Stars & Dogs: Use the Matrix, Then Stop

What Classic Menu Engineering Still Does Well

In the first chapter, I deliberately challenged the way the four-box model is sometimes used. Now I want to do the opposite. I want to defend it. Traditional menu engineering remains useful because it forces us to look at two basic commercial questions that every restaurant should understand:

What are guests choosing?

And:

What does each sale contribute?

Those two questions give us a fast way to screen a menu that may contain dozens of different products. The problem is not the matrix. The problem is asking the matrix to make a decision it was never designed to make. So before we follow an item through TRAIL, I want to make sure we understand the classical screen properly.

Start by comparing the right items

Imagine that I give you one list containing:

• a Club Sandwich
• a glass of wine
• a cheesecake
• a cocktail
• a grilled chicken main course

and a coffee.

Then I ask:

“Which one is popular?” You can calculate which item sold the most, but the comparison may not tell you very much. A guest ordering a main course has a different purchase opportunity from a guest deciding whether to order dessert. A beverage may be purchased with the meal rather than instead of it.

Lunch and dinner may behave differently. Room service may behave differently from the restaurant. For that reason, the training workbook keeps the classical screen within controlled categories. Mains are compared with Mains.

Desserts with Desserts. Beverages with Beverages. We will go deeper into the analysis population in the next chapter.

For now, remember one simple rule:

Compare items that had a reasonably similar opportunity to be purchased.

Otherwise, your calculation may be mathematically correct while the comparison itself is weak.

Build the screen from a meaningful analysis population.

The first question: what are guests choosing?

The first axis of classical menu engineering is popularity.

In practical terms, I want to know how much of the relevant menu mix each item represents. Let us use the four Main Course items from the TRAIL training example.

During the current period, the sales are:

These are synthetic training figures from the companion workbook, not property results. Now I can calculate the menu mix.

For the Club Sandwich:

260 ÷ 780 = 33.3%

For Grilled Chicken:

300 ÷ 780 = 38.5%

For the Local Seafood Plate:

100 ÷ 780 = 12.8%

And for the Vegan Curry:

120 ÷ 780 = 15.4%

Immediately, I can see something useful. The Club Sandwich and Grilled Chicken account for much more of the Main Course mix than Seafood or Vegan Curry. That does not yet tell me whether any of these items are good or bad. It simply tells me what guests are choosing relatively often within this population.

Popularity is a screen within the chosen category, not a universal judgement.

We still need a popularity benchmark

If I only know that an item represents 15% of category sales, I still need some basis for deciding whether that is relatively high or low.

The workbook uses a conventional popularity factor of 70% of equal-share popularity as an editable screening default. It is deliberately labelled as a conventional default rather than a universal rule.

Let me show you the calculation. There are four Main Course items.

If every item sold equally, each would represent:

100% ÷ 4 = 25%

The workbook then applies the 70% popularity factor:

25% × 70% = 17.5%

So for this training example, the popularity threshold is:

17.5%

That means:

Club Sandwich at 33.3% is above the threshold. Grilled Chicken at 38.5% is above it. Local Seafood Plate at 12.8% is below it. Vegan Curry at 15.4% is below it.

We have now divided the category into relatively higher-popularity and lower-popularity items. But I want to put a warning beside that calculation immediately.

Do not turn 70% into a law

Please do not take the 70% factor from this example and treat it as a universal hotel or restaurant standard. The workbook makes the factor editable for a reason. A category with four permanent Main Courses is different from a long cocktail list. A seasonal menu is different from an all-day menu.

A buffet decision environment is different from an à la carte menu. And even within the same outlet, the correct analysis population may change depending on the management question.

So the threshold is useful as a screening device.

It is not a substitute for judgement. Later, when we reach Trend & Target, we will ask whether the resulting classification is even representative enough to deserve confidence. For now, the popularity calculation gives me one axis of the matrix. I still need the second.

The second question: what does each sale contribute?

The second axis is contribution.

For the classical SCREEN used in this guide, I calculate what the workbook calls:

Classic Contribution Margin per unit.

The calculation is deliberately straightforward:

Net revenue per item − recipe or product cost = Classic Contribution Margin

Notice that I said net revenue, not automatically the printed menu price.

That distinction matters.

Suppose the Club Sandwich is listed at $19.00.

But the average discount or allowance attached to the item is $0.30.

The economic selling value used in the training workbook is therefore:

$19.00 − $0.30 = $18.70

The current recipe or product cost is:

$6.80

So the Classic Contribution Margin is:

$18.70 − $6.80 = $11.90

The workbook uses the same approach for the other Main Courses.

Now we have something different from popularity. The Local Seafood Plate sells relatively few units, but it produces the highest Classic Contribution Margin per sale in this group. The Club Sandwich sells much more frequently, but its contribution per sale is lower. Already, the menu is starting to tell us a more interesting story.

Classic Contribution Margin keeps the first screen simple and recognisable.

Now we need a contribution benchmark

Just as popularity needs a benchmark, contribution needs one too.

In the TRAIL training workbook, the four Main Courses are compared with the category’s weighted-average Classic Contribution Margin.

For this example, that benchmark is approximately:

$14.18 per item sold

So:

Club Sandwich at $11.90 is below the benchmark. Grilled Chicken at $15.30 is above it. Local Seafood Plate at $19.00 is above it. Vegan Curry at $12.30 is below it.

Now we have both axes.

Popularity:

above or below 17.5%

Contribution:

above or below $14.18

And now the four-box model becomes very useful.

One category. Four different patterns.

Look at what happens when we place the four Main Courses into the matrix.

