is not “will it be nicely written”, but whether it can then be compared and implemented.

• Prompt 1: builds a map of competitors and search context. On input you provide a list of domains, products and phrases, and AI divides entities into direct competitors, indirect competitors and substitutes, then indicates at which stage of the funnel they appear.
• Prompt 2: compares the offer and audience segments. It reviews homepages, categories, pricing and benefits sections to show what problems companies solve, who they speak to and what they try to differentiate with.
• Prompt 3: identifies the keyword gap and gaps in SEO architecture. Based on keyword exports and ranking URLs, AI groups topics, intents and page types missing from your site.
• Prompt 4: analyses messaging in the SERP. It compares title tags, meta descriptions, H1s and rich results to identify promises, benefit-led language, trust elements and differences in CTA.
• Prompt 5: assesses content and topical coverage. It checks the blog, guides, FAQ and service pages to determine which topics support education, which support sales, and where the user does not get an answer before making a decision.
• Prompt 6: breaks down ads and landing pages into their component parts. AI analyses the promise, proof, offer, CTA and consistency between the ad creative and the landing page.
• Prompt 7: identifies UX issues and conversion barriers. Based on screenshots, forms and key views, it points out friction in navigation, information hierarchy, mobile UX and process length.
• Prompt 8: examines trust and proof of quality. It checks reviews, testimonials, case studies, policies, FAQs, certificates and contact details to assess how competitors reduce purchase risk.
• Prompt 9: analyses the funnel after the user lands on the site. It covers forms, lead magnets, newsletters, thank-you pages and visible automations, i.e. what happens after traffic has been acquired.
• Prompt 10: produces a strategic synthesis. It combines the results from the previous steps and arranges a backlog of actions according to business impact, ease of implementation, dependencies and required resources.

One thing is key: every prompt must have a clearly defined input, scope of comparison and expected output. If you ask for a table with source evidence one time and a loose description another, it then becomes hard to compare the answers and set priorities. In practice, it is best to keep to a “on rails” format: columns, source, confidence level and a separate section for hypotheses to verify.

Such a workflow simply works. It lets you scrutinise the same elements for each competitor using identical criteria, without improvising halfway through. And then you can see not only individual differences, but also patterns that like to come back like a chorus: the same promises in ads, similar UX gaps, repeated content gaps or shared trust-building elements. The question is, what will you do with it? Because it is precisely from such patterns that sensible marketing tests are born.

In the end, it is not about knowing “what the competition is doing”, but what it means for your brand. A good use of 10 prompts ends with a short list of decisions: what to improve quickly, what to test straight away, what requires a bigger project and which actions really have the biggest impact on results. Without prioritisation, even accurate analysis quickly turns into notes that lead nowhere.

Key elements of competitor analysis: SEO, offer, ads and UX

SEO, offer, ads and UX. Only together do they show who attracts attention, who persuades the click and who genuinely makes buying or getting in touch easier. If you look solely at visibility in Google, you are only looking at one slice of the picture. A competitor may have weaker SEO, but win with a better offer, a simpler form or a stronger ad message. The most useful analysis does not ask only “who is higher”, but “why does the user choose this company”.

In SEO, the number of keywords alone does not win. What matters is which search intents the competition covers and how it guides the user to the right page instead of spinning them in circles. Check whether competitors have separate pages for services, segments, problems and informational questions, or whether they cram everything onto one subpage. Such a diagnosis tells you directly where you have content architecture gaps, rather than just a “lower position” for a single keyword.

What also matters is the communication visible already in the SERP. SEO titles, meta descriptions, H1s and rich results reveal how companies promise results, how they reduce risk and what they encourage in the first contact. If a competitor wins the click, the reason often lies not in the position itself, but in a better-formulated promise and a clearer CTA. And that is not a cliché, but everyday practice.

The offer answers a simple question: what exactly is the company selling, to whom and in what package. In a comparison, detail matters, not generalities, so set out the range of services or products, pricing variants, declared differentiators, the way customers are segmented and whether the message is built around the recipient’s problem or around the company itself. A good offer analysis does not stop at a table of features, but goes one step further and checks what proof of quality the competition adds to its promises (and what it deliberately does not show).

Ads state outright which messages companies are testing where they are actually paying for user attention. From ad copy, creative and the landing page, you can read which arguments are important enough commercially for the brand to back them with budget. But beware: external tools make it easy to draw overconfident conclusions, because traffic and spend estimates remain only approximate. The most reliable material for ad analysis is what is actually visible in the ad library, in the creative and on the landing page after the click.

UX turns interest into action. And there is no magic here, there is mechanics, which is why competitor analysis must also cover navigation, forms, CTA visibility, mobile and trust elements. Users do not judge a site in a vacuum as “nice” or “ugly”, but check whether they understand the offer, whether they know what to do next and whether unnecessary friction awaits them along the way. In practice, compare form length, the order of information, the visibility of price or the next step, page speed and how the site behaves on a phone. Because what is the point of the promise if the click gets bogged down in a detail.

The biggest value appears only when you bring these four areas together into one picture. SEO shows where traffic comes from, the offer answers whether there is a reason to come in, ads reveal the strongest messages, and UX decides whether the user takes the next step. If you analyse any of these elements in isolation from the rest, it is easy to draw a conclusion that is correct, but not very useful from a business perspective.

