Menu engineering: traditional method vs the Masterestaurant method: impact and employment

When the classic matrix falls short?

The Kasavana-Smith matrix falls short the day your per-portion costing turns ninety days old, and the tell is a STAR dish that no longer leaves cash behind.

With full-service menu inflation hitting 9.0% year over year in 2022 (National Restaurant Association / Restaurant Business) and limited service peaking at 8.2% in April 2023 (National Restaurant Association / BLS), a core input can climb twenty points between one costing and the next while the menu stays blissfully unaware. You defend that dish in the board meeting, you park it on the first line of the card, your server pushes it, and every plate that leaves the pass costs you money. The matrix did not lie: the data feeding it had been dead for three months and nobody signed the certificate.

Option 1: Kasavana-Smith with disciplined monthly recosting

Recosting every month with the same old matrix solves about 70% of the problem and costs less than anyone assumes. The profile is clear: chef-owner of a single location, somewhere between 40 and 90 menu references, a spreadsheet that already exists, and a supplier whose price list arrives over WhatsApp. Switching costs you four measured hours a month, plus the discipline not to skip December, which is precisely when prices move most. The downside carries weight: you are still classifying against a sales mix you tally by hand, and counting error on menus above sixty items piles up fast. It works for stable operations. It stops working the day you open a second location and both kitchens start buying differently.

Option 2: a three-axis matrix that prices in volatility

Adding a third axis — how volatile the core input has been over the last ninety days — turns the matrix into something that anticipates instead of describing. A dish with high margin and high popularity whose main input swings more than 15% per quarter is not a star; it is a star under watch, and it deserves a backup recipe written before you need it. The profile shifts here: an owner with two or more locations, or a chef whose menu leans on one protein, in a market where chicken already absorbs 37% of quick-service food spending in the United States per Nation's Restaurant News, and that concentration repeats across plenty of Latin American menus. Effort goes up: you log purchase prices for twelve critical inputs weekly. Twelve, not forty. That cut is what keeps the method alive.

Option 3: pricing psychology run as an experiment

Anchoring, decoys and dropping the currency symbol all work, but they work differently in Bogotá than in Santo Domingo, and the only serious way to know is measuring it with the same menu across twenty-one-day windows. Traditional practice treats that as designer's art; at Masterestaurant we treat it as an experiment with a baseline, and Diego F. Parra keeps hammering a detail almost nobody respects: change ONE variable per window, never three at once, because if you move price, position and description the same Monday, the result teaches you nothing. The profile is an operator with enough volume for twenty-one days to yield a readable sample, say 1,500 tickets per window. Below that, noise eats the signal and you will celebrate a coincidence. Honest downside: six weeks minimum for two clean comparisons.

Option 4: rebuild the menu around the glass

Sometimes the lever sits in the glass, not the plate. Alcoholic beverages account for roughly 21% of total sales at full-service restaurants according to the National Restaurant Association, and that share moves on menu decisions almost nobody subjects to menu engineering. Cold coffee is the case that interests me most: cold brew went from under 1% menu penetration in the United States in 2014 to 7.7% in 2024 (Datassential), and 34% of specialty iced coffee drinkers are Gen Z against 30% millennials (Tastewise). For a chef-owner whose kitchen is jammed at peak, shifting margin into beverage demands neither an extra cook nor another square meter. Against it: your service team needs suggestion training, and staff turnover works against you every single quarter.

Option 5: engineer by segment, not by whole menu

Once your menu passes eighty references, stop running the matrix over the total and run it by product family, because decisions that come from blending starters with desserts are average decisions and the average cooks nothing. Penetration data by segment shows exactly why: plant-based offerings reach 64.7% of fast-casual menus, drop to 41.8% in quick service and land at 31.6% in fine dining (Plant Based Foods Association / Datassential, 2024). That spread proves one family's behavior predicts nothing about another inside the same building. The profile here is the operator with a large menu and several distinct dayparts. Switching cost: reorganizing the recipe master by family, one weekend of dirty work, after which the matrix runs the same way but finally tells you something you can act on.

Why the instrument matters beyond the kitchen?

SATE Institute reads this lag as credit risk rather than culinary management, and that reading changes what is at stake.

An operator blind to marginal profitability per dish cannot project cash flow either, and an SME portfolio built on projections without operating support decays at the first price shock. A restaurant closing does not cost one business: it costs between 6 and 14 formal positions that rarely return to the same neighborhood. That is why the technology instrument contributed by Masterestaurant S.A.S. as an ally of the model stops being restaurant software and becomes scoring infrastructure.

When NOT to switch methods?

Keep the matrix you already run if you operate one location with fewer than thirty references, input prices negotiated on fixed terms, and a sales mix your POS already reads without anyone transcribing a thing.

There is a tension worth settling head-on: more measurement does not mean better cooking, and I have watched operations replace the chef's judgment with a dashboard and lose the two dishes people were coming for. My rule for deciding is plain: if your most volatile input moved less than 8% last quarter and your real food cost lives under 32% without portion tricks, switching methods will cost you more attention than it returns in margin. Recost, close the notebook and get back in the kitchen.

Where the two paths genuinely diverge?

The divergence is not in the matrix. It sits in the age of the data feeding it.

Kasavana-Smith with a ninety-day-old costing will label a dish a STAR even though its main input rose 22% in that span, and the operator proudly defends a plate that loses money every time it leaves the kitchen. Pricing psychology is treated as craft by the traditional method and as a measurable experiment by the Masterestaurant method. Anchoring, decoys, dropping the currency symbol — all of it works, but it works differently in Bogotá than in Santo Domingo, and the only way to know is to compare the sales mix before and after with the same menu and a twenty-one-day window.

Where the two paths genuinely diverge — in practice?

One tension deserves resolving head-on: more measurement does not mean better cooking. Technically flawless menus can be gastronomically dead, every dish clearing the marginal-profitability filter and not one of them giving a reason to come back.

Cost per portion decides what CAN go on the menu; the chef's judgment decides what SHOULD. Invert that order and you build restaurants that are profitable for fourteen months and empty in the fifteenth. For a multilateral lender the split is sharper still: the manual method yields a diagnosis, the instrumented one yields a series. A diagnosis persuades an owner; a series lets you compute default probability across a portfolio of 400 restaurants, and that changes the price of credit the whole sector pays.