AI Prompt Library

Aftermarket Manufacturer
Intelligence Prompts

Ready-to-run prompts for sales performance, customer health, product mix, rep coaching, and operations — built for B2B aftermarket teams.

01Load the required data files into your AI workspace

02Copy the starter prompt and paste as-is or personalize

03Use the follow-up tips to go deeper after the first output

Sales Performance

4 prompts

Use Case	Starter Prompt	Tips & Required Data
Q1 Revenue Health Check Get an executive-ready summary of where the business stands and what needs attention before the next period	You are reviewing Q1 revenue data for a B2B aftermarket manufacturer. Give me the three most important things leadership needs to know heading into Q2 — one about account health, one about product mix, and one about risk. Lead each with the dollar impact and be specific.	▸Works best when customer totals and SKU totals are loaded together — AI will cross-reference automatically →Follow up: "Now tell me the one thing in this data that should concern us most that we're probably not talking about" Customer Monthly Totals SKU Monthly Totals
Hidden Risk in Top Accounts Find declining accounts that still rank high in total spend — the risk most revenue reports miss	Looking at this revenue data, which accounts are declining but still ranking in our top 25 by total spend? What does that combination tell us about where our biggest Q2 risk actually lives? Rank them by urgency, not just by size of decline.	▸The at-risk file adds root cause context — load it alongside revenue data for richer output !If accounts show decline AND high spend, those are your follow-up calls this week — not next quarter Customer Monthly Totals Lapsed & At-Risk Accounts
Separate Controllable vs. External Declines Prioritize recovery actions by understanding which drops you can actually fix	Look at every account that showed a revenue decline this period. Separate them into two groups: declines within our control to fix, and declines caused by something outside our control. For each account, use the sales notes and call transcripts to explain what the conversations actually reveal about why the number dropped.	▸This prompt requires all four data layers to work properly — revenue data sets the what, notes and transcripts explain the why →Follow up: "For the controllable declines, draft a one-paragraph recovery brief for each rep" Customer Monthly Totals Lapsed & At-Risk Accounts Sales Notes Call Transcripts
Forecast Accuracy Check Compare what the data suggests will happen in Q2 against what your reps are saying in the field	Based on Q1 revenue trends and what reps are noting in the field, which accounts are signaling growth in Q2 and which are signaling contraction? List them separately. For each growth signal, tell me what's driving it. For each contraction signal, tell me if it's confirmed or just a risk.	▸Sales notes are the key input here — signals like "larger order coming in April" show up as plain text →Follow up: "Which of these signals came from the rep, and which came directly from the customer?" Customer Monthly Totals Sales Notes

Product & SKU Performance

4 prompts

Use Case	Starter Prompt	Tips & Required Data
Revenue vs. Margin Disconnect Find the products driving the most revenue but the least profit — and the high-margin SKUs being undersold	Looking at SKU performance data, which products are driving the most revenue but the lowest margin? Which are the opposite — high margin but low volume? Tell me where we should be pushing harder based on margin opportunity, not just top-line performance.	▸Requires margin % per SKU in your data — without it, AI can only sort by revenue →Follow up: "Which reps are selling the most high-margin SKUs and which are defaulting to the easy sell?" SKU Monthly Totals Master SKU List
SKUs Quietly Disappearing Identify products customers used to buy regularly that are no longer appearing in their orders	Before I show you the data, tell me: are there any SKUs you've noticed customers asking about less, substituting with something else, or dropping from orders? Share what you're seeing, then I'll cross-reference it against the order data to show you where SKU drop-off is actually happening and which accounts stopped buying a product they used to order regularly.	→The opening question primes the AI to look for specific patterns — don't skip it even if you think you know the answer !SKU drop-off by account is often an early warning sign before full customer churn SKU Monthly Totals Customer Monthly Totals
Portfolio Concentration Risk Understand how dependent the business is on a small number of products	Based on this SKU data, what percentage of total revenue comes from our top five products? If we lost those SKUs or saw a 30% volume drop on them, what would that mean for the business? Flag any product concentration that should concern leadership.	▸Clean SKU-level revenue data with monthly breakdowns produces the most useful output here →Follow up: "Which customers are most responsible for those top five SKUs? Are they also in our at-risk list?" SKU Monthly Totals
Cross-Sell Gap Analysis Find accounts buying one product category but not the complementary products they should also need	Look at which SKUs our top 20 accounts are buying. Which accounts are buying products in one category but not the complementary category they would typically need alongside it? Give me the top five cross-sell gaps and which rep owns each account.	▸Works best when your SKU list includes product category groupings — AI uses category logic to identify the gaps →Follow up: "Write a one-line outreach message for each of these gaps that a rep could use on their next call" SKU Monthly Totals Customer Monthly Totals Master SKU List

