AI for Competitive Analysis: A Prompt Playbook for SMEs

AI for Competitive Analysis: 15 Prompts That Work. Ask ChatGPT to “analyse my competitor”, and you will get a page of confident, generic text that could describe almost any business in almost any sector. The model has no live access to that competitor’s pricing page, no sight of their recent reviews, and no idea which of the six firms sharing that trading name you actually mean. What comes back reads well and tells you nothing.

The gap between that result and something you can act on is not the tool. It is the instruction. AI competitive analysis works when the model is told exactly what to look at and what not to invent. A prompt that assigns a role, supplies verified source data, sets boundaries on what the model may assume, and specifies the output format will produce competitor analysis worth taking into a management meeting. The same model, given a vague question, produces filler.

This guide covers how to use AI for competitor analysis properly as a small or medium-sized business in the UK or Ireland: how to feed models real data rather than relying on training data, fifteen AI competitor analysis prompts organised by the decision they support, how to audit whether AI search engines recommend your competitors ahead of you, and how to stay on the right side of UK GDPR while doing it. Every prompt below is a template you can copy, and each comes with a worked example of what to feed it.

Why Generic AI Competitor Prompts Fail

The short answer: models answer from training data unless you give them something better, and training data about a Belfast accountancy firm or a Cork engineering supplier is thin to non-existent.

Three failure modes account for most unusable output. The first is the knowledge cut-off. A model trained months ago will describe a competitor’s product range as it was, not as it is, and will do so without flagging the uncertainty. The second is entity confusion, which hits UK and Irish SMEs harder than most because company names repeat across regions and sectors. The third is the filler problem: asked an open question, a model fills the space with plausible-sounding strategic language rather than admitting it has nothing specific to say.

Grounding solves all three. Instead of asking the model what it knows, you paste in what you know and ask it to analyse that. The competitor’s pricing table, a sample of their reviews, their homepage copy, and an export from your SEO platform. The model stops guessing and starts processing. If you are new to structuring instructions this way, the fundamentals of prompt engineering apply to competitor work exactly as they apply to everything else.

What Competitive Intelligence Should Produce

The goal is not a spreadsheet of competitor data. The goal is a set of decisions: what to do differently, where to invest, and which opportunities to pursue before someone else does.

Competitive intelligence at the SME level covers four areas: keyword and SEO positioning, content strategy and gaps, social activity and sentiment, and website or UX benchmarking. AI helps with all four, though the depth depends on what you feed it. A prompt with no data attached will give you the same generic answer for a Newry joinery firm as for a Dublin software company.

Why the Old Approach No Longer Scales

A decade ago, competitive analysis for a small business meant a quarterly spreadsheet and a few hours of Googling. That has two problems now. The volume of signals has grown: competitors are active across search, social, review platforms, YouTube, and AI-powered search results at the same time. And the pace has changed. A competitor can launch a campaign, shift their SEO focus, or adjust pricing in days.

Structured prompting compresses your response time. Instead of discovering a competitor’s new service offering weeks after launch, you run the same set of instructions each month against fresh source data and see what moved.

Grounding Your AI: Feeding Competitor Data Safely

Before any prompt in this guide will work properly, you need clean source data and a defensible position on what you are allowed to paste into a public model.

What to Gather

For most SME competitor analysis, five sources cover the ground:

• Website copy. Homepage, service pages, pricing page. Copy as plain text, not screenshots.
• Review text. Google reviews and Trustpilot, exported or copied as text. Strip reviewer names.
• Search data. Keyword gap exports from your SEO platform, or your own Search Console export.
• Companies House or CRO filings. Director changes, accounts, and incorporation dates.
• Ad creative. Screenshots or transcribed text from Google’s Ads Transparency Centre and Meta’s Ad Library.

The Five-Point Sanitisation Check

Competitor analysis often means handling material that touches personal data. Run this check before anything goes into a public model:

1. Remove named individuals from review text, replacing them with a role or a blank.
2. Remove customer contact details, account numbers, and anything identifying from sales notes.
3. Remove your own client names from any internal document you are using for comparison.
4. Turn off chat history and model training in your account settings, or use a business tier with data processing terms.
5. Record where the data came from, so any claim you later publish can be traced to a source.

