A step-by-step way to benchmark competitors in AI search

1. Start with the outcome you actually need

Before you touch any tool or spreadsheet, decide what a win actually looks like. Are you trying to show up more often when buyers ask AI platforms to recommend vendors in your category? Or do you want to know why a specific competitor keeps getting cited and you don’t? Knowing the answer shapes every other decision.

Pick one narrow goal first — for example, “track our share of voice versus two direct competitors across 20 buying-intent queries on ChatGPT and Perplexity.” That is specific enough to act on. Vague goals produce vague data.

If you’re still figuring out which tools fit this kind of work, the upcoming guide on competitor benchmarking tools covers the main options worth evaluating.

2. Set up the simplest workable system

You need three things before you start collecting data: a defined query set, a defined competitor list, and a way to record results consistently.

• Query set: 15–30 prompts that real buyers would type into an AI platform at the consideration stage (“best [category] tools for [use case],” “compare [category] vendors,” etc.)
• Competitor list: 3–5 brands, not 20
• Tracking method: a shared spreadsheet or a dedicated ai search analytics platform — whichever your team will actually maintain

The biggest mistake here is starting with too many queries or too many competitors. You end up with a dataset nobody has time to interpret. Start small, prove the process works, then expand.

3. Follow the process without overcomplicating it

This is the core loop for how to do competitor benchmarking in AI search:

1. Run each query in your set across the AI platforms you care about (ChatGPT, Perplexity, Gemini, or whichever are relevant to your audience).
2. Record whether your brand is mentioned, where in the response it appears, and whether it is cited as a source.
3. Do the same for each competitor.
4. Calculate share of voice: how often each brand appears across the full query set, expressed as a percentage of total mentions.

If you’re doing this manually, batch it weekly. If you’re using a competitor benchmarking tool or an ai search analytics platform, you can automate the data collection and focus your time on interpretation. The goal is a consistent cadence — not a one-time audit.

4. Check what is working and what is not

After two to four weeks of data, look for patterns rather than individual data points. A single query result means almost nothing. Trends across 20+ queries over time tell you something real.

Questions worth asking at this stage:

• Which query types consistently surface competitors but not you?
• Are competitors being cited from specific domains or content types you don’t have?
• Is your mention rate improving, flat, or declining week over week?

The value of using ai search analytics here is that it removes the manual noise. But even in a spreadsheet, you can spot the gaps if your query set is focused. Avoid the trap of optimizing for queries where you already appear — the opportunity is in the gaps.

5. Adjust when your situation changes

Benchmarking is not a set-and-forget process. A few situations that should trigger a reset:

• A competitor launches new content or a product that changes how AI platforms describe your category
• You publish a major piece of content and want to measure whether it shifts your visibility
• A new AI platform becomes relevant to your buyers and you need to search monitor competitors there too

When any of these happen, update your query set to reflect the new landscape. Don’t just add queries — retire ones that are no longer relevant. A bloated query set is harder to act on than a tight, current one.

The same logic applies to your competitor list. If a new entrant starts appearing frequently in AI responses, add them. If a former rival has faded from results, drop them and use that slot for someone who matters now.