Rep Coaching & Performance

4 prompts

Use Case	Starter Prompt	Tips & Required Data
Missed Opportunity Scan Surface every moment a customer signaled a need or buying intent that went unacted on	Read through all the sales notes and call transcripts. Find every moment where a customer signaled a future buying opportunity — a new project, a budget mention, a timeline, a problem they need solved, or a question about a product they don't currently buy — and the rep did not follow up. List each instance with the customer name, what the customer said, and what action was or wasn't taken.	!This prompt reliably surfaces 5–10 missed opportunities in a real dataset — plan for the conversation it starts →Follow up: "Rank these by estimated revenue impact if the rep had followed up" Sales Notes Call Transcripts
Rep Behavior Comparison See the difference between your top and developing reps without looking at revenue — look at how they talk	Read the call transcripts for all reps. Without me telling you anything, what is the most significant behavioral difference between how the top-performing rep and the lowest-performing rep handle customer conversations? Use specific examples from the transcripts to make your case.	▸Works without revenue data — the AI reads the conversation quality directly from transcripts →Follow up: "Write the coaching feedback you would give the lower-performing rep in a 1:1 — be specific and cite their exact words" Call Transcripts
1:1 Coaching Brief Walk into a rep coaching conversation with specific evidence, not generalities	Act as a sales manager preparing for a 1:1 with [rep name]. Based on their call transcripts and sales notes from this period, write a coaching brief. Include: what they are doing well with specific examples, the single most important behavior to change, a specific call moment that illustrates the gap, and the suggested focus for the next 30 days.	→Replace [rep name] before running — specificity dramatically improves output quality !Always review AI output before using in an actual coaching conversation — validate the examples cited Call Transcripts Sales Notes
Activity vs. Results Disconnect Find reps who are active but not converting — or accounts getting too little attention relative to their size	Cross-reference rep activity in the sales notes against the revenue each account is generating. Which accounts are receiving high rep attention but showing flat or declining revenue? Which high-revenue accounts appear infrequently in the notes? Tell me what each pattern suggests about how we're allocating rep time.	▸Sales notes need a date and customer field for this to work — frequency is calculated from how often an account appears →Follow up: "Which accounts should get more rep time based on revenue potential, and which are getting attention they don't warrant?" Sales Notes Customer Monthly Totals

Customer Health & Retention

4 prompts

Use Case	Starter Prompt	Tips & Required Data
Full Account Story Get a complete picture of a specific account from every data source at once	Pull everything you have on [account name] across all data sources. Walk me through the full account story: what the revenue data shows, what the sales notes tell us about recent activity, and what the call transcripts reveal about the relationship and any risks or opportunities. End with one recommended action and who owns it.	→This is your pre-call prep prompt — run it 10 minutes before any strategic account conversation !Replace [account name] before running — works for any account in your dataset Customer Monthly Totals Sales Notes Call Transcripts Lapsed & At-Risk Accounts
Early Churn Warning Signals Identify accounts showing behavioral patterns that precede churn before revenue confirms it	First — have you noticed any customers ordering less frequently, skipping their usual SKUs, or going quiet on communication? Tell me what you're seeing, and I'll cross-reference it against the order data and sales notes to show you where the behavioral warning signs are already showing up — things like order frequency changes, SKU drop-off, and reduced engagement in calls.	→Start with your gut read — the AI uses it to focus its analysis rather than scanning everything equally ▸Month-over-month order data gives AI the frequency signal it needs — single-period snapshots miss this Customer Monthly Totals Sales Notes
Triage At-Risk Accounts Decide which at-risk accounts to prioritize when you can't work all of them at once	Based on recovery probability, revenue size, and the root cause listed for each at-risk account, which three accounts should leadership prioritize for recovery action this week? For each one, make a specific case for why it's recoverable and what the first action should be.	▸Recovery probability and root cause fields in your at-risk file are essential — without them AI can only sort by revenue size →Follow up: "Which accounts on this list are probably not recoverable and should be deprioritized?" Lapsed & At-Risk Accounts Customer Monthly Totals
Flat Account Expansion Turn long-term loyal accounts that have stopped growing into active expansion opportunities	Which of our established accounts have been ordering roughly the same products at roughly the same volume for multiple periods with no growth? For each flat account, tell me whether the notes suggest anyone has actually tried to expand the relationship — or whether the account has just been maintained on autopilot. Flag the top three expansion opportunities being left on the table.	!Flat accounts are often the biggest untapped revenue in a book of business — this prompt reliably surfaces them →Follow up: "What specific product would you recommend introducing to each of these three accounts based on what they currently buy?" Customer Monthly Totals Sales Notes SKU Monthly Totals