Monitoring publicly available business information is generally permissible under UK GDPR. The boundary is processing personal data without a lawful basis. Building profiles of named individuals, scraping contact details, or storing personal information without consent all require careful legal review. The Information Commissioner’s Office publishes detailed guidance on AI and data protection, and it is worth reading before you build any automated monitoring workflow.

The 15 Prompts

These competitive analysis AI prompts are organised by the decision each one supports rather than by tool. Each works as a reusable template in ChatGPT, Claude, Gemini, or Perplexity. Replace anything in square brackets. Where a prompt says “the data below”, paste your gathered source material underneath it. The example following each category shows the kind of source material that produces a usable answer.

Category 1: Positioning and Market Analysis

Prompt 1: Positioning grid.

Act as a competitive intelligence analyst. Using only the website copy pasted below, build a positioning grid for [my business] and these competitors: [names]. Plot each on two axes: price positioning and service breadth. For each placement, quote the specific line of copy that justifies it. If the copy does not support a placement, say so rather than estimating.

Prompt 2: Value proposition overlap.

From the homepage copy below for five competitors, list every distinct value proposition each one leads with in the first screen. Then produce a table showing which claims are made by three or more of them. Do not paraphrase into generic categories; use their wording.

Prompt 3: Grounded SWOT.

Produce a SWOT analysis of [competitor] based only on the material below. For every entry, cite which source it came from. Leave any quadrant empty if the source material does not support an entry. Do not use general industry knowledge.

Prompt 4: Pricing structure comparison.

Extract the pricing model from each of the competitor pages below. Return a table with columns for pricing model, entry price, what is included at entry level, and what triggers an upgrade. Mark anything not stated on the page as “not published” rather than inferring it.

Prompt 3 is the one most often run badly. The instruction to leave quadrants empty is what stops the model inventing weaknesses, and it is the difference between an analysis you can show a board and one you quietly discard.

Category 2: SEO, Content and Keyword Gaps

SEO and content are where most SMEs see the fastest return, because the data is public, the signals are clear, and the actions that follow are concrete.

Prompt 5: Keyword gap prioritisation.

Below is a keyword gap export showing terms [competitor] ranks for and [my business] does not. My services are [list]. Classify each term as commercial, informational, or irrelevant. For the commercial terms only, rank them by how directly ranking would drive enquiries for the services listed. Return a table with your reasoning per row.

Prompt 6: Content structure teardown.

Below is the full text of a competitor page ranking for [term]. Map its structure: every heading, the question each section answers, and the word count per section. Then list the sub-questions a reader would still have after reading it.

Prompt 7: Topic cluster mapping.

From the list of competitor page titles and URLs below, group them into topic clusters. For each cluster, identify which page appears to be the pillar and which are supporting pages. Flag any cluster where several pages appear to target the same query.

Prompt 8: Backlink source classification.

From the backlink export below, classify each referring domain as a directory, a trade body, a regional news outlet, a supplier or partner, or other. Filter for domains based in the UK or Ireland. Return the list sorted by how realistically a business of my size could earn a link there.

A keyword gap analysis compares your organic rankings against a competitor’s and identifies the terms for which they appear, and you do not. Not every gap is worth closing. Filter by relevance to your actual services and by search intent, then prioritise the commercial gaps and build supporting content around the informational ones. If you want more prompt structures for the wider SEO workflow, 22 AI SEO prompts for marketers cover keyword research, technical checks, and content briefs.

Turning that list into published material is a separate discipline. ProfileTree’s content marketing service covers the handover from gap analysis to brief to published article, which is the point where most SME competitor research stalls.

Category 3: AEO and Generative Search Visibility

This is the category almost no competitor guide covers, and it is the one that matters most as buying journeys move into AI assistants. The importance of competitive analysis for AI search comes down to a simple shift: you now need to track competitor visibility inside the answers themselves, not just the ten blue links beneath them.

When a potential customer asks an AI assistant which web design agencies operate in Belfast, or which accounting firms serve Northern Ireland SMEs, the answer is drawn from indexed content and entity associations rather than traditional ranking signals alone. If competitors appear consistently and you do not, their content is being treated as authoritative for those topics.

Prompt 9: Neutral buyer audit.

