AI Prompts I use to improve my Customer Profiling (Customer Targeting Systems)

In the Commercial Excellence Play book, Customer Profiling is a more than just a segmentation exercise. Targeting both Current revenue and future potential. Often finding the right level of data about a customer has hindered or made us work on semi accurate information or heavy reliance on proxies. That's all changed with AI, we now have much more access to data that would take weeks for us to find and compile with a team can now be done by 1 individual within the organization. Your Commercial Excellence Practitioner. Here is what i use it for (prompts at the bottom):

1. Pull together customer data

2. Identify high-potential customers

3. Refine segmentation

4. Keep profiles current

5. Improve targeting decisions

6. Support judgment with AI

7. Generate an ICP

8. Find cross-sell and upsell opportunities

9. Create a customer action plan

10. Turn insight into messaging

AIM structure you can reuse

If you want to build your own prompts consistently, use this simple format:

A = Audience: Who is the output for?

I = Input: What data, context, or constraints should the AI use?

M = Method: What format should the answer take, and how should it be structured?

Now The Prompts:

1) Pull together customer data

Prompt:

Act as a commercial excellence analyst. Audience: a B2B sales and marketing team. Input: use CRM notes, customer service feedback, purchase history, web behaviour, and any available market data. Check Taxation filing and company annual reports for financial information, Check Company Websites, Social media posts, information aggregation platforms and Consulting company case studies. Method: organise the insights into a clear customer profile that identifies patterns, pain points, buying triggers, and key decision factors. Present the answer as a structured summary with headings for demographics, behaviours, needs, barriers, and opportunities.

2) Identify high-potential customers

Prompt:

Act as a revenue growth strategist. Audience: a sales leadership team. Input: review the customer list and rank accounts based on current revenue, growth potential, strategic fit, cross-sell potential, and likelihood to convert. Method: produce a prioritised list of customers with a simple rationale for each ranking. Present the output as a table with columns for account, priority level, reason, and recommended next action.

3) Refine segmentation

Prompt:

Act as a customer segmentation specialist. Audience: a commercial team seeking sharper targeting. Input: use customer size, industry, buying behaviour, product preference, margin contribution, and growth potential. Method: group customers into meaningful segments and explain why each segment matters commercially. Present the result as a segmentation framework with segment name, defining traits, needs, and best-fit offer.

4) Keep profiles current

Prompt:

Act as a customer intelligence analyst. Audience: a commercial team that needs dynamic customer profiles. Input: compare the existing customer profile against the latest customer activity, market changes, and new engagement signals. Method: identify what has changed, what assumptions are now outdated, and what should be updated in the profile. Present the answer as “what changed,” “why it matters,” and “recommended profile updates.”

5) Improve targeting decisions

Prompt:

Act as a go-to-market strategist. Audience: a sales and marketing leadership team. Input: use the customer profile, segment definitions, and commercial goals. Method: recommend who to target first, which messages to use, and which offers are most likely to convert. Present the output as a prioritised targeting plan with customer segment, message angle, offer, channel, and expected commercial impact.

6) Support judgment with AI

Prompt:

Act as a commercial decision-support advisor. Audience: a leader making customer targeting decisions. Input: analyse the available customer data, note key assumptions, and highlight any evidence gaps. Method: separate facts from assumptions, identify confidence levels, and recommend the most sensible next decision. Present the answer in three sections: evidence, interpretation, and recommendation.

7) Generate an ICP

Prompt:

Act as an ideal customer profile specialist. Audience: a B2B growth team. Input: use our best customers, worst-fit customers, and win-loss patterns. Method: build a clear ideal customer profile that shows who we should target, who we should avoid, and why. Present the result as a concise ICP with firmographics, behaviors, pain points, buying triggers, and disqualifies.

8) Find cross-sell and upsell opportunities

Prompt:

Act as a revenue expansion analyst. Audience: an account management team. Input: use customer purchase history, product mix, service usage, and account relationship data. Method: identify cross-sell and upsell opportunities with the strongest likelihood of success. Present the output as a ranked list with opportunity, evidence, expected value, and recommended next step.

9) Create a customer action plan

Prompt:

Act as a strategic account planner. Audience: a sales manager and account executive. Input: use the customer profile, segment priority, growth potential, and known pain points. Method: create a practical 90-day action plan to improve engagement and revenue. Present it as a step-by-step plan with objective, action, owner, timing, and success measure.

10) Turn insight into messaging

Prompt:

Act as a messaging strategist. Audience: a sales and marketing team. Input: use the customer profile, segment needs, and competitive context. Method: turn the insights into clear value propositions, proof points, and objection-handling messages. Present the output as a messaging framework with pain point, key message, proof, and call to action.