TL;DR Summary:
Hours are spent poring over pricing data by resale store owners—Hammoq replaces that human judgment with AI that recommends the ideal price from 30, 90, 365+ days of real POS data.
With a one-store consignment store, a regional vintage shop, or a multi-store thrift chain, one fact is true in all cases: pricing is time-consuming.
In most second-hand stores, pricing isn't guesswork—homework. You or somebody on staff most likely spends some hours a week doing things like:
Running vintage sell-through by category
Verifying comparable listings online
Investigating average sale price (ASP) by condition
Keeping an eye on markdown history in order to identify patterns
Doing price changes manually by location
Some stores even have analysts simply to optimize prices by category.
Here's the question:
Why are you still doing it all manually?
All of that goes automated with Hammoq.
No spreadsheets. No guessing. No back and forth between inventory and analytics. Just data-driven pricing at scale—done in seconds.
It Works Like This: Hammoq as Your AI Pricing Analyst
Hammoq plugs into your Point-of-Sale system and pulls in data from:
- 30, 90, or 365+ days of transactions
- Sell-throughs by brand, category, and store
- Average Sale Price (ASP) over time and by condition
- Store-specific behavior (what sells fast in urban vs. suburban stores)
- Pricing adjustments over time and their impact on sell-through
Using this historical sales data, Hammoq automatically suggests a starting price for every item at intake—optimized for revenue and velocity.
No more endless analysis.
Just scan the item → AI reviews your real data → A tag prints with the right price.
Example: Women’s Levi’s Jeans
Let's say you typically sell women's Levi's jeans for $6.99 to $12.99 depending on condition. But each of your stores is pricing them a little differently.
Store A overprices them → They sit on shelves for 35+ days
Store B underprices them → They sell fast but hurt margin
With Hammoq:
AI reviews 90 days of history
Recommends $9.99 in Store A for faster turnover
Implying $12.99 in Store B using historical demand and margin protection
→ Each store labels the same product based on what works for that store
This is not one-size-fits-all pricing rules.
It's real retail math, powered by AI, calculated in seconds.
✅ Key Takeaways:
Replace human price research with AI
No more spreadsheets, guessing, or analyst bottlenecks—Hammoq does the heavy lifting.
Leverage 30, 90, or 365+ days of sales history
Choose the right pricing model for seasonal, evergreen, or long-tail inventory.
Protect margin and improve sell-through
Price smart from the start—avoid markdown cycles and slow-moving inventory.
The Real Cost of Manual Pricing Analysis
Most store operators underestimate how expensive manual pricing is. You’re paying for:
- Time spent reviewing past sales
- Employees “guesstimating” prices
- Overpriced inventory that sits unsold
- Underpriced inventory that flies off the floor, sacrificing margin
- Unlimited markdowns, consuming labor and shelf space
Let's say your price analyst is paid $25/hr and takes 8 hrs/wk keeping track of sales history and updating pricing guidelines.
That's $800–$1,000/month—before even considering the cost of inaccuracy.
With Hammoq, that logic is baked into each tag—driven by actual store performance.
Why Data-Driven Pricing Prevails
Pricing is not a one-time thing. It's a recurring opportunity to optimize. Hammoq gives you the power
- Price faster through the inventory by pricing for performance
- Raise ASP where demand is present
- Avoid overpricing dead stock that requires markdown
- Project margin performance from historical performance standards
- Fine tune store by store, category by category, pricing logic
Your best-selling categories now drive future sourcing, floor allocation, and tagging strategy automatically.
Who Is This For?
Hammoq's AI price engine is suited for:
- Thrift stores with high daily intake
- Vintage retailers pricing by style, decade, or brand
- Multi-location resale chains that need store-specific pricing logic
- Consignment shops balancing client payout with store margin
- Liquidators trying to move return pallets efficiently while preserving value
If you’re touching used inventory, AI pricing changes the game.
What’s Next (How To):
Identify your top-selling categories
Start with denim, outerwear, footwear—high volume, high value.
Connect Hammoq to your POS
Pull 30, 90, or 365+ days of category and location-specific sales history.
Compare a 7-day A/B test with live pricing versus Hammoq pricing printed onto tags.
Measure results
Analyze sell-through, ASP, and margin achievement on AI-priced items.