Strategic approach to comparison and implementation

Strategy starts with comparability. The point is that you assess all companies according to the same criteria, and the results are immediately arranged for decisions, tests and tasks. Without a fixed model, one brand will be judged through the lens of SEO, another through ads and a third through the look of the site, and the whole comparison becomes skewed. That is why before you start, define the scope: country, language, customer segment, device, funnel stage and the channels you take into account. Instead of a free-for-all — the same ruler for everyone.

It is also crucial to prepare the input data in an organised form. Separate sources for domains, URLs, keywords, ads, landing pages and UX observations make it easier for the model to work and limit mixing up threads. The more precise the input, the fewer generalisations, and the easier it is later to defend the conclusions to the team or client. It is the difference between “it seems” and “it follows from the material”.

A good process separates three layers: facts, interpretation and recommendations. First you gather what is actually visible in the sources, then you spot patterns, and only at the end do you translate that into decisions. This matters because many mistakes come from treating tool estimates or single observations as hard evidence. In every AI response it is worth requiring the source, the confidence level and a separate section for hypotheses to check. The question is: what do we know, and what do we only suspect.

Analysis alone does not deliver value if it does not end with prioritisation. The key is deciding which gaps really matter to the business, which can be closed quickly, and which require changes across several channels at once. In practice, a simple split works best: quick fixes, tests and larger projects. Add to that the task owner, channel dependency and a clear starting point.

The final output after the analysis should include at least:

• a comparison table of competitors using the same criteria,
• a list of gaps in the offer, content, communication and UX,
• communication patterns visible in SEO and ads,
• test hypotheses for pages, ads and content,
• a backlog of actions with priority, owner and estimated implementation difficulty.

The most common mistake is simple: the analysis ends with interesting observations, but does not turn into a work plan. The team then knows more about the market, but changes nothing in campaigns, content or on the website. And that is not a cliché. A strategic approach means turning every bigger insight into something concrete: what to change, where, on what basis and how you will assess the effect.

The most important mistakes to avoid in competitor analysis

The most common mistakes in competitor analysis are a lack of source material, too broad a comparison scope and drawing conclusions without separating facts from hypotheses. The problem is that when AI is not given specific URLs, screenshots, keyword exports or ad examples, it starts generalising. The result may feel polished, but it is usually of limited use for decision-making. If you cannot point to a source for a conclusion, do not treat it as a basis for implementation.

• Analysis without input data or based on random observations.
• Comparing too many entities at once, without splitting them into direct competitors, indirect competitors and substitutes.
• Mixing SEO, PPC, content, offer and UX into one general description without separate evaluation criteria.
• Treating traffic, budget and visibility estimates from external tools as hard data.
• Failing to distinguish between what is confirmed in the sources and what is interpretation.
• Ending the analysis with observations, without priorities, a task owner and a test plan.

The second common problem is an inconsistent analysis scope. One time you compare mobile, another time desktop, then service pages, and then the whole website — and suddenly the results stop being comparable. The question is: what are you actually measuring if you keep changing the ruler during the measurement. Analysis only works well when all competitors are assessed against the same criteria, at the same stage of the funnel and in the same market context.

It is also a mistake to focus solely on visibility in Google. In practice, the user makes a decision under the influence of several things at once: the promise, price, proof of quality, the simplicity of the form, page speed and the clarity of the CTA. A competitor may have less content and still win with a better message or simply a shorter contact path. And this is where it gets interesting: an analysis without the offer, ads and UX gives an image that is not so much modest as simply incomplete.

At the end, many teams fall into an operational mistake. They gather plenty of insights, but cannot turn them into a work plan. A comparison document on its own does not improve anything. The output of the analysis should end with a backlog of actions, not just a description of the market. Without priorities it is easy to throw small meta title fixes and a large landing page rebuild project into the same basket, even though their cost, risk and impact are completely different.

Optimising results: what to do to make the analysis effective

Effective competitor analysis likes order. It requires organised data, a consistent prompt format and a clear move from observation to decision, not to yet another “for review” file. It works best when, before you start, you define: country, language, customer segment, type of offer, device and the channels you are comparing. This prevents AI from mixing contexts and comparing apples with oranges. The more precise the input, the less general and the more actionable the output.

In practice, it is worth preparing separate sets for domains, keywords, ads, landing pages and UX observations. This division makes it easier to compare the same elements between brands instead of jumping between threads. A good practice is also to impose one answer structure: table, source evidence, confidence level and a separate section for hypotheses (to verify). Then you will more quickly distinguish hard findings from ideas that sound good but need checking.

• Collect data from several sources at once: GA4 and Search Console for your own website, keyword exports, a manual SERP review, ad libraries, a crawler and screenshots from mobile and desktop.
• Build every prompt on specific input: URL lists, tables, screenshots and message examples.
• First make AI list the facts from the sources, then the patterns, and only at the end the recommendations.
• Require the result in a comparative format, not a descriptive one, because it is easier to turn it into decisions.
• At the end, group the findings by business impact, ease of implementation and dependencies between channels.

Equally important is keeping an eye on the quality of the comparison itself. If you are analysing ads, check their consistency with the landing page, because that is where the “truth” of the promise is played out. If you are analysing content, look not only at the number of topics, but also at whether they really answer the user’s questions before the decision. The best insights do not just show what a competitor is doing, but where exactly they are losing or winning the user. The question is: at which point does that advantage appear, and at which point does it disappear.

The final material should be ready for marketing, SEO, content and sales to work with. The key is to close the analysis into a few hard deliverables: a comparison table, a list of gaps, UX issues, communication patterns, test proposals and a backlog with an assigned task owner. It sounds technical. And that is good, because such a package lets you move straight to implementation instead of once again starting from interpreting everything from scratch.