KPOT Korean BBQ: Dual-Framework Pricing Strategy

The Problem

KPOT is an all-you-can-eat Korean BBQ franchise with 100+ locations nationwide. Like most AYCE restaurants, it runs one flat price for all guests at all hours, leaving two obvious profit gaps unaddressed: empty seats during off-peak hours generating zero revenue, and peak-hour guests willing to pay more with no option to do so.

The engagement required building a financially validated pricing model that could quantify both gaps and recommend a combined strategy without adding new locations, new staff, or new fixed costs.

Methodology

Phase 1: Cost Structure Foundation. Decomposed unit economics using GEN Korean BBQ's SEC 10-K as the closest public financial proxy. Established a 55% variable cost rate for standard guests and 58% for premium guests (higher-cost proteins). Fixed cost baseline: $1,500/day. Validated a $14.32/guest variable cost floor, the minimum price at which any seat generates positive contribution margin.

Phase 2: F5 Incremental Cost Pricing (Off-Peak). Applied the F5 framework to off-peak periods (morning, mid-afternoon, late night), which run at 25 to 45 percent occupancy against a 70 to 85 percent peak. Modeled 15 to 20 percent discounts anchored above the $14.32 floor across three dayparts. Because fixed costs are sunk, every additional guest above that floor is pure margin. Result: +56 guests/day, +$364/day net profit improvement.

Phase 3: F4 Segmented Pricing and F2 EVC Validation (Peak). Built a two-tier pricing model for lunch and dinner peak hours:

• Standard: $22.99 lunch / $29.99 dinner
• Premium: $33.99 lunch / $44.99 dinner

Used F2 Economic Value to Customer (EVC) analysis to set the premium ceiling at $34.99 lunch and $45.49 dinner, ensuring prices leave $1.00 and $0.50 in customer surplus respectively, incentivizing upgrades without overreaching. Modeled 30 percent lunch and 40 percent dinner premium adoption as the base case. Separated cost pools correctly: standard revenue at 55 percent VC, premium revenue at 58 percent VC on the full premium bill. Result: +$599/day net profit improvement.

Phase 4: Validation. Ran 62 independent Python checks against raw inputs. All passed. Validated 364 operating days via KPOT's published holiday hour announcements, confirming zero closures on Thanksgiving, Christmas, and New Year's.

Results

+$351K annualized profit improvement per location. No new customers. No new fixed costs.

• F5 Day-Part Pricing: +$364/day, +$133K annualized
• F4/F2 Tiered Pricing: +$599/day, +$218K annualized
• Combined: +$963/day, +$351K annualized (over 364 operating days)

Sensitivity range: $175K conservative (10 percent premium adoption, 50 percent occupancy lift) to $526K optimistic. Contribution-margin positive at every scenario. The two frameworks operate in completely separate time windows, producing zero cannibalization.

Tools Used

Excel (financial modeling), Python and pandas (62-point validation), F2/F4/F5 Pricing Frameworks, GEN Korean BBQ 10-K (data proxy).

What I Would Do Differently

Run a willingness-to-pay survey at actual KPOT locations before committing to the 30/40 percent adoption assumption, which is the single input the model is most sensitive to. I would also add location-level sensitivity cuts, since a suburban Virginia location and a Manhattan location almost certainly have different elasticity profiles. The model is directionally sound; field data would make it operationally deployable.