Glossary · Concept
What is a product recommendation engine?
A product recommendation engine is the software that decides which products to show a given shopper. It takes inputs (browsing behavior, purchase history, or stated preferences) and ranks your catalog to surface the best matches.
Last reviewed June 7, 2026
Every store that shows a shopper something other than the full catalog is running a recommendation engine of some kind, even if it is just a manual best-sellers rail. The interesting question is what the engine uses to decide.
The main types
Behavioral engines use what shoppers do: pages viewed, items clicked, things bought. They power the classic recommended for you and customers also bought rails. They rely on collaborative filtering, which needs a lot of traffic and history to work well.
Rules or attribute-based engines use product data and merchandiser-set logic: show accessories with this category, surface in-stock items first. They are predictable but static.
Preference or quiz-based engines use what the shopper tells you directly. The shopper answers a few questions and the engine ranks the catalog against those answers. This is the most direct signal of intent, because it is volunteered rather than inferred.
The cold-start problem
Behavioral engines have a well-known weakness: they break for new visitors and new products. A first-time shopper has no history, so the engine has nothing to go on and falls back to generic best-sellers. A newly added product has no clicks yet, so it rarely gets recommended.
A preference-based engine has no cold start. A brand-new visitor who answers three questions gives the engine everything it needs on the very first visit, with no tracking and no waiting for data to accumulate.
How a quiz engine ranks products
Each answer carries weight. A must-have answer can upvote the products that match and downvote the ones that do not, while a hard constraint like an allergy or an incompatible spec can exclude products entirely, no matter how well they scored otherwise.
The result is a ranked, justified shortlist rather than a single guess. Good engines also reserve a slot for each role in a set, so the output can be a complete routine, stack, or bundle instead of one product.
Product recommendation engine with RevenueHunt
RevenueHunt is a preference-based product recommendation engine driven by a quiz. Answers upvote, downvote, or exclude products from your live catalog, recommendation slots reserve a place for each role in a set, and it works for a first-time visitor with zero history.
Because the inputs are volunteered, the same answers double as zero-party data you own and can use for segmentation long after the shopper leaves.
Frequently asked questions
What is the difference between a behavioral and a quiz-based recommendation engine?
A behavioral engine infers intent from clicks and purchase history, which needs traffic and breaks for new visitors. A quiz-based engine uses preferences the shopper states directly, so it works on the first visit with no history.
Do I need a lot of traffic for a recommendation engine to work?
Not for a quiz-based engine. Because the shopper tells you their preferences, it works from the first visit. Behavioral engines, by contrast, need significant traffic and purchase history before their recommendations are reliable.
Can a recommendation engine suggest more than one product?
Yes. With recommendation slots, the engine reserves a place for each role in a set and fills each with the best match, returning a complete routine, stack, or bundle rather than a single item.
Related reading
- Personalized product recommendations
- Conversational commerce
- Product finder quiz
- How it works
- Quiz for Shopify
More glossary terms
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Conversational commerce
Conversational commerce is selling through an interactive, two-way conversation instead of a static product grid. Shoppers answer questions, the store responds with tailored recommendations, the way a good salesperson works in a physical shop.
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Product discovery
Product discovery is how shoppers find the right product in your catalog. Good discovery, through search, filters, and guided quizzes, moves a shopper from I have a problem to this is the product with as little friction as possible.
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Personalized product recommendations
Personalized product recommendations are suggestions tailored to an individual shopper rather than the same best-sellers shown to everyone. They can be based on browsing behavior, past purchases, or, most directly, on what the shopper tells you.
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Ecommerce personalization
Ecommerce personalization is adapting the shopping experience, the products, content, and offers a shopper sees, to the individual rather than showing everyone the same store. Done well, it lifts conversion and average order value.
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Shoppable quiz
A shoppable quiz is an interactive quiz that ends in a personalized results page where shoppers can add the recommended products straight to cart. The quiz is part of the storefront, not a survey that lives off to the side.
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Quiz funnel
A quiz funnel is a marketing funnel that uses a quiz as the entry point. A shopper takes a quiz, gets a recommendation, gives their email, and enters a segmented follow-up sequence. It turns anonymous traffic into a qualified lead with a known preference.
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Lead generation quiz
A lead generation quiz captures qualified leads: a shopper answers a few questions, gives their email to see the result, and you get a contact tagged with their stated preferences. It is an opt-in with a built-in reason to subscribe.
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Conversion rate optimization (CRO)
Conversion rate optimization (CRO) is the practice of increasing the percentage of visitors who take a desired action, usually a purchase. You measure conversion rate as conversions divided by visitors, then improve it without buying more traffic.
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Average order value (AOV)
Average order value (AOV) is the average amount a customer spends in a single order. You calculate it by dividing total revenue by the number of orders over the same period.
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Cross-selling and upselling
Cross-selling recommends related products that complement what a shopper is buying, like a moisturizer with a cleanser. Upselling recommends a better or larger version of what they already want, like a bigger size or a premium tier. Both raise order value.
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Customer segmentation
Customer segmentation is the practice of grouping customers by shared traits, like goals, behavior, or demographics, so you can market to each group with relevant messaging instead of sending everyone the same thing.