TL;DR Summary:
Hammoq uses your store’s unique history to build pricing logic—no generic charts, no one-size-fits-all.
Longer Description:
If you run more than one resale location, you already know:
📍 Store A is not Store B.
They don’t sell the same inventory the same way—even if the shelves look identical.
- One location moves jackets at $19.99 in 2 days
- Another struggles to move the same brand at $12.99
- One has fast turnover on shoes—another has shoes piling up
That’s why “one-size-fits-all” pricing never really fits.
Yet many resale retailers still rely on:
- Static pricing templates
- Flat pricing charts
- Franchise-wide pricing guidelines
- Manual adjustments that vary by staff member
These systems may be efficient, but they aren’t accurate. And that’s where margin and velocity start to slip.
Hammoq was built to fix that.
🧠 Resale Pricing Is Local—Your AI Should Be Too
Most pricing systems rely on:
- Market trends
- National resale averages
- General rules like $4.99 tops, $9.99 jackets
But resale isn’t like traditional retail. Your performance is hyper-local. It’s impacted by:
- Neighborhood income levels
- Donated item mix
- Store layout and merchandising
- Seasonal inventory cycles
- Staff pricing practices
What works at one location might stall at another.
That’s why Hammoq uses your store’s own sales history—not someone else’s.
It builds an AI pricing model trained only on your floor:
- 📊 Analyzes your past 30, 90, and 365+ days of POS data
- 📍 Reviews ASP (Average Sale Price) and sell-through rate per category
- 🧠 Adjusts pricing recommendations to match your real-world performance
No external assumptions. No national averages. Just data that knows how your store actually sells.
🔁 Real-World Example: Same Jacket, Different Stores
Let’s say you operate 3 stores:
- Store A: Patagonia jackets sell best at $24.99 within 3 days
- Store B: Same jacket lingers until it hits $16.99
- Store C: No traction above $14.99 due to local demand
If you use a generic rule like “All Patagonia jackets = $19.99,”
you’re either:
- Underselling in Store A
- Overpricing in Store C
- Slowing down inventory turnover in Store B
Hammoq lets you set the right price for each location—without spreadsheets or second-guessing.
✅ Key Takeaways:
- Use your store’s actual data, not guesswork
Hammoq reads your store’s full pricing and sales history. - Pricing reflects store-specific patterns
No generic price tiers or “best practices” that don’t apply. - Works for thrift chains, consignment stores, and liquidation centers
Any resale operation with local variance will benefit from location-aware pricing logic.
📉 The Cost of Generic Pricing Models
Relying on flat or franchise-wide pricing leads to:
- 📦 Overloaded inventory in slower stores
- 💸 Missed margin in fast stores with higher customer value
- 🧍 Staff guessing instead of following data
- 📉 Lost revenue opportunities across multiple locations
Even small pricing mismatches—$3 to $5 per item—add up quickly:
- If just 25% of your items are under or over-priced
- Across 500 items a week = 125 suboptimal prices
- At $4 average margin loss = $500/week = $26,000/year, per store
With Hammoq, you recover that margin with intelligent automation—and no additional staff time.
🔁 Before & After Snapshot
Before Hammoq:
- Every store uses the same pricing chart
- Staff makes adjustments based on instinct
- Price tags vary for identical items
- No clear understanding of what works best
After Hammoq:
- Pricing based on 365+ days of actual sales
- Store-specific price logic per brand, category, and condition
- Tags reflect performance data, not policy
- Higher ASP, faster sell-through, fewer markdowns
🛍️ Who This Is Built For:
Hammoq’s custom AI pricing engine is built for:
- 🧥 Multi-location thrift stores and nonprofits
- 👟 Consignment chains that want to keep control while scaling
- 📦 Liquidation warehouses managing local pricing velocity
- 🧾 Franchise resale models that want consistency and flexibility
- 🧠 Data-driven operators ready to stop guessing and start optimizing
If you want every store to price like its own analyst—but without more headcount—Hammoq gets you there.
🚀 What’s Next (How To):
- Load past 365+ days of sales into Hammoq
The more data, the smarter the AI model becomes - Let AI build a store-specific pricing model
Trained on each location’s performance—not just brand or category - Review and compare pricing logic by store
See how Hammoq prices the same item differently across regions—based on real-world data - Roll out AI-powered tagging across your intake workflow
Test by department or location—monitor ASP and sell-through improvement
