TL;DR Summary:
Hammoq discovers what is succeeding in your highest-performing resale stores—and automatically applies that pricing model everywhere else.
- Your highest-performing store in resale is probably doing something right.
- Sells denim jackets in four days
- Sells branded shoes without marking down
- Extracts more value from each donation or trade-in
- Enjoys lowest price corrections and customer complaints
- The question is: can you duplicate that store's success—everywhere else?
Manually, it's nearly impossible.
Each store comes with their own staff, price instinct, and work pace. That means even with the same product, pricing results vary significantly across stores.
Enter Hammoq: your AI-powered pricing engine that can identify, extract, and replicate what succeeds at your top-performing stores—and apply that logic to your whole portfolio.
It's not smart pricing. It's smart scale-up.
How It Works: AI That Learns from Your Winners
Hammoq connects directly to your POS system and starts by identifying:
- Which store has the highest sell-through per category
- Where Average Sale Price (ASP) is highest per item type
- What pricing patterns result in faster turnover without markdowns
- Which price points generate the best margin and speed
Once it learns those signals, it can export that successful logic to other stores—automatically.
Let’s say:
- Store A sells Levi’s jeans best at $12.99
- Store B is holding them at $9.99
- Store C is pushing them too hard at $16.99 and has to resort to markdowns
Hammoq finds the sweet spot—and implements it across Stores B and C with no spreadsheets, meetings, or retraining.
✅ Key Takeaways:
- Learn from top store pricing success
- Discover what price, brand, and condition logic is performing best—and why.
- Replicate winners without spreadsheets
- No more sending price books via email. Hammoq scales for you.
- Scale with AI with no loss of detail
Each store gets updated but keeps what is most effective for that category, brand, or condition.
Why Manual Replication Fails
Having 5, 15, or 50 stores requires manual replication of your best-performing location's pricing logic to yield:
- Time spent researching each SKU
- Erroneous or inconsistent data
- "Close enough" estimates by employees instead of data-driven decisions
- Overcorrection—racking prices up too high or flattening across all stores
The payoff? Slower turns, smaller margin, and more markdowns.
Store A's success gets lost in translation by the time it reaches Store Z.
Hammoq remedies this with automated price transfer—optimized by store.
Real-Life Example: AI Copy-Paste Done Right
Before Hammoq:
- Store A sells Columbia jackets in 6 days at $19.99
- Store D retains same jackets for 24 days at $24.99
- Store G sells them at $12.99 and cuts margin by 40%
After Hammoq:
- AI identifies Store A's price + sell-through pattern
- Deploys similar pricing to Stores D & G
- Tunes modestly by store foot traffic and geography
Final result:
- Inventory turns 2x faster
- ASP improves in underperforming stores
- Markdown volume drops 30%
This is how localized wins turn into organizational growth.
- Who Benefits the Most?
- Multi-location thrift chains
- Vintage store networks with consistent brands
- Franchise resale models
- Liquidation centers trying to harmonize strategy across regions
- Retail operators managing pricing across 3+ stores
If you’ve ever said:
“I wish every store tagged like our flagship does…”
Hammoq turns that wish into a process.
It’s Not About Standardization—It’s About Smart Replication
Hammoq doesn’t flatten pricing across the board. It pulls the best logic and applies it dynamically, based on what’s proven to work.
You get:
- Higher-performing pricing logic, store by store
- AI-driven consistency, without removing local adaptability
- Better category planning, forecasting, and margin growth data
It's like having a clone of your best pricing analyst—and sending him everywhere.
What's Next (How To):
Use Hammoq to identify your leading store by category
Denim, outerwear, footwear, handbags, etc.
Get AI to extract pricing trends from those leading performers
Prioritize ASP, velocity, and condition-based price rules
Start cross-store replication
Roll out successful pricing rules across the remainder of your store network
Monitor lift in ASP, sell-through, and prevention of markdown
Monitor changes in 14–30 days and optimize in real-time
