Product assortment shapes how businesses attract and retain customers. I see it as a balancing act—too few options frustrate shoppers, while too many overwhelm them. The right mix depends on consumer behavior, inventory costs, and competitive positioning. In this article, I break down the science behind assortment optimization, the math that drives it, and real-world applications.
Table of Contents
What Is Product Assortment?
Product assortment refers to the variety of items a retailer offers. It includes:
- Breadth (Width): The number of product categories (e.g., electronics, clothing, groceries).
- Depth: The number of products within a category (e.g., 10 types of jeans vs. 50).
- Variation: Differences in size, color, or features (e.g., iPhone models with varying storage).
A well-structured assortment maximizes sales while minimizing excess inventory.
The Economics of Assortment Decisions
Retailers face a trade-off between choice and cost. More products mean higher storage, handling, and capital costs. The goal is to find the point where marginal revenue equals marginal cost.
Let’s model this:
MR = MCWhere:
- MR = Marginal Revenue (additional income from one more product variant)
- MC = Marginal Cost (additional expense of stocking one more variant)
If MR > MC, adding more variants increases profit. If MR < MC, the assortment is too broad.
Example: A Clothing Store’s Assortment
Suppose a store sells t-shirts. Each new design costs $500 to stock (procurement + holding cost). Data shows:
| T-Shirt Designs | Expected Revenue per Design | Total Revenue | Marginal Revenue |
|---|---|---|---|
| 10 | $1,200 | $12,000 | – |
| 11 | $1,150 | $12,650 | $650 |
| 12 | $1,100 | $13,200 | $550 |
Here, adding an 11th design increases revenue by $650, which exceeds the $500 cost. But the 12th design only adds $550, barely justifying the cost. The optimal stopping point is likely 11 designs.
Consumer Psychology and Choice Overload
Studies show that while consumers demand variety, too many options paralyze decision-making. A famous experiment by Sheena Iyengar found that jam displays with 24 varieties attracted more attention but fewer sales than displays with 6 varieties.
The Paradox of Choice
Barry Schwartz’s Paradox of Choice argues that beyond a certain point, more options reduce satisfaction. Retailers must find the “sweet spot.”
A simple formula to estimate the ideal number of variants (N) in a category:
N = \frac{D}{1 - e^{-k \cdot S}}Where:
- D = Maximum demand potential
- k = Sensitivity factor (varies by product)
- S = Shelf space constraint
This ensures enough variety without overwhelming shoppers.
Assortment Planning in E-Commerce vs. Brick-and-Mortar
Online and offline retailers approach assortment differently:
| Factor | E-Commerce | Brick-and-Mortar |
|---|---|---|
| Space Constraints | Virtual (unlimited in theory) | Physical (limited shelves) |
| Cost of Expansion | Low (digital listings) | High (rent, storage) |
| Consumer Behavior | Filter-heavy (search bars) | Impulse-driven (displays) |
Amazon uses algorithms to personalize product displays, while Walmart optimizes shelf space based on regional demand.
Data-Driven Assortment Optimization
Modern retailers rely on:
- Sales Data Analysis – Identifying top sellers and dead stock.
- Market Basket Analysis – Seeing which products are bought together.
- Clustering Algorithms – Grouping similar products for better placement.
A basic sales velocity formula helps prioritize products:
Velocity = \frac{Units\ Sold}{Time\ Period}High-velocity items deserve prime placement; low-velocity items may need replacement.
Real-World Case: Best Buy’s Assortment Strategy
Best Buy reduced SKUs by 20% in 2012, focusing on high-demand electronics. Result? Higher sales per square foot and lower inventory costs. They used:
- ABC Analysis: Classifying items by revenue contribution.
- Customer Surveys: Identifying must-have products.
Conclusion
Product assortment isn’t just about offering more—it’s about offering better. By balancing variety with operational efficiency, retailers can enhance customer satisfaction and profitability. Whether through data models or consumer psychology, the key is continuous testing and refinement.