Operations & Fulfillment

3 prompts

Use Case	Starter Prompt	Tips & Required Data
Backorder Impact Analysis Understand which stockouts are costing the most in customer trust and revenue	Scan the sales notes and call transcripts for any mention of backorders, stock issues, or delayed shipments. For each instance, tell me which account was affected, which SKU was unavailable, and whether the customer's response suggests a relationship risk beyond the immediate order. Rank by business impact.	▸This is a qualitative scan — works on notes and transcripts without needing inventory data →Follow up: "Which of these backorder situations could have been avoided with better advance communication to the customer?" Sales Notes Call Transcripts
Shipping & Quality Complaint Trends Identify patterns in damage claims, quality issues, or delivery failures before they become customer losses	Read through the sales notes and flag every mention of a damaged shipment, quality complaint, or delivery failure. Are these isolated incidents or a pattern? Which SKUs or shipping lanes are involved most often? And have any of these incidents affected account revenue in the periods that follow?	!Damage claim patterns often appear in notes weeks before they show up in churn data — this prompt catches them early →Follow up: "Draft a short internal ops memo summarizing the pattern and recommending one immediate action" Sales Notes
Urgent Order Win Patterns Find where same-day responsiveness to urgent customer needs is creating competitive advantage	Look through the sales notes and transcripts for situations where we responded urgently to a customer need — a same-day shipment, an emergency stock pull, a fast turnaround on a technical question. What do these moments have in common? And which accounts seem most likely to consolidate more spend with us because of how we responded?	→Great prompt for identifying what your team is already doing right — use the output to train other reps on what "good" looks like →Follow up: "Turn the best example into a one-paragraph case story we could share with prospects" Sales Notes Call Transcripts

Knowledge Base & Product Intelligence

4 prompts

Use Case	Starter Prompt	Tips & Required Data
Warranty Question Resolution Give customer service reps instant, accurate answers to coverage questions without digging through policy docs	A customer says their [product name] failed at [X months]. Based on our warranty policy, are they covered? Walk me through the coverage window, what voids the warranty for this product type, and exactly how a rep should communicate the decision — including what to say if the answer is no.	→Replace [product name] and [X months] before running — specificity makes the answer actionable ▸Load both warranty policy and product specs together — AI needs specs to know which warranty tier applies Warranty Policy Product Technical Specs
Cross-Document Product Guidance Answer complex customer questions that require pulling from specs, warranty, and application guidelines at once	A customer wants to use [product SKU] for an application that regularly exceeds its rated specification. Based on our product specs and warranty policy, what should we tell them? Include what the spec says, what the warranty implications are if they run it outside rating, and how a rep should frame this conversation so the customer feels helped rather than turned away.	▸This prompt requires both specs and warranty — either doc alone gives an incomplete answer →Follow up: "Is there a product in our catalog that is rated for what they actually need? If so, which one?" Product Technical Specs Warranty Policy Master SKU List
New Rep Onboarding Brief Synthesize what a new sales rep needs to know across all product and policy documents before their first call	Based on everything in our knowledge base, what are the five things a new sales rep needs to know cold before their first customer call? Focus on what will make them sound credible, avoid costly mistakes, and handle the questions customers ask most often. Give one practical example for each.	→The output from this prompt makes a strong onboarding document — follow up with "Format this as a one-page reference card" ▸More docs = richer output here — load everything you have Product Technical Specs Warranty Policy QA Procedures Master SKU List
Knowledge Base Gap Audit Use AI to find what your documentation doesn't answer — before a customer finds it first	You've read through our product knowledge base. What are the questions a customer or new rep could reasonably ask that our current documentation cannot answer? Group them by category — product performance, warranty edge cases, installation, and application guidance. These are our documentation gaps.	!This is one of the most underused prompts in any knowledge base workflow — run it once a quarter →Follow up: "Prioritize these gaps by which ones are most likely to cost us a sale or a warranty dispute" Product Technical Specs Warranty Policy QA Procedures