You can get a first read on your Nomination, the ANSWER category that measures whether AI recommends you for specific use cases, by turning the scenarios you serve into the questions your customers actually ask. This worksheet shows you how to build that list and read the results. For the full background, see the Nomination pillar guide.
Step 1: List the Scenarios You Serve
Write down the real situations your best customers are in when they buy. Be specific. Each scenario usually has three parts: a segment or industry, a size or stage, and a constraint or job to be done. For example: a remote-first startup that needs payroll, a mid-size factory that needs collaborative robots, a 5-person real estate team that needs a CRM. Aim for the 8 to 12 scenarios you most want to win.
Step 2: Turn Each Scenario into a Prompt
Phrase each scenario the way a customer would ask an engine, without naming your brand. Use templates like these:
- "Which [category] is best for [segment] [size]?"
- "Recommend a [category] for [scenario]."
- "What's a good [category] for a [customer type] that needs [job]?"
- "I run a [customer type]. What [category] should I use for [constraint]?"
- "Best [category] for [segment] on [budget or stage]."
Step 3: Run the 3 Rules
- Log out. Run every prompt in a logged-out or private session, so the engine is not drawing on your history.
- One fresh chat per prompt. Start a new conversation each time, so one answer does not color the next.
- Use your real market. Run the prompts in the geography you actually sell into, because answers vary by location.
Run each scenario on at least 2 engines, for example ChatGPT and Perplexity, plus Google's AI answers if you can.
Step 4: Score Each Scenario
For each prompt, mark one of four outcomes: recommended as the top fit, recommended but not first, merely mentioned, or absent while a rival is nominated. Note which competitor wins the ones you lose, and whether the engine gave a specific reason for its pick.
How to Read the Results
If you are recommended across most of your scenarios, your Nomination is strong. If you are merely mentioned or absent on the specific prompts while still appearing in the broad category, you are on the list but not the pick, which is explained in On the List, Never the Pick. If you are nominated for scenarios you do not serve best and skipped for the ones you do, you have a mismatch to correct. And if a competitor wins a scenario with a specific reason while you get a generic mention, that is the gap to close first.
What This Check Cannot Tell You
A scenario worksheet is a useful hint, not a baseline. It covers the use cases you thought to list, on one occasion, scored by eye, and answers vary from ask to ask. A real measurement runs many scenarios, repeats them, covers more engines, and scores every answer the same way so the result is stable and trackable. Doing that across every use case by hand is a real undertaking, which is the honest reason most teams have it run for them.
If your worksheet turns up scenarios you are losing, the next step is a proper baseline and a fix plan. A HiBot audit measures your Nomination across real use cases and ranks what to fix first. The tactics are in How to Improve Your Nomination Score. Download the ANSWER whitepaper to see the method, or request an AI visibility audit at hibot.com.
