A shopper arrives knowing what they want to do, but your menu asks them to think like your buying team. Another knows the exact product type and wants to get straight to it. Both need a route through the same range.
The decision is how to group products: by familiar types, by features, or by the job they will do. Category design should help customers understand the range and compare useful alternatives.
🔎 What research says
Give unfamiliar shoppers a clear structure. Check that the groupings match how customers see the products. Test specific use-based paths where they help people picture using the product, and examine the options inside each group before adding more labels.
🗺️ In this guide
1. Give unfamiliar shoppers a clear starting structure
📈 Recommendation
When visitors do not know the range, test a few clearly signposted groups before asking them to inspect every product. Use labels that explain what sits inside.
Compare grouped and ungrouped views without changing the products, prices or availability.
Check whether less knowledgeable shoppers can find a suitable option and feel confident about it.
🎓 Findings
Mogilner, Rudnick and Iyengar (2008) randomly assigned 61 students to choose a familiar or unfamiliar magazine from the same range, organized into three or 18 categories.
Among those choosing an unfamiliar magazine, satisfaction averaged 7.50 out of 10 with 18 categories, versus 6.18 with three. The familiar-choice group showed no clear difference. This was reported satisfaction with a magazine choice, not a paid-sales result.
🧠 Why it works
Groups can make differences in an unfamiliar range easier to perceive. In this study, greater perceived variety helped explain satisfaction. Clear, informative labels are our practical application; some study conditions also worked with uninformative labels.
✋ Limitations
Eighteen is not a target for your menu. Lewis and Gill (2016) reported that the effect did not extend to investment choices. Test the actual task, especially when the product is complex.
2. Match the groups customers already expect
📈 Recommendation
Before reorganizing a category, ask customers which products belong together. Test a layout based on their answers rather than assuming your supplier list or internal product codes make sense to them.
Give shoppers product cards to group and let them name the groups in their own words.
Check the result with both occasional and knowledgeable buyers; do not assume one grouping fits everyone.
🎓 Findings
Rooderkerk and Lehmann (2021) studied product grouping in two online shelf-choice experiments and a supermarket study. The online analyses included 334 and 660 participants, using yogurt snacks; the field study used biscuits.
A closer match between a shopper's own grouping and the displayed layout was linked to more perceived variety, less complexity, and more favorable choice outcomes. Shoppers' groupings often could not be explained simply by their stated favorite product attribute.
🧠 Why it works
A category can contain plenty of variety and still be easy to read when its structure makes sense to the customer. Asking which products belong together reveals more than asking whether brand, flavor or price is important.
✋ Limitations
The online studies used virtual shelves and measured purchase intention and expected satisfaction. They do not supply a forecast for paid ecommerce orders. The supplier-versus-type example should be tested against your customers' actual grouping.
3. Test a path built around product use
📈 Recommendation
When shoppers struggle to connect features with a useful outcome, test a specific route based on when or how they will use the product. Keep the familiar product-type route available.
For example, compare a tea-type menu with an additional morning-cup or evening-cup path, using only products that fit.
Measure completed purchases and returns as well as browsing; a more appealing route must still lead to a suitable product.
🎓 Findings
Ghiassaleh, Kocher and Czellar (2024) compared groups based on product features with groups based on intended benefits across eight studies involving 3,418 people.
In one supermarket study tracking 474 shoppers, the benefit-grouping period averaged 0.63 products selected per shopper, versus 0.40 in the feature-grouping period. These were products put into baskets, including shoppers who selected none. The comparison used different time periods, and it did not verify completed paid purchases.
🧠 Why it works
A use-based label can help someone picture the product in their life. The researchers found support for a path through imagined use and expected consumption value. That advantage weakened when imagery was already encouraged or shoppers could readily imagine product use.
✋ Limitations
The field comparison could also reflect differences between time periods. Broad benefit labels weakened the effect in other studies. Our morning/evening example was not the tested layout; use accurate, specific labels and check the result in your store.
4. Check the options inside each category
📈 Recommendation
Review how many useful alternatives each category contains before adding more branches. Test a structure that lets shoppers compare relevant options without making them open a new category for every item.
Identify branches with almost no choice and branches that still leave shoppers facing an undifferentiated wall.
Compare alternative group sizes while keeping the range accessible. Track successful product finding, backtracking and orders.
🎓 Findings
Sharma and Nair (2023) examined the number of options under each category label. They call this the category ratio.
Their paper reports three experiments: field and laboratory tests found less choice overload with the tested ratio, and another experiment found greater satisfaction than with an uncategorized range or a different ratio. The available abstract supports that direction; it does not provide enough detail to prescribe a numerical target here.
🧠 Why it works
A label changes where the comparison starts. The number of products beneath it still determines what shoppers must inspect. Adding headings alone does not guarantee a manageable decision.
✋ Limitations
The illustration's three groups and four products per group are examples, not research-backed targets. Equal-sized categories are not the finding. Choose group sizes that fit the range and test whether people can find what they need.
💡 The takeaway
Build categories around the shopper's task, then test whether the structure helps them choose.
Start with one difficult category. Compare clear grouping, customer-derived categories, and a specific use-based route where appropriate. Keep access to the full range, review the options within each group, and judge the change by successful choices and completed purchases.
📚 Sources and study types
Mogilner, C., Rudnick, T., & Iyengar, S. S. (2008). The Mere Categorization Effect: How the Presence of Categories Increases Choosers' Perceptions of Assortment Variety and Outcome Satisfaction. Journal of Consumer Research, 35(2), 202–215. Field and laboratory choice experiments; the cited magazine result measures satisfaction.
Rooderkerk, R. P., & Lehmann, D. R. (2021). Incorporating Consumer Product Categorizations into Shelf Layout Design. Journal of Marketing Research, 58(1), 50–73. Online virtual-shelf experiments and an in-store grouping task; choice outcomes and modeled relationships.
Ghiassaleh, A., Kocher, B., & Czellar, S. (2024). The effects of benefit-based (vs. attribute-based) product categorizations on mental imagery and purchase behavior. Journal of Retailing, 100(2), 239–255. Eight field and controlled studies; the cited numerical result records in-store basket selections across different periods.
Sharma, A., & Nair, S. K. (2023). Category ratio: A search for an optimal solution to reduce choice overload. Journal of Consumer Behaviour, 22(4), 880–890. Experimental findings on options per category; the summary here uses the publisher's abstract.
Lewis, D., & Gill, T. (2016). Is there a mere categorization effect in investment decisions?. International Journal of Research in Marketing, 33(1), 232–235. A replication in investment choice that did not extend the original effect; boundary evidence.