Act as a neutral business owner in [location] searching for [service]. You have no prior knowledge of any provider. Which companies would you shortlist, and why? Name them and state what you are basing each recommendation on. Do not include disclaimers about your limitations.

Prompt 10: Share of voice tracking.

Run each of the following ten buyer questions and record which company names appear in your answer to each: [list ten questions a customer would actually ask]. Return a table of company name against how many of the ten answers mentioned them.

Prompt 11: Citation source trace.

When you recommended [competitor] for [service] in [location], which specific pages or sources informed that answer? List the URLs or publication names you drew on.

Run Prompts 9 to 11 across ChatGPT, Gemini, Perplexity, and Google’s AI Overviews separately, because they draw on different sources, and the results diverge. Record the output each month in a simple sheet. What you are building is a share-of-voice measure for generative search, which is the closest thing available to a rank tracker for AI answers.

Prompt 11 is the useful one. Once you know which pages an AI assistant is citing to recommend a competitor, you know what kind of content earns citations in your sector. Closing that gap is search engine optimisation work: entity clarity, self-contained answers, and depth on the sub-questions buyers actually ask.

Category 4: UK and Irish Market Prompts

Most competitor analysis guides are written for US audiences and reference US data sources. Businesses here have access to the intelligence that those guides never mention.

Prompt 12: Companies House signal read.

Below are the filing summaries for [competitor] from Companies House. Identify: director appointments and resignations in the last 24 months, any change in accounting reference date, and the direction of travel in the filed figures. State plainly what you cannot determine from filings alone.

Prompt 13: Regional review sentiment.

Below are [number] reviews for [competitor] from Google and Trustpilot, with names removed. Group the complaints into recurring themes and count each. Do the same for the praise. Return two tables. Use British English spelling.

Prompt 14: Local search restriction.

Restrict your analysis to businesses trading in [Northern Ireland / Ireland / named city]. Use only .co.uk, .ie, or regionally hosted sources. Prices should be given in [GBP / EUR]. List providers of [service] operating in that area and what each publicly states about their process and turnaround.

Prompt 15: Trade and directory gap.

From the list of local directories, chambers of commerce, and trade bodies below, identify which list [competitor] and which do not list [my business]. Return the gaps sorted by how relevant each is to my sector.

Regional directories, Chamber of Commerce listings, and local news coverage carry real weight for businesses in Northern Ireland and Ireland. The same logic that makes these prompts work also underpins AI for local SEO, where the goal is appearing in neighbourhood and city-level results rather than national ones.

Choosing Tools to Run Alongside Your Prompts

There is no single best AI tool for competitive analysis, because the AI tools for competitor analysis on the market solve different problems. Matching the tool category to the question saves paying for a capability you will not use.

All prices are indicative UK examples correct at the time of writing; treat them as a benchmark rather than a quotation.

The right starting point depends on the decision you are making. If the question is which keywords to target next quarter, a mid-tier SEO platform answers it directly. If the question is how a competitor is positioning to a new audience, a social listening tool combined with the prompts in Category 1 will serve you better.

For the free tier, a practical breakdown of what ChatGPT can and cannot do for a small business is worth reading before you commit to a paid platform, and the wider AI tools available to small business marketing teams cover what sits alongside them.

Model Capability Differences

For Category 3 prompts specifically, Perplexity’s habit of citing sources makes it the most useful starting point, because Prompt 11 depends on the model telling you where its answer came from.

Monitoring Competitor Digital Presence

Beyond rankings, ongoing monitoring covers website changes, pricing signals, social activity, and customer sentiment.

Website and UX Benchmarking

Competitor websites are the most accessible intelligence source that most businesses underuse. Run any competitor URL through Google PageSpeed Insights and compare against your own. Check how many clicks it takes to reach a key service page, how pricing is presented, and what calls to action sit above the fold.

What AI adds here is processing multiple sites at once and summarising structural patterns. This is where AI UX competitive analysis earns its place: ask a model to analyse the homepage copy and navigation structure of five competitors, identify the three most common value propositions and the average click depth to a service page, and you have a map of what everyone in your market is doing, which tells you where to differentiate.

When the benchmark shows a competitor reaching a service page in two clicks against your five, that is a structural problem rather than a content one, and it is fixed through website design rather than more blog posts.