Grilled Chicken — STAR

Grilled Chicken has:

38.5% menu mix

and

$15.30 Classic Contribution Margin

It is above both benchmarks.

So it becomes a:

STAR

In classical terms, this is a relatively popular item with relatively strong contribution. That is useful information. But remember Chapter 1.

It does not yet mean:

“Promote it harder.”

Club Sandwich — PLOWHORSE

Club Sandwich has:

33.3% menu mix

and

$11.90 Classic Contribution Margin

Popularity is above the benchmark. Contribution is below it.

So it becomes a:

PLOWHORSE

Guests clearly want the product. The economics per sale are relatively weaker than the category benchmark. That deserves management attention. It does not yet tell us whether the answer is price, portion, recipe, discounting or something else.

Local Seafood Plate — PUZZLE

The Local Seafood Plate has:

12.8% menu mix

and

$19.00 Classic Contribution Margin

Popularity is below the threshold. Contribution is well above the benchmark.

So it becomes a:

PUZZLE

Each sale looks economically attractive under the classical calculation. But relatively fewer guests are choosing it. That gives me a very useful question. Why?

Vegan Curry — DOG

The Vegan Curry has:

15.4% menu mix

and

$12.30 Classic Contribution Margin

It is below both benchmarks.

So it becomes a:

DOG

Again, the classification is useful. It tells me that the item is relatively weak on both classical dimensions.

What it does not yet tell me is whether the right answer is removal.

We will eventually discover that this particular item has an important dietary and assortment role in the synthetic menu example. But the classical matrix does not know that yet. And that is exactly the point.

One category can contain four very different commercial patterns.

This is what the matrix does very well

Look at what we have achieved with a fairly simple calculation. We started with four Main Courses. Within a few minutes, we can see four different commercial patterns:

Grilled Chicken: strong popularity, strong contribution.

Club Sandwich: strong popularity, weaker contribution.

Local Seafood Plate: weaker popularity, strong contribution.

Vegan Curry: weaker popularity, weaker contribution.

That is a meaningful management screen. If the restaurant had thirty items, this kind of classification could help us quickly identify where management attention is likely to be most useful. This is why I do not want to throw away classic menu engineering. It reduces complexity.

It makes patterns visible. And it gives different departments a common language. A Chef, Restaurant Manager, Finance Manager and GM can all look at the same matrix and quickly see where the commercial tension sits.

The workbook therefore describes the matrix explicitly as an orientation screen. It is designed to tell management where to investigate rather than automatically decide the item.

Translate the classification into a question

This is the point where I want you to develop a new habit.

Every time you see a quadrant, convert it into a question.

Not an instruction.

For a Star, ask:

Why is this item working, and what must I protect before I try to improve or increase it?

For a Plowhorse, ask:

Why is an item that guests clearly want producing relatively weaker contribution?

For a Puzzle, ask:

Why is an economically attractive item not being chosen more often?

For a Dog, ask:

Is this item genuinely weak, or does it perform another role that the two-axis matrix cannot see?

That is also how the companion workbook frames the classifications: every item ends with the same essential instruction—

Investigate. Do not act from the label alone.

Convert each quadrant into an investigation question, not an automatic action.

Why I keep Classic Contribution separate

There is one more design choice I want to explain before we leave this chapter.

You may notice that the workbook contains both:

Classic Contribution Margin

and

Retained Contribution Margin.

We are deliberately not using Retained Contribution Margin to build the classical four-box screen.

Why? Because I want the SCREEN to remain recognisable and clean. First, we calculate the conventional menu-engineering view properly. Then we challenge it with additional evidence.

Later, under R — Retained Economics, we will bring in costs such as supported packaging, channel or order costs, and other directly decision-caused costs.

For example, the Club Sandwich has a Classic Contribution Margin of $11.90, but the synthetic workbook later shows Retained Contribution of $10.90 after supported direct decision costs are included.

That later view is important. But I do not want to quietly mix the two calculations and then pretend the traditional quadrant means something different.

The sequence matters:

Calculate the classical screen cleanly.

Then:

challenge it with better evidence.

SCREEN is a filter, not the final decision

So where are we now?

We know:

Grilled Chicken — Star

Club Sandwich — Plowhorse

Local Seafood Plate — Puzzle

Vegan Curry — Dog

If I were sitting in the menu review meeting, would I now make four decisions? No.

I would say:

“Good. Now I know where I want to look.”

The next step is not:

• raise the Club Sandwich price
• promote Seafood
• protect Chicken

and remove Vegan Curry. The next step is to ask whether these comparisons were built on the right population and whether the current classifications deserve to be interpreted the way we think they do. That is why the first part of our overall framework is called:

SCREEN

It tells me:

Where should I investigate further?

It does not tell me:

What should I do?

If you only remember one thing

Traditional menu engineering is useful because popularity and contribution quickly reveal different commercial patterns across the menu.

Its value comes from screening the menu, not from automatically prescribing the action.

Ask this question

When you look at a Star, Plowhorse, Puzzle or Dog, ask:

“What question is this classification asking me to investigate?”

Do not begin with:

“What action normally belongs to this box?”

Do this next

Take one category from your own menu. Do not analyse the full restaurant yet. Choose one reasonably comparable group—perhaps Main Courses, Desserts or Cocktails.

For each item, gather:

• units sold
• actual net selling value where available

and current trusted recipe or product cost. Calculate the menu mix and Classic Contribution Margin. Then classify the items. Stop there.

Do not reprice anything. Do not remove anything. Do not start promoting anything.

In the next chapter, we will build the SCREEN more carefully and deal with one of the most important questions in the entire process:

Are we comparing the right items in the first place?

CHAPTER 3