Your Reps Are Quoting the Wrong Products (And AI Can Fix It)

Welcome to The Ops Digest!

Each week, we drop no-BS insights + one AI prompt to cut wasted costs, tighten workflows, and eliminate manual grunt work.

Today: the products your reps never quote - and the revenue that disappears with them.

Let’s dive in.

💰 Your Reps Are Quoting the Wrong Products (And AI Can Fix It)

Your catalog has 5,000 SKUs.

Your reps quote the same 20.

Every single time.

They're not lazy. They're human. They default to what they know—the products they've sold before, the ones they can explain confidently, the SKUs that come to mind first.

Meanwhile, the perfect complementary product sits in your warehouse, unquoted and unsold.

📉 The Cost of Comfort Zone Selling

Research from several McKinsey studies suggests that B2B companies leave 25-30% of potential revenue on the table through poor product mix optimization. That's not because customers don't want more - it's because your reps aren't showing them what's available.

Think about it: Your rep knows the customer needs fasteners. They quote the standard bolt package because that's what they always quote. But sitting three aisles over in your warehouse are:

• Specialty washers that prevent the exact corrosion problem this customer mentioned last month

• A higher-margin stainless option that actually costs the customer less over three years

• The thread-locking compound that 60% of similar customers buy with this exact bolt

None of these get quoted. The rep doesn't think of them. The customer doesn't know they exist.

You just left $800 on the table on a single order.

Multiply that across hundreds of quotes per month, and you're looking at six figures in annual missed revenue - from products you already stock, for customers who already trust you.

🎯 Why Reps Default to the Same SKUs

This isn't a training problem. It's a human memory problem.

• Cognitive load: Your reps can't hold 5,000 products in their head. Research shows that 82% of B2B buyers think sales reps are underprepared, with 77% saying reps don't understand how products could solve their specific problems. (Zippia).

• Risk aversion: It's safer to quote what you know will work than to suggest something unfamiliar, even if it's better for the customer.

• Time pressure: Finding the right complementary products takes research. When you're processing 30 quotes a day, you don't have time to browse the catalog for every single one.

• Lack of visibility into alternatives: Even if a rep wanted to find better options, most systems don't surface them. ERP systems show what's in stock, not what should be quoted together.

The result? Your reps become really good at selling 4% of your catalog. The other 96% collects dust.

🤖 How AI Becomes Your Quote Optimizer

AI doesn't get tired. It doesn't forget. And it can instantly analyze your entire catalog against a customer's specific situation to recommend the optimal product mix.

The best part? You set this up once in a Claude Project. Upload your catalog and customer data, and then every quote takes 10 seconds instead of searching through spreadsheets or guessing based on memory.

Here's what AI sees that your reps don't:

• Purchase history patterns: Customers who bought X also bought Y 73% of the time. Your rep doesn't know that. AI does.

• Industry-specific applications: This customer is in food processing. AI knows that food-grade SKUs exist and should be recommended instead of standard options.

• Margin opportunities: There's a higher-margin alternative that performs identically. AI surfaces it. Your rep quotes the usual product.

• Complementary products: The AI recognizes that this fastener order needs the matching installation tool, which 80% of customers buy separately later—why not include it now?

Studies show that product recommendations drive 10-30% of e-commerce revenues on average. B2B is no different. In fact, with higher order values and more complex product relationships, the opportunity is even bigger.

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Y Meadows captures orders from email, pulls the right items from your product catalog, and automatically attaches the correct quote in your ERP.

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📋 Set Up Your AI Quote Assistant (One-Time Setup)

Instead of pasting your entire catalog for every quote, set up a Claude Project once with all your product and customer data. Then each quote takes 10 seconds.

Step 1: Create a "Quote Optimizer" Project in Claude

Go to claude.ai and create a new Project. Upload your product catalog and customer data files (see setup details below). This becomes your AI quote assistant's permanent knowledge base.

Step 2: Add Custom Instructions to Your Project

In the project's custom instructions, add:

You are an expert product recommendation assistant for [Your Company Name], a manufacturer/distributor serving [your key industries]. 

Your role: When given a customer quote request, analyze our complete product catalog and the customer's purchase history to recommend:

1. THE BEST PRIMARY PRODUCT MATCH
   - Specific SKU and name
   - Why this product fits better than alternatives
   - Key specs for their industry/application

2. THREE HIGH-VALUE COMPLEMENTARY PRODUCTS
   - SKU and specific pairing rationale
   - Problem solved or benefit provided
   - Reference co-purchase data if available

3. HIGHER-MARGIN ALTERNATIVES (when applicable)
   - Premium SKU with quantified benefits
   - ROI justification for upgrade

4. INDUSTRY-SPECIFIC CONSIDERATIONS
   - Specialized SKUs for their vertical
   - Compliance or certification advantages

5. READY-TO-USE QUOTE PARAGRAPH
   - 3-4 sentences explaining why these products fit their operation
   - Position complementary items as value-adds
   - Professional tone that shows we understand their business

Always reference customer purchase history to identify patterns. 

