Published January 12, 2026
The “Dead Item” That Shouldn’t Die — Understanding Penetration vs. Habituation
You’re reviewing menu performance – one item makes up 2% of product mix.
Barely registers in total sales. Takes up menu real estate. Adds kitchen complexity. Obvious removal candidate, right?
Before you axe it: What’s the frequency among those who DO buy it?
The Dimension Most Menu Analysis Misses
Most menu analysis focuses on volume. Total units. Total revenue. Percentage of mix.
But volume conflates two very different inputs: reach (how many different customers order this item) and frequency(how often those customers order it).
A product can have high volume through wide reach—lots of people buy it occasionally. Or through deep frequency—a smaller group buys it constantly. The strategic implications are completely different.
The Four Quadrants
High Reach + High Frequency = Core Winner. Many people know about it, and they order it repeatedly. Protect it. Keep it consistent. Never run out.
High Reach + Low Frequency = Known But Not Loved. Many people have tried it, but they don’t come back for it. Awareness isn’t the problem—the product itself isn’t compelling enough to drive repeat behavior. Consider reformulating or removing.
Low Reach + Low Frequency = True Dead Item. Few people order it, and those who do don’t come back. Nobody knows about it AND those who try it aren’t impressed. This is your legitimate removal candidate.
Low Reach + High Frequency = Niche Winner. This is where the magic happens.
The Quantiiv Console highlights these quadrants in our Menu Item Performance Matrix, making it easy to spot which items fall where—and more importantly, which “low performers” are actually niche winners waiting to be discovered.
The Niche Winner Pattern
A product with low reach but high frequency has achieved something remarkable: proven product-market fit with a small audience.
The customers who know about this item don’t just like it—they’re passionate about it. They order it repeatedly. They might order it every single visit. They probably tell their friends about it. They’d be disappointed if you removed it.
This isn’t a product problem. It’s an awareness problem.
The product is great. It just isn’t visible enough. The strategic move isn’t removal—it’s amplification: better menu placement, LTO spotlight campaigns, staff recommendation training, social media features, email highlights to customers with similar taste profiles.
You’ve already done the hard work of creating something people love. The product development is done. The recipe is proven. You just need more people to discover it.
The Cost of Getting This Wrong
Here’s what happens when brands sort products by total volume and cut from the bottom:
They eliminate their niche winners alongside their true dead items. Both look the same in a simple ranking—low total volume. But their futures were completely different.
The true dead item wouldn’t have been missed. Removing it simplifies operations with no customer impact.
The niche winner had an enthusiastic fan base. Some might be vocal about the removal. Some might quietly reduce their visit frequency. Some might have been the customers telling friends about “this amazing thing you have to try.”
The action for each is opposite. One should be removed to reduce complexity. The other should be promoted to capture latent demand. Treating them the same is a strategic error.
A Real Pattern We’ve Seen
Specialty seasonal beverage. Appeared near the bottom of product rankings—maybe 1.5% of customers ordered it.
But when you look at frequency, the story flips. Customers who ordered it had average frequency of 3.2 orders over 60 days. Compared to 1.4 for customers who didn’t order it.
That beverage wasn’t dead. It was a loyalty anchor for a passionate segment. The customers who discovered it visited more often specifically because of it.
The recommendation wasn’t removal. It was a targeted awareness campaign. The hypothesis: if we can increase reach without diluting frequency, we grow both sales and visit frequency simultaneously.
Why Generic Analytics Fails Here
A tool without domain knowledge will flag low-reach items for removal. It sees low volume and draws a conclusion.
It won’t distinguish between “unloved” and “undiscovered.” It won’t recommend entirely different strategies for items that look identical in a simple ranking.
This is why Quantiiv automatically calculates both reach and frequency, plots products into the quadrant framework, and identifies outliers where awareness investment has the highest expected return. Because a product ranking is not the same as a product strategy.
What’s the most surprising “dead item” you’ve found that turned out to be a niche winner?