Jonathan Landy
Why do grocery stores often carry over 100 options for both cheese and wine — but just a handful for pasta?
In this post, we explore the economic forces behind that imbalance, highlighting four key traits that determine whether a product category can realistically support a broad assortment. To test the framework, we review assortments across various industries and find that it aligns closely with what we observe in practice.
Effective inventory management always depends on a clear understanding of supply and demand. The framework we outline here can help to qualitatively guide assortment breadth decisions in your business — supporting growth while minimizing waste.
Drivers of Assortment Complexity
We see four key factors – three on the supply side and one on the demand side – that influence whether a product category can support meaningful complexity.
Supply-Side Factors:
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Shelf Life: When you add competing options, demand fragments. This makes it harder to predict demand for each individual product, increasing clearance or spoilage risk. This risk is reduced with increased shelf life – which allows for demand fluctuations to average out – so we expect to see more complexity in assortments of stable goods.
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Retail Price: Adding one more option to an assortment incurs a fixed opportunity cost but will often result in only a marginal increase to sales. However, when the products in question have a high price, those extra sales are more valuable and the tradeoff is more likely to be favorable.
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Cost to Support Diversity: Some categories are simply easier to diversify. E.g., various juices use apple juice as a base, making it cheaper than you'd expect to offer a wide juice selection. Wine is another good example: Distributors often let shops buy just a few bottles per label at a time, enabling affordable breadth.
Demand-Side Factors:
- Variable Customer Preferences: Without variation in consumer taste, additional options won’t lift sales. These preferences can be inherent (e.g., flavor preferences in snacks) or tied to signaling (e.g., style choices in apparel). The more visible and expressive the product, the more likely customers are to value and pay for differentiation.
In short: it’s all about supply and demand. A product category can support more options only if the marginal return justifies it. Costs include input, upkeep, and the spoilage risk from fractured demand — lowering these costs allows for greater assortment complexity. Gains come from higher marginal revenue, which rises with consumer preference diversity and higher price points.
These ideas may seem self-evident, but we'll now check to see that they fit the data.
Empirical Review of Assortment Breadths
Categories Where Complexity Is Supported-
Wine: Long shelf life, high price, distributors keep price of complexity low, strong signaling value encourages diverse preferences. All four of our requirements check out.
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Apparel: Long shelf life, moderate-to-high price, relatively low cost to support diversity above MOQs, again strong signaling value.
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Cars at a dealership: Long shelf life, very high price, relatively low cost to support diversity at a dealership through manufacturer financing rules, again strong signaling value.
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Greeting Cards: Long shelf life, compact (good price per unit volume), cheap to diversify, expressive.
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Microbrewery Beer: Similar to wine, this checks off all requirements. We also note that this was not always the case – instead, availability exploded after deregulation in 1978 (USA). The before and after change highlights the importance of the cost of complexity.
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Canned Food, Ketchup, etc.: Good shelf life, but low price and little consumer demand for breadth, perhaps because of little signaling value.
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Produce: Outside specialty shops (e.g., Berkeley Bowl here in the Bay Area), grocery stores rarely carry a great many varieties of fruit. Here, I posit the issue is not lack of consumer interest, but rather the short shelf life.
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Toothpaste: Durable, reasonable price per unit volume. But it’s hard to believe there is much marginal demand driven by the great many options available (though a dentist friend assures me some people are very particular about their toothpaste).
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Chips: Long shelf life, but relatively low cost per unit volume and little signaling benefit. Nevertheless, my local Trader Joes has 10 tortilla chip options (see image below).
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Higher Education: Majors and courses often abound, but the PhD advisor of a friend suggested to me that this is often supply / faculty-driven, not demand-driven. Are market forces weak here?
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Restaurant Menus: Huge menus are sometimes created via multiplicative combinatorics – meaning it’s cheap for some restaurants to support these. But do diners really want that much choice?
Conclusion: The empirical data seems to mostly align with our framework. In the suspect cases, we assert there must either be more here than meets the eye (perhaps hidden supply factors) OR these are legitimate market inefficiencies!
Figure 1: My local Trader Joes carries 10 tortilla chip options. The unit price is relatively modest, flavors are similar (chip connoisseurs may disagree), and the selection takes up a meaningful fraction of an aisle, incurring significant opportunity cost. Does this make sense or is this inefficient?
Summary
Whether a product category supports assortment complexity is largely determined by supply and demand. On the supply side, the cost of enabling that complexity must remain manageable – sometimes through tiered systems like retailers and distributors in wine, or manufacturers and dealerships in cars. The marginal lift from added variety must justify the space or effort (favoring higher-priced items), and the increased demand uncertainty must be tolerable – often made easier by long shelf life. On the demand side, it comes down to whether customers have distinct preferences and are willing to pay for variety.
In a perfectly rational market, these forces would dictate the ideal level of complexity. But in practice, we see edge cases that are hard to understand. Some of these might be explained by obscure market dynamics, but others likely reflect true inefficiencies – the challenge of managing complexity. In those cases, better tools and decision frameworks can help.
About VarietyIQ
VarietyIQ helps retailers and brands optimize inventory through smarter forecasting and product curation. We combine advanced data science with domain expertise to improve planning accuracy and unlock growth.
Curious how this framework might apply to your assortment? Get in touch — we’d love to connect.
Thanks to Jaireh Tecarro for creating the images for this post, to Warren Taylor for suggesting the mircrobrewery beer example, and to Greg Novak for the historical context in that example.
