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Set pricing rules that adjust discounts based on inventory and conversion, then let the system run during BFCM without manual override.
15 units
in stock and conversion drops below
2%
for two hours
10% discount
If stock falls below
5 units
return to full price
Willy Chavarria deployed an agentic commerce system for Black Friday Cyber Monday 2024 that made autonomous pricing and inventory decisions in real time, according to Modern Retail. The AI platform adjusted product availability and pricing during the peak shopping period without requiring manual approval from the brand's team.
The system monitored demand signals across Willy Chavarria's e-commerce site and made pricing adjustments based on inventory levels, conversion velocity, and competitor positioning. Instead of setting static discounts or manually reviewing SKU performance every few hours, the brand allowed the AI to rebalance pricing and product visibility continuously throughout the BFCM window. Modern Retail reported the approach helped the brand maximize revenue during the high-stakes selling period.
This worked because agentic commerce solves the core BFCM problem: pricing windows move faster than human decision cycles. A product that needs a deeper discount at 11 a.m. on Black Friday might need to return to full price by 3 p.m. if inventory drops below threshold. A manual merchandiser cannot monitor every SKU across that timeline. The AI watches conversion rate, stock depth, and margin floor simultaneously, then adjusts. The brand sets guardrails—minimum acceptable margin, maximum discount depth, priority SKUs—and the system operates within those boundaries.
For a small physical-product brand, the steal is to use rule-based dynamic pricing tools instead of full agentic AI. Start with Shopify's native discount automation or a lightweight app like Bold Dynamic Pricing. Set three rules before BFCM: if a SKU has more than 15 units in stock and conversion drops below 2% for two hours, apply a 10% discount. If stock falls below 5 units, return to full price. If margin on a SKU falls below 30%, lock pricing. Run these rules only on non-hero products—the mid-catalog items where manual attention is thin. Total setup cost is under $50/month for the app and two hours of rule configuration. You are not building an AI agent. You are automating the pricing decisions you would make if you had time to watch every product every hour.
During BFCM, do not override the rules unless something breaks. Let the system run. Check margin and revenue once at end of day, adjust thresholds if needed, then let it run again. The goal is to recover 5-8% more revenue from products that would have sat at full price or been manually discounted too late. Track results by comparing same SKUs year-over-year: did the automated SKUs convert faster or hold margin better than last year's manual approach. If the answer is yes, expand the ruleset to more products next quarter.
The broader pattern is that peak-season commerce is moving from manual campaign planning to continuous automated optimization. Brands that can delegate pricing and inventory decisions to systems—whether full AI or simple rules—gain speed. Speed during a 96-hour revenue window is worth more than perfect planning.
Use Shopify discount automation with three inventory-based rules on mid-catalog SKUs to recover revenue without watching every product.
For the operator
Deploy rule-based dynamic pricing across non-hero catalog before BFCM, track margin and conversion lift, expand ruleset if results beat manual benchmarks.
For the house
Require vendors to provide real-time inventory APIs so your procurement system can adjust reorder timing based on actual sell-through during peak windows.
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