TL;DR Summary:
Hammoq uses your item history, category patterns, and store-specific data to generate the smartest price narrative for every tag.
In resale, a price tag is never just a number.
That $14.99 tag? It’s communication.
It tells your customer:
💬 “This is a deal.”
💬 “This brand usually costs more.”
💬 “It’s in great condition and worth this price.”
The problem is—many resale stores apply that tag based on outdated charts or general rules of thumb.
Without context, a $14.99 tag on a blouse could feel:
- Too high in one location
- Too low for the brand
- Just right—but only by accident
Hammoq eliminates the guesswork.
It uses real historical sales data from your own store to determine the optimal price that communicates value and drives action.
🧠 Tags Are Micro-Messages. Let’s Make Them Work.
Every resale customer has this internal question when they pick up an item:
💭 “Is this worth the price?”
Hammoq answers that for you—before they even ask.
Using:
- 30, 90, and 365+ days of local sales data
- Sell-through patterns by brand, gender, and condition
- Average sale price (ASP) per category and season
- Local store-specific insights
Hammoq tags aren’t arbitrary—they’re persuasive. They say:
🏷️ “This price is backed by performance.”
🏷️ “It reflects what similar items sold for here.”
🏷️ “You can buy with confidence.”
✅ Key Takeaways:
- Let AI price with context and confidence
Don’t guess—build each price using performance trends and real outcomes. - Turn tags into mini-strategies
Every tag becomes a sales tool, not just a label. - Use deeper data than brand + condition alone
Hammoq blends timing, trend, velocity, and pricing logic from your store.
🔁 Before & After Snapshot:
Before Hammoq:
- Flat price charts for tops, jackets, and jeans
- Tags vary by team member’s best guess
- Some items overprice and stall
- Others underprice and fly—but lose margin
After Hammoq:
- Tags based on proven local sales performance
- Consistency across all staff
- Better buyer confidence
- Fewer markdowns, higher conversion
🚀 What’s Next (How To):
- Compare AI pricing to flat-rate categories
Start with a high-volume category like shoes or jackets - Analyze conversion rates post-Hammoq tagging
Measure how fast items sell at AI-generated prices vs. static ones - Monitor sell windows by item type
Track time-to-sale across Hammoq tags vs. manual pricing
