AI lifts sari-sari store sales by 46%: what sales teams can copy from Packworks' findings
AI has moved from buzzword to back-room tool for neighborhood retail. In Packworks.io's latest analysis, sari-sari stores that applied AI recommendations saw a 46% increase in daily GMV and a 17% rise in total sales over a two-week period.
Here's the kicker: these stores earned more while operating on 20% fewer selling days (five down to four). That points to cleaner inventory decisions, tighter demand planning, and a product mix that actually moves.
"We've recorded increased sales and better operational efficiency as stores use the AI-driven insights they access through SIP," said Packworks Chief Data Officer Andoy Montiel, referring to the company's Store Insighting Project (SIP) document that turns each store's transaction history into practical recommendations.
Key results at a glance
- 300+ stores analyzed post-September 2025 data collection.
- +46% daily GMV after engaging with AI insights via SIP.
- +17% total sales across the same two-week period.
- Higher revenue with 20% fewer active selling days.
- Sales lift driven by underperforming SKUs flagged by the tool-owners knew what to push, clear, or restock to keep operations efficient.
Why it worked
- Assortment tuning: AI highlighted "sleepers" and deadweight. Stores pushed the right slow movers, trimmed true laggards, and doubled down on proven items.
- Demand planning: Recommendations timed reorders and suggested depth by SKU, reducing stockouts and idle capital.
- Focused effort: With fewer selling days, stores concentrated on high-probability sales windows and SKUs that convert.
How Packworks enabled the lift
Insights flowed through the Store Insighting Project (SIP)-a personalized, AI-backed report that translates transactions into next steps for pricing, assortment, and replenishment. Packworks developed its precision marketing tool with support from the DOST-PCIEERD Startup Grant Fund (2024), tapped ST Telemedia Global Data Centres (Philippines) for large-scale model runs, and partnered with Ateneo's BUILD for data warehousing and BI.
What sales leaders can do now
- Audit your long tail: Identify SKUs with low sell-through but strategic value (traffic drivers, bundle anchors, seasonal upside). Classify: push, fix, or clear.
- Run weekly microtests: Trial price points, facings, and shelf positions on 5-10 SKUs. Keep what moves the needle, drop what doesn't-fast.
- Shift effort to peak windows: Concentrate activations and field visits on the days and hours that produce outsized sales.
- Tune promos to intent: Use precision offers to move flagged underperformers instead of blanket discounts.
- Close the loop: Feed results back into your model or rules engine so recommendations sharpen each week.
- Equip store owners: Share simple, one-page playbooks: which SKUs to restock, what to feature, and when to reorder.
Metrics to track weekly
- GMV per active day vs. total GMV (prove focus beats hours).
- Sell-through of previously underperforming SKUs.
- Stockout rate and days of inventory by top SKUs.
- Gross margin by SKU and category after changes.
- Promo ROI (incremental sales minus discount cost).
Bigger picture for the PH retail channel
This push to bring practical AI into micro retail supports the Philippine Development Plan 2023-2028 focus on digital transformation. For sales teams, the lesson is clear: precise recommendations, tight feedback loops, and weekly iteration beat broad, one-size-fits-all programs.
Upskill your sales org
If you're building AI skills across field, trade marketing, or category teams, start with structured learning paths and hands-on tools. Explore options here: AI courses by job.
Bottom line: the stores that won didn't work longer-they worked smarter. Put your data to work, test small every week, and keep only what drives sell-through and margin.
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