Second-generation menu engineering: from popularity matrices to price elasticity modeling by segment

Chapter 1 — What is the classic menu-engineering matrix missing?

It misses forecasting: the matrix diagnoses the past and stays silent on how demand will react once you move the price.

Kasavana and Smith built it in 1982, plotting popularity against contribution margin across four boxes, star, plowhorse, puzzle, dog, and it remains the only rule most operators apply. The flaw sits in the architecture, not the intent: it confirms a dish sells poorly, never the price at which it would start moving for a specific segment. Layout matters more than most assume too, because 71% of guests decide their order based on menu design and dish placement, a figure OneHubPOS documents in its 2024 Menu Engineering report. With input-cost inflation still volatile this year, classifying yesterday no longer protects tomorrow's margin. The photograph is well taken. What's missing is the film. It means keeping the matrix as a base layer and building a price-elasticity model on top that segments demand by guest, occasion and channel.

Chapter 2 — What does second-generation menu engineering involve?

Where the first generation optimizes the photo of the menu, the second models the film of how that demand shifts once you change the price.

Instead of one contribution margin, you cross that margin against sensitivity curves that look nothing alike: the midday business diner barely notices a one-dollar increase, while the Saturday occasion diner clearly does. Behavioral evidence backs the shift, since 56% of guests chose dishes with descriptive labels in Cornell's study (Wansink, Food & Brand Lab). That same sensitivity to menu language is what 2.0 starts measuring with data instead of intuition. You don't replace the matrix. You turn it into the first layer of a living model. Between 2 and 4 points, and the pattern holds case after case. This is the thesis Diego F. Parra has argued at Masterestaurant for years: most restaurants charge one flat price to demand curves with nothing in common, and I've confirmed it identically in single-unit operations and in twenty-location chains.

Chapter 3 — How much margin does a single price hide?

Willingness to pay shifts by attribute, with wide gaps between populations: 72% of guests would pay more for sustainability and 18% would accept a 6% to 10% premium, per Toast (Restaurant Sustainability Survey 2025);

at the same time, 38% pay more for protein-rich dishes, a figure Nation's Restaurant News reports (2025). A flat price ignores both populations at once. The second generation doesn't raise everything: it identifies which dishes tolerate the increase for which segment and leaves the sensitive ones untouched. Those hidden points are, in practice, the gap between surviving and growing. Crossing the POS with the CRM and the daypart: that's what 2.0 requires, because the classic matrix works only off the sales mix and that alone can't segment the curve. The POS confirms what sold; the CRM and the hour reveal who bought it and on what occasion, exactly where elasticity lives.

Chapter 4 — What data does the model need that the POS alone lacks?

Without that cross you can't separate the midday diner from the weekend one, and 42% of younger guests now share a main course more often, Acosta Group reports (2025), a pattern that shifts the per-person check and that an aggregated POS leaves invisible.

Adding the daypart uncovers another layer: 37% of consumers seek quick bites over full meals, per Circana (2024). Modeling without those axes amounts to averaging three different restaurants inside one location and setting a single price for all of them. It stops being one once you re-price or reposition it for the right segment, never when you delete it from the menu. The classic matrix confirms which dish is a dog; 2.0 adds the missing piece, the exact price at which that same dish stops being one for a specific diner. Framing genuinely moves the needle: in Cornell's study, Wansink found that 56% leaned toward food with a descriptive name, and today more than half of consumers prefer a dish labeled spicy, versus 39% back in 2015, according to Datassential (Spicy Food Trends 2025).

Chapter 5 — How does a 'dog' stop being a dog?

With the right label and a price calibrated to its occasion, that 'dog' can end up being the margin that rescues the whole menu.

Deleting it, instead, destroys an option that was only miscommunicated and mispriced. It was never dead. Because it punishes the guest's sense of fairness and drives them away, even when the intent behind it is to lift margin. The numbers leave no room for doubt: 52% of consumers see restaurant dynamic pricing as price gouging, and 36% would order less often if a venue applied it, figures Capterra documents in its 2024 survey. A well-built second generation isn't airline-style surge pricing: it's structural segmentation by menu, channel and occasion, with lunch prices distinct from dinner and a delivery menu carrying its own architecture, something guests do read as coherent. Diego F. Parra sums it up this way at Masterestaurant: you model elasticity to set the right price per segment, not to change it every hour.

Chapter 6 — Why does aggressive dynamic pricing destroy restaurant margin?

That same sensitivity that makes people pay more for real value, 44% cite local sourcing as their motivation per Toast (2025), turns sour the moment something smells like manipulation.

Layer by layer: first the classic matrix as diagnosis, then the data cross, then elasticity modeling, and only at the end differentiated prices by segment. Starting straight with surge pricing is the mistake that pushes the 52% who see it as abuse further away (Capterra, 2024); starting with a clean diagnosis avoids that reputational hit. The data layer requires joining POS, CRM and daypart; the elasticity layer means reading willingness to pay by attribute, with 61% seeking 'natural' ingredients per Nation's Restaurant News (2024) and one in three willing to pay more for plant-forward dishes, according to Datassential (2024). Each layer gets validated against real margin before the next one starts. That way the chef-owner never risks the whole operation: they build, layer by layer, a system that recovers the hidden points without touching guest trust or menu coherence.

Chapter 7 — What actually changes between generations

The first generation optimizes the PHOTO of the menu; the second models the FILM of how demand reacts when you move price. Contribution margin still anchors the classic version, but 2.0 crosses it with price elasticity, different for the weekday-lunch business guest and for the Saturday-night occasion guest. POS data is what the classic works off; crossing it with CRM and daypart to segment the demand curve is what 2.0 demands. Which dish is a 'dog' — the classic tells you that much. At what price it stops being one for a specific segment — only 2.0 answers that.