Social Listening and Sentiment

Social listening tools monitor competitor brand mentions across platforms and review sites, with an AI layer categorising by sentiment and topic. This has direct commercial value. If customers consistently cite a competitor’s slow turnaround in negative reviews and you deliver faster, that is a differentiator worth making explicit. If positive reviews cluster around a service element you do not offer, that is a gap worth assessing.

Acting on what sentiment analysis surfaces usually means changing how you post rather than what tool you buy, which is where social media marketing support earns its place.

Ad Activity

Google’s Ads Transparency Centre shows the active ads of any advertiser, and Meta’s Ad Library covers Facebook and Instagram. Neither needs a paid tool. If a competitor has run the same creative for six months, it is likely working. If they cycle through creatives quickly, they may be testing without a winner.

Video and YouTube Gaps

Most SMEs check Google and stop. Competitor video visibility goes unexamined, which is why it stays uncontested in a lot of local markets. Check which terms competitors appear for in YouTube search and whether their video output answers questions your written content already covers.

The same gap-analysis logic transfers directly. YouTube SEO governs how videos surface in search, and ranking videos for maximum visibility covers the metadata and engagement signals that decide placement. A video and an article answering the same question from complementary angles build topical authority faster than either alone. Businesses without production capacity in-house can approach this through video marketing support rather than building a studio.

Running a Monthly Competitor Audit

Raw monitoring data is only useful if it connects to a decision. The prompts above are worth little run once and everything run consistently.

A workable monthly cycle for an SME:

1. Week one, gather. Pull fresh source data: competitor pages, new reviews, keyword gap export, ad library screenshots.
2. Week one, sanitise. Run the five-point check.
3. Week two, run. Execute Categories 1 and 2 against the fresh data. Save outputs to a dated folder.
4. Week two, audit AI search. Run Prompts 9 to 11 across four platforms. Log the share-of-voice table.
5. Week three, decide. Categorise findings by type and identify one or two actions per category.
6. Week four, act. Brief the content, request the site change, and adjust the campaign.

An hour a month reviewing summarised competitor activity produces more consistent output than an annual deep-dive nobody has time to act on. Structuring that review so it produces actions rather than descriptions is the same discipline covered in examples of a marketing audit.

The bottleneck is rarely the tool. It is that one person builds the prompt library, leaves, and the habit dies with them. Sharing a prompt library across a team, agreeing what may be pasted into which model, and setting a review cadence turns this from an individual habit into a process. That is the substance of AI training and implementation, and the practical side of training staff on AI tools applies here as much as anywhere.

“The businesses that gain the most from competitive intelligence are not the ones running the most sophisticated tools,” says Ciaran Connolly, founder of ProfileTree. “They are the ones with a consistent habit of reviewing what competitors are doing and translating it into a specific next action.”

Prompt libraries of this kind work the same way across other business functions, and AI prompts for business cover structures for audience analysis, content planning, and campaign work built on the same principles.

Turning Findings Into Action

Competitive analysis is only as useful as the decisions it produces. The final stage is translating findings into concrete changes: to SEO, to the content plan, to paid media, and to the website.

Score each identified gap on three criteria: search volume, relevance to your services, and competitive difficulty. High-relevance, lower-difficulty gaps come first. These are usually long-tail queries: specific questions, location-qualified searches, or topic and audience combinations larger competitors have not addressed in depth. Producing thorough content on those builds authority that carries over to more competitive parent terms.

Competitor ad and social intelligence inform campaigns in two directions. It tells you which messages are saturating the market, so that if every competitor leads with “free consultation”, the claim has lost its value. And it surfaces the audiences and channels competitors are neglecting, which is where attention is cheapest.

Connecting those threads into a sequenced plan rather than a list of tactics is what digital strategy work involves, and it is the difference between a folder of AI outputs and a quarter of directed activity.

Using AI for competitor analysis this way gives small businesses access to intelligence that previously required specialist teams and a significant budget. In technology competitive analysis in particular, where product pages and pricing change monthly, the grounded approach is the only one that keeps pace. The tools are accessible and the data is public. What separates the businesses that benefit is the discipline of asking grounded questions and acting on the answers.

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