Prioritize in-stock items. Flag margin improvement opportunities.

Step 3: For Each Quote, Just Ask

Now when you need a quote, simply open your project and type:

Quote for [Customer Name]:
[Paste their request - could be an email, a part number, or a description]

Industry: [if not in system]
Urgency: [if relevant]

That's it. AI has all your product data, customer history, and margin information already. It gives you optimized recommendations in 30 seconds.

📊 Setting Up Your Project Knowledge (One-Time)

Here's what to upload to your "Quote Optimizer" project. Do this once, update monthly or quarterly.

File 1: Product Catalog (Critical)

Export from your ERP as CSV or Excel with these columns:

• SKU Number

• Product Name/Description

• Category & Subcategory

• Base Price

• Cost (optional but recommended) - enables margin optimization

• Current Inventory Quantity

• Key Specifications (material, size, capacity, etc.)

• Industry/Application Tags (food-grade, marine, pharmaceutical, etc.)

• Substitute SKUs (if tracked)

File 2: Customer Purchase History (Critical)

Export the last 12-24 months of order history:

• Customer Name

• Industry/Vertical

• Order Date

• SKUs Purchased (on each order)

• Quantities

• Order Totals

File 3: Frequently Bought Together Analysis (Optional but Powerful)

If your system tracks it, create a document showing:

• "SKU-123 purchased with SKU-456 in 68% of orders"

• "Customers who buy X typically also buy Y and Z"

• Common product bundles or kits

If you don't have this data, AI will learn patterns from the purchase history file.

File 4: Product Pairing Rules (Optional - Your Expertise)

Create a simple document with your team's product knowledge:

• "When quoting stainless fasteners, always offer thread-locking compound"

• "Food processing customers require food-grade lubricants (SKU-XXX, SKU-YYY)"

• "High-temp applications need ceramic insulators instead of standard"

• "Most installation tool orders need spare blades within 90 days"

This captures your reps' expertise so AI applies it consistently.

File 5: Customer Notes (Optional)

If you have account-specific information worth remembering:

• Special requirements (only ships to certain locations)

• Budget constraints

• Past issues or preferences

• Key contacts and their priorities

Keeping It Updated:

• Monthly: Update inventory levels if stock changes significantly affect recommendations

• Quarterly: Refresh customer purchase history to reflect recent orders

• As needed: Add new products, update pricing, note new product pairings you discover

Why This Works Better:

• ✅ One-time setup instead of pasting data every quote

• ✅ 10-second quotes instead of 5-minute prompts

• ✅ Consistent recommendations - same knowledge base for all reps

• ✅ Learning over time - add insights as you discover them

• ✅ Team access - multiple reps can use the same project

🎯 Real-World Impact

A mid-sized industrial distributor implemented AI-powered quote optimization for their inside sales team using a Claude Project setup.

The Setup: They had 3,800 SKUs but reps consistently quoted from a pool of about 150 familiar products. The rest of the catalog was essentially invisible to the sales team.

The Implementation: They created a "Quote Assistant" project and uploaded:

• Complete product catalog (3,800 SKUs with specs, pricing, and inventory)

• 24 months of customer purchase history

• A document of "sales wisdom" - known product pairings and application rules their veteran reps had learned over years

The Workflow: When a customer request came in, reps simply typed into the project: "Quote for [Customer Name]: [request details]". Within 30 seconds, they got:

• Best primary product match based on application and history

• Three complementary products with specific reasons why each made sense

• One premium alternative with ROI justification

• A paragraph to include in the quote explaining the recommendations

The Results After 90 Days:

• Average order value increased 14% (from $2,100 to $2,394 per quote)
• Complementary product attachment rate jumped from 12% to 38%
• Quote-to-close rate improved 8% because recommendations were more targeted
• Reps reported higher confidence in recommendations—they weren't guessing anymore
• Previously "invisible" products now represented 22% of quoted items
• Quote preparation time dropped 60% - from 12 minutes to under 5 minutes per quote

The sales manager said: "The project setup took our ops manager about two hours - just exporting data from our ERP and uploading it. Now every single quote gets the benefit of our entire product knowledge base. AI knows the catalog better than any human could. Our reps focus on understanding the customer, and AI tells them exactly what to quote."

Annual Impact: With ~2,400 quotes per year, the 14% increase in average order value delivered an additional $673,000 in revenue - from the exact same customer base, with zero additional marketing spend. Setup time: 2 hours. ROI: Immediate.

🚀 Stop Leaving Money in the Warehouse

You invested in that catalog for a reason. Every SKU represents an opportunity to serve customers better and grow revenue.

But if your reps only quote 4% of what you sell, you're not just missing revenue - you're training customers to see you as a limited supplier.

AI fixes this. Set it up once in a Claude Project, then every quote gets optimized recommendations in seconds.

Your catalog is an asset. Start using all of it.

❌ Your competitors aren’t typing orders anymore.

They’re matching SKUs, validating quotes, and sending order confirmations - automatically.

See the Automated Order Workflow →