TL;DR Summary:

Hammoq acts as an in-house pricing analyst—computing sell-through and ASP at scale through all your channels for resale.

Large resale businesses typically maintain payroll pricing analysts to squeeze out the most inventory value throughout the stores. Their job? Review what sold, what sat on the shelves, and what it should be priced at when it arrives on the floor again.

It's a vital task—but one that is:

⏳ Time-consuming

• Subjective (based on individual judgment)
• Hard to replicate across 5, 10, or 100 stores

Which means if your best analyst is out sick—or handling 50+ categories—your pricing plan comes to a screeching halt or falls apart entirely.

Hammoq solves this.

Think of Hammoq as a digital pricing analyst that operates 24/7. It:

• Pulls past sales from your POS
• Analyzes sell-through windows and ASP by store
• Recommends the optimal price for every item at intake
• Tunes based on seasonality, demand shifts, and performance

And most of all—it accomplishes this in seconds, everywhere, without the need for extra headcount.

Hammoq's AI-Powered Pricing Analyst in Action

If your analyst manually prices:

Denim jackets at $14.99 based on avg. demand

Premium sneakers at $24.99, projecting demand is up

Coats at $9.99–$29.99, varying by brand and season

But that logic exists in their head or a spreadsheet—and hours to implement at scale.

With Hammoq:

• AI extracts 30, 90, or 365 days of store-specific sales
• It calculates ASP + sell-through rate by brand, category, and condition
• It suggests the ideal intake price for every item—just when it's received
• Your human analyst can review, approve, or modify logic at any time

Result?

Pricing is now automated, scalable, and tied to your actual store performance—not guesses.

✅ Key Takeaways:

Remove bottlenecks from manual price review

Free your analysts from SKU-by-SKU guesswork—they focus on strategy, not spreadsheets.

AI reveals price recommendations store by store

Each store gets its own recommendations based on its unique ASP and sell-through.

Analysts are still in control, but not bogged down in grunt work

Price logic is still open to edit—Hammoq just does the heavy lifting.

The Cost of Manual Price Analysis

In big resale companies, this is how pricing still sits in most stores:

Analysts sort through legacy sales reports

They cross-reference levels of brand, levels of condition, and seasonal fashion

Pricing is posted store-by-store—or not at all

This leads to:

• Too much time to get products priced and on the floor
• Price inconsistency between stores
• Margins lost on high-ASP items that are underpriced
• Overstock when prices are too high and don't move

Your analysts are great—but they're overwhelmed with repetitive ones. Hammoq frees them up with a scalable pricing co-pilot.

• Strategic Advantage: Analysts Become Decision-Makers
• With Hammoq, you turn your analyst function from tactical to strategic.

Now they can:

• Focus on high-level price trends by region or season
• Review AI recommendations, flag outliers, and try new logic
• Drive long-term margin and pricing strategy—without having to do the grunt work

This isn't about replacing analysts—it's about making them more effective throughout your organization.

Who This Is Designed For

Hammoq's AI-driven pricing engine works for:

• Big donation intake thrift shops
• Multi-unit consignment chains
• Bin stores retailing high volume by category
• Any resale operation with internal analysts and regional managers

Regardless of how many analysts you have, two to twenty, Hammoq can price the front 80%—leaving your staff to perfect the last 20%.

Real-World ROI

Shoppers using Hammoq's AI for price analysis report:

⏳ 60–75% less time spent setting prices

• 20–30% ASP increase on top-selling items
• 30–40% faster sell-through on AI-labeled inventory
• Steep reduction in markdown frequency due to precise initial prices

Your best products are no longer under-tagged. Your worst inventory doesn't linger.

Everything is priced to perform—backed by your data.

What's Next (How To):

Connect your POS system to Hammoq

Import 30–365 days of historical sales per store

See Hammoq's initial price suggestions by category

Have your analysts compare against current manual pricing logic

Enhance rules and thresholds by brand, season, or store type

Build rules once—and have the AI apply them at scale

Tag 100–200 items with AI + Analyst Collaboration

Compare margin, turnover, and markdowns against business-as-usual

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