Menu matrix methodology
How MenuWright classifies every menu item on profitability and popularity using the Kasavana–Smith menu engineering method.
The menu matrix is MenuWright's core model. Every menu item is scored on two axes — popularity (menu mix) and profitability (contribution margin) — then placed in one of four quadrants. The method follows the classic Kasavana–Smith menu engineering approach (1982), the industry-standard way to classify items by what they earn and how often they sell.
The two axes
Popularity — menu mix percentage
menu mix % = item quantity sold / total quantity soldProfitability — contribution margin
contribution margin = menu price − food costThe thresholds
An item is "high" on an axis when it clears that axis's threshold:
| Axis | Threshold |
|---|---|
| Popularity | 1 / N × 0.70 — the average share an item would have if sales were perfectly even, discounted by the standard 70% adjustment. N is the number of active menu items. |
| Profitability | The weighted average contribution margin across all items, weighted by quantity sold. |
Values are rounded to cents (0.01). An item at or above the threshold counts as high.
The four quadrants
| High popularity | Low popularity | |
|---|---|---|
| High profit | Star — protect it: keep quality consistent, give it prime placement, don't discount. | Hidden Gem — promote it: better placement, rename, or a bundle. |
| Low profit | Crowd Pleaser — reprice it: raise price or trim portion cost. It sells well but earns thin margin. | Leftover — cut or reinvent it: low sales, low margin. |
These are MenuWright's current product labels. The classic literature calls the same quadrants Star, Plowhorse, Puzzle, and Dog; the product renamed them to be self-explanatory to restaurant owners.
Revenue-only fallback
When any item lacks food cost data, MenuWright falls back to a one-dimensional analysis using revenue contribution and sales volume. This still identifies bestsellers and underperformers, but it cannot place items in the full four-quadrant matrix — true contribution margins require food costs.
The moment every item has a food cost, the full profit-based matrix unlocks.
Analysis snapshots
Every analysis run writes an immutable MenuAnalysis snapshot with per-item results. Snapshots let you:
- Track how classifications change between periods — was that item always a Leftover, or did it use to be a Star?
- Compare trends month over month
What happens after classification
Classification is only the first half. After a run, MenuWright's AI layer reviews the matrix and produces specific, prioritized recommendations — reprice, promote, bundle, or cut — each with a projected monthly dollar impact where one can be estimated. Recommendations attach to the analysis snapshot and appear in the dashboard's Recommendations feed.
The AI generates candidates; you decide what to act on. When you accept a recommendation, MenuWright anchors a closed-loop measurement: it compares the item's average daily revenue in matched calendar windows before and after the action day and, once both windows have at least 7 days of recorded sales, displays the observed revenue change alongside the original projection. This is an honest before/after comparison, not an attributable impact — see the FAQ for the full explanation.