Product proliferation starts small. A customer asks for a color change, a competitor forces a new variant, and a few years later a company is managing hundreds of SKUs that each looked reasonable to add on their own.
The problem is that this growth rarely gets reviewed as a whole. Each new stock-keeping unit gets approved individually, but nobody tracks the total cost of managing them until margins have already thinned out.
This article explains what product proliferation is, why it damages profitability quietly, and how conjoint analysis gives companies a data-backed way to decide which products to keep.
What is product proliferation?
Product proliferation is the gradual expansion of a company’s product line into a large number of variants, often driven by individual customer requests or competitive pressure, without a corresponding review of whether each variant remains profitable.
It is different from healthy product line growth. Healthy growth adds variants because market research shows clear demand. Proliferation adds variants because saying no to one customer request feels riskier than adding another SKU, even when the long-term cost outweighs the short-term win.
How does product proliferation quietly hurt profitability?
Product proliferation hurts profitability by increasing complexity costs faster than it increases revenue, and that gap is easy to miss because it grows slowly.
The typical pattern looks like this. A customer requests a custom variant and promises meaningful volume. The company adds it. Volume comes in lower than promised, and shrinks further over time. Meanwhile, the paperwork, quality system management, and manufacturing complexity for that SKU stay fixed or even grow.
Research on product line complexity from McKinsey has found that complexity costs frequently outpace the revenue gains from added SKUs once a portfolio grows past a manageable size. A company can end up managing dozens of unprofitable variants while only watching the aggregate margin shrink, without seeing which specific products are causing it.
Why is discontinuing products so difficult?
Discontinuing products is difficult because it forces a conflict between departments that measure success differently. Sales and marketing worry about losing a customer relationship. Finance and manufacturing worry about a margin that keeps eroding.
Historically, companies faced two blunt options: cut the underperforming product and risk the customer relationship, or keep it and accept the bleeding margin. Neither option resolves the real question, which is whether customers actually value the feature that justifies the SKU in the first place.
How does conjoint analysis solve the SKU decision?
Conjoint analysis solves the SKU decision by asking customers directly to trade off product features against price, instead of guessing internally which variants matter.
Conjoint analysis is a market research technique that shows respondents different combinations of product features and prices, then measures which combination they actually prefer. Rather than asking “do you like this feature,” it asks a more useful question: how much would you pay for it, relative to the alternatives?
How do you set up a conjoint study?
Setting up a conjoint study starts with identifying the features and price points that matter most for the product category.
- List the core features of the product, such as screen size, format, or color options.
- Define the levels within each feature, for example, LCD or LED for a display format.
- Define the price points to test against those feature combinations.
- Present respondents with combinations to choose from or rate.
Most teams run this alongside a broader market research survey to first confirm which features are even worth testing before building the full conjoint design.
A conjoint analysis module inside a market research platform builds these combinations automatically and calculates which features respondents value most, along with the price they are willing to pay for each.
What does conjoint analysis reveal that internal opinion cannot?
Conjoint analysis reveals the actual price elasticity of a feature, something internal debate cannot settle on its own. A team can argue for weeks about whether a color option justifies its cost. A conjoint study answers it directly: is a customer willing to pay 10 percent more for it, or would they walk away from the purchase entirely without it?
This often produces counterintuitive results. A feature the internal team assumed was low priority sometimes turns out to justify a meaningful price premium, which means keeping it, at a higher price, beats discontinuing it. The reverse also happens. A feature everyone assumed customers loved sometimes shows up as something they will not pay extra for at all.
Common mistakes when rationalizing a product line
Companies cutting SKUs without research tend to repeat the same errors.
- Cutting based on internal sales volume alone, without checking whether low volume reflects low demand or poor placement.
- Assuming the loudest customer request represents broader market demand.
- Skipping a re-test after a price change, since customer tolerance for a feature can shift once price moves.
- Treating one conjoint study as permanent, when customer preferences shift as competitors and market conditions change.
Running a lighter pricing research survey between full conjoint studies helps catch these shifts earlier.
Product rationalization backed by real customer data
Cutting products always feels risky, because it means saying no to someone. Conjoint analysis takes the guesswork out of that decision by putting real trade-offs in front of real customers before a company commits to keeping or cutting a SKU.
A market research platform that supports conjoint studies turns a defensive, department-versus-department argument into a data-backed decision both sides can act on.
Frequently Asked Questions (FAQs)
A well-scoped conjoint study can be designed, fielded, and analyzed within two to four weeks for most consumer products, though complex B2B products with many feature combinations may take longer to reach a reliable sample size.
No. Small and mid-sized companies with a manageable SKU count benefit just as much, since even a modest product line can carry hidden unprofitable variants that a conjoint study can identify quickly.
A preference survey asks which feature customers like most in isolation. Conjoint analysis forces a trade-off between features and price at the same time, which produces a more realistic picture of what customers will actually pay for.
Yes. The same trade-off data that identifies which existing products to cut also identifies which new feature combinations are worth developing, since it shows exactly what customers value enough to pay for.
Most conjoint studies need a minimum of 150 to 300 respondents per key segment to produce statistically reliable feature and price trade-off data, though the exact number depends on how many features and levels are being tested.
