How to Do Competitive Analysis in AI Search
A step-by-step method for competitive analysis in AI search: choose prompts, benchmark rivals, read their citations and turn gaps into a prioritized plan.
Key takeaways
- Competitive analysis in AI search compares how often and how favorably AI engines name you versus rivals on the prompts your buyers actually use.
- Your competitors in AI answers are often not your usual SEO competitors - review sites, forums and marketplaces take slots too.
- The most useful step is reading why a rival was cited, which means opening the sources behind its mentions.
- Repeat the benchmark monthly on the same prompts; a single snapshot is noise.
To do competitive analysis in AI search, build a list of buyer prompts, run them across the engines your customers use, record which brands each answer names, then open the sources behind your rivals’ mentions to see why they were chosen. Turn the gaps into a ranked list of pages to create, fix or earn coverage on, and repeat the same prompts monthly. This article is the method; for software that automates it, see AI search competitive analysis tools.
Step 1: What are you comparing, and against whom?
Define the market in one sentence (“project management software for agencies”) and list 5 to 8 known competitors. Treat this list as a starting point. The real list comes from the answers in step 4, because AI engines often recommend brands you never thought of as rivals.
Step 2: Which prompts should you test?
Write the questions a buyer asks at each stage:
| Stage | Example prompt pattern |
|---|---|
| Discovery | “What are the best [category] tools for [use case]?” |
| Comparison | “[Brand A] vs [Brand B] for [use case]” |
| Alternatives | “[Competitor] alternatives” |
| Problem | “How do I [job your product does]?” |
| Local or segment | “Best [category] for [industry/country]” |
Aim for 20 to 30 prompts to start. Use plain phrasing; people do not type keyword strings into chat. Set language and country per prompt if you sell in more than one market.
Step 3: How do you collect the answers?
Run each prompt in each engine at least three times, logged out, same location. Record the brands named, their order and any cited URLs in a spreadsheet with one row per answer. Keep the raw text; you will want to reread it.
This is the point where manual work stops scaling. 25 prompts × 3 engines × 3 runs is 225 answers a month. A tracker such as GeoGen runs the same prompts every day and stores each answer, which also gives you the run-to-run variance for free.
Step 4: How do you measure share of voice and position?
For each brand, calculate:
- Share of voice: answers naming the brand divided by total answers.
- Average position: mean rank when named (first recommendation = 1).
- Sentiment: whether the description is positive, neutral or negative; GeoGen scores this 0 to 100.
Sort by share. Brands you did not list but that appear often are your real AI-search competitors. Add them to the list and rerun.
Step 5: Why were rivals chosen? Read their citations
This is the step most guides skip. For answers where a rival beat you, list the cited URLs and classify them:
- Rival’s own pages (product, pricing, docs)
- Third-party reviews and comparison articles
- Forums and community threads
- Publisher guides and news
- Directories and marketplaces
You will usually find a small set of third-party sources doing most of the work. GeoGen’s web hosting visibility report found that the top five sources, led by Reddit (11%), CNET (10.8%) and PCMag (7.9%), made up about 45% of citations across 2,000+ prompts in that vertical. Your vertical will have its own short list. For the tools that do this at scale, see citation analysis options.
Step 6: What gaps should you prioritize?
Score each gap on three things: how many prompts it affects, how winnable it is, and how close it is to revenue.
| Gap type | Typical fix |
|---|---|
| Rival named, you are absent | Create or rewrite the page that answers that prompt directly |
| You are named but ranked low | Strengthen comparison and proof content; earn third-party coverage |
| Described inaccurately | Fix your own pages and the sources that repeat the error |
| Cited source you can join | Pitch the publisher, answer the thread, update the listing |
Pick five fixes, not fifty.
Step 7: How do you keep the analysis current?
Rerun the same prompts monthly and compare share of voice, not individual answers. Log what you changed and when, so movement can be linked to action. GeoGen’s Actions turn gaps into a prioritized task list, and the kanban board tracks what is done.
Continue with the AI search monitoring hub for related methods, or compare the monitoring tools that automate this process.
Frequently asked questions
What is competitive analysis in AI search?
It is the process of asking AI engines the questions your customers ask and comparing which brands they name, how they describe them and which sources they cite. The result is a benchmark showing where rivals are recommended and you are not, plus the pages that earn those recommendations.
How do I benchmark competitors in ChatGPT?
Write 20 to 30 buyer questions, run each several times in a logged-out session, and record which brands appear and in what order. Compute each brand's share of answers. Repeating the exercise manually is slow, so many teams move to a daily tracker once the prompt list is stable.
Are my AI search competitors the same as my SEO competitors?
Often not. AI answers frequently name brands that rank poorly in classic search, and they cite publishers, forums and review sites alongside brands. Build your competitor list from the answers themselves, not only from keyword overlap.
What is a competitor citation analysis?
It lists the domains and URLs AI engines cite when they recommend a rival, then classifies them by type, such as review site, forum, publisher or the rival's own site. It tells you which third-party sources to earn coverage on.
How often should I repeat the analysis?
Monthly for the full benchmark, weekly for a short watchlist of priority prompts. AI answers shift as models and sources update, so compare averages over several runs rather than single results.
