As of 13 August 2026, AI can only partly identify your ideal customers.

What to actually do

1. Hand it to a person

   The route this page recommends

   A person who owns the outcome does this end to end, worth it when the failure is dear.

2. Use a tool built for this

   Second choice

3. Do it yourself

   The distant third

   A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

   How to actually do it

   1. Open your CRM, order records and customer feedback files, then create an anonymised table of your best customers with their industry, organisation size, location, use case, purchase value and repeat-purchase pattern.
   2. Add a second table of lost, low-value or difficult customers and record the factual reason each one was a poor fit, such as budget, timing, need or service requirements.
   3. Write down your current offer, price range, buying process, geographic coverage and the business outcome you want from the customer profile.
   4. Paste the two tables, your offer details and the business objective into the prompt, then ask the chatbot to produce the ranked segments, disqualifiers and testable hypotheses.
   5. Compare each proposed segment with your CRM results and customer feedback, remove claims unsupported by your records, and mark assumptions that need a customer conversation.
   6. Speak to several customers from the leading and rejected segments using the proposed problems and buying triggers, then update the profile with what they confirm or contradict.
   7. Use the final profile to define a small outreach test, and compare replies, qualified conversations and sales outcomes by segment before changing your wider prospecting criteria.

   Prompt

   Identify my ideal customers from the business evidence below.
   
   Business and offer:
   [Describe what you sell, to whom, the price range, buying process and geographic coverage.]
   
   Best existing customers:
   [Add 5 to 20 examples with industry, organisation size, location, use case, purchase value, repeat-purchase pattern and any other facts you can verify.]
   
   Poor-fit or lost customers:
   [Add examples and the factual reasons they were poor fits or did not buy.]
   
   Sales and customer evidence:
   [Paste anonymised CRM summaries, win and loss notes, customer feedback, support themes and website enquiries. Do not include unnecessary personal data.]
   
   Business objective:
   [State whether the priority is revenue, margin, retention, shorter sales cycles, a specific market entry or something else.]
   
   Analyse only the evidence supplied. Separate observed facts, reasonable inferences and assumptions. Produce:
   1. A specific ideal customer profile with firmographic, behavioural and situational characteristics.
   2. The strongest customer segments, ranked by fit and explainable evidence.
   3. The problems and buying triggers most associated with good-fit customers.
   4. Disqualifiers and segments I should avoid.
   5. Five testable hypotheses about where to find more customers like these.
   6. The missing evidence that could change the conclusion.
   7. A simple validation plan using existing records, customer conversations and a small outreach test.
   Do not claim that a segment is profitable unless the supplied evidence supports it. Do not invent market sizes, customer facts or conversion rates.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.