HubSpot Prompts for AI Agents: The CRM Revenue Audit

Before running any revenue audit, inspect the HubSpot objects, associations, and properties available to you across contacts, companies, deals, activities, tickets, products, line items, campaigns, and workflows.

Do not assume a field exists. Tell me which fields are present, which are missing, and which you will use as proxies.

Operate in read-only mode. Do not create, update, delete, merge, export, enroll, or send anything.

For every finding in the audits that follow, show:
1. the exact HubSpot fields used,
2. the calculation logic,
3. records excluded because data was missing,
4. a confidence level from 1 to 5,
5. estimated dollar impact where it can be calculated,
6. whether this is a true performance issue or a data-quality issue.

If the data is too incomplete to support a conclusion, say so plainly instead of guessing.

Pull every open deal in HubSpot. Flag any deal with no logged activity in 21 or more days, a close date already in the past, or no next step set. Rank by deal amount.

Return a table with deal name, owner, amount, days stale, last activity, close date, next-step status, the likely reason it is stuck, and the recommended action.

Total the dollars sitting in stalled deals, then give a conservative estimate of what is realistically recoverable.

Audit contacts, companies, and deals for duplicate records, missing or malformed emails, missing lifecycle stages, deals with no owner, companies with no associated contacts, contacts with no company, and records missing source attribution.

Group the problems by type, count each, and give me the worst 50 records to fix first.

Flag which issues are hurting email deliverability, lead routing, reporting accuracy, or sales follow-up.

Find contacts showing demand but with no active sales motion. Include:
- contacts at MQL stage or higher with no associated deal,
- leads with no owner,
- contacts with no outbound activity in the last 30 days,
- contacts who opened, clicked, visited a key page, or submitted a form in the last 14 days,
- previously engaged contacts who have gone silent for 60 to 180 days.

Rank by fit, lead score, signal strength, recency, and estimated value. Use the actual average deal size from closed-won deals to estimate pipeline value where possible.

Sort each contact into one of four buckets: contact today, nurture, research first, or ignore. Draft short outreach for the top 25. Do not send anything.

For every inbound lead from the last 180 days, calculate the time from lead creation, form submission, or first conversion event to the first human sales activity.

Bucket response time into: under 5 minutes, under 1 hour, same day, next day, 3 or more days, and never contacted.

Show conversion rate, pipeline created, and closed-won revenue for each bucket. Break the results down by owner, source, form, campaign, day of week, and hour of day.

Estimate the revenue tied to slow or missing follow-up, and name the owners and sources with the biggest response-time problem.

Analyze won and lost deal history from the last 12 months. Calculate stage-to-stage conversion rates, average and median time in each stage, and where deals most often die.

Show me:
- the stage with the worst pass-through rate,
- the stage where deals sit longest before dying,
- the stage where current open deals are most over-aged,
- the dollars currently stuck in bottleneck stages,
- the single process fix most likely to improve conversion.

Then list current open deals that have exceeded the 75th-percentile time for their current stage.

Find every open deal above [your threshold amount] that is associated with only one contact at the account, or that has engagement from only one active stakeholder.

For each deal, show deal name, owner, amount, stage, associated contacts, last engagement by contact, the buyer roles that appear to be missing, a risk level, and the next stakeholder to engage.

Identify whether we are missing an economic buyer, technical buyer, user champion, procurement contact, legal contact, or executive sponsor. Rank by amount at risk and give a one-line relationship-widening angle for the top 20.

Build a win-back list from two groups:
1. Closed-lost deals from the last 6 to 18 months.
2. Former customers with no purchase, renewal, or open deal in the last 12 months or more.

Group the closed-lost deals by loss reason, especially no budget, bad timing, went silent, no decision, and internal priority changed.

Rank every win-back opportunity by original deal size, historical spend, fit, and likelihood to re-engage. For the top 25, draft a short message that references their prior context, explains why the timing may be different now, and offers a low-friction next step.

Analyze current customers by product purchased, historical spend, tenure, engagement, renewal date, support-ticket volume, and past expansion.

Compare each customer against the full product and service list and identify the obvious expansion gaps. Separately, identify happy or high-value customers who look like strong candidates for referrals, introductions, testimonials, or case studies.

Return two ranked lists:
1. Top 25 expansion opportunities, each with an estimated dollar amount and a reason.
2. Top 20 referral or advocacy opportunities, each with a specific ask.

For each account, say whether the better move is upsell, cross-sell, referral, testimonial, case study, or executive check-in.

Find every customer with a renewal date, contract end date, purchase anniversary, or subscription milestone in the next 180 days.

For each account, analyze account value, recent engagement trend, support-ticket volume, unresolved tickets, product or service usage fields if available, last executive touch, open expansion opportunities, and prior renewal history.

Rank accounts by combined churn risk and expansion upside. Show revenue at risk, the likely reason for the risk, the recommended owner, the outreach timing, and a combined renewal-plus-expansion angle.

Analyze closed-won deals from the last 12 months. Identify which industries, company sizes, lead sources, deal sizes, job titles, regions, and use cases closed fastest, won most often, and produced the highest average deal value.

Define our real best-fit profile from that data, not from what we assume it is.

Then scan open contacts, companies, and deals, surface the records that match this profile, rank them, and explain why each one matches the real ICP.

Break down contacts, opportunities, pipeline, closed-won revenue, win rate, average deal size, sales-cycle length, and churn risk by original lead source and campaign.

If spend data is available, calculate cost of acquisition, cost per opportunity, cost per closed-won customer, and ROI by source. If spend data is not available, identify which sources create real revenue versus sources that create volume that never converts.

Recommend where to increase, hold, decrease, or stop spend, with the dollar logic for each call.

Analyze closed-won deals from the last 12 months for discounting, price reductions, custom pricing, concessions, or unusually low amounts compared with similar deals.

Use list price, product pricing, line items, or quoted amount where available. Where it is not, compare against the average deal size by product, segment, company size, and owner.

Break discounting down by owner, product, segment, deal size, lead source, and close month. Show the estimated revenue or margin given away, whether discounted deals closed faster, and whether they had higher churn or lower expansion where that data exists. Estimate how much margin could be recovered if discounting moved to the team median.

Take the findings from every audit I ran and consolidate them into one master revenue stack.

Build a ranked table with: source audit, the account, contact, or deal, the owner, the issue, the dollars affected, a recoverable-dollar estimate, the effort to capture (low, medium, or high), urgency, a confidence score, the recommended first action, and whether this is a data issue, sales issue, marketing issue, customer-success issue, or leadership issue.

Then give me:
1. the top 10 quick wins this week,
2. the top 10 highest-dollar opportunities,
3. the top 10 process fixes,
4. the owners accountable,
5. total pipeline at risk,
6. estimated recoverable revenue,
7. the data gaps preventing stronger conclusions.

Draft nothing, send nothing, export nothing, and change no records until I approve the list.