AI tool lifts sari-sari store sales by 46%-even with fewer operating days
Micro-retail moves fast. Packworks' latest study shows just how much smarter it can get when decisions are guided by store-level data.
In a two-week sample of 300+ sari-sari stores, those using AI-generated recommendations increased daily gross merchandise value by 46% and total sales by 17%. The kicker: these gains happened even while stores operated 20% fewer days.
According to Packworks, this points to better inventory control, sharper product mix, and demand planning that aligns with real buying patterns. In short, more productive selling hours.
What the tool does
The Store Insighting Project (SIP) analyzes transaction data and flags where money is made-or lost. It tells owners which SKUs are underperforming, which ones to prioritize, and when to restock so shelves aren't tied up with slow movers.
"As stores learn to leverage the recommendations from the AI-driven insights they can access through SIP, microretailers can make smarter decisions that translate into higher sales and more efficient operations," says Packworks chief data officer Andoy Montiel.
Why this matters to sales teams
For field sales, trade marketing, and channel leaders, this is proof that cleaner assortments and tight replenishment windows lift revenue without adding more open days. Less noise, more turns.
When store owners act on specific SKU and timing cues, your sell-in becomes easier, promos get placement, and stockouts shrink. The result is higher GMV per open day and better visibility into what actually drives conversion at the barangay level.
- Prioritize winners: Push fast-moving SKUs by neighborhood and cut the long tail that stalls cash flow.
- Tune replenishment: Align restock timing with known demand spikes (paydays, school hours, weekend traffic).
- Fix underperformance: Swap or bundle slow items; test micro-promos where SIP flags weak velocity.
- Track the right metric: Manage to GMV per open day, not just total volume.
- Build a cadence: Review SIP insights weekly with store owners; set clear actions and follow-ups.
Built with public-private support
The SIP tool was developed under the startup grant fund program of the Department of Science and Technology and the Philippine Council for Industry, Energy and Emerging Technology Research and Development, with partnerships that include ST Telemedia Global Data Centers for large-scale model work and Ateneo's Business Insights Laboratory for Development on data warehousing and BI.
Learn more about DOST-PCIEERD.
"Our mission at Packworks is to close the gap by making AI practical, accessible, and useful for the smallest retailers," says co-founder Hubert Yap.
What to do next
Set up a pilot with a defined store cluster. Pick clear KPIs: GMV per open day, stockout rate, SKU rationalization, and reorder accuracy. Agree on a weekly review, execute two to three actions per store, and measure deltas.
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