From the conversion glossary
Concepts referenced in this article, defined.
Search Results Personalization
Start lifting conversions today.
Run rigorous A/B tests and personalize every visit on Shopify or any storefront โ no engineers required.
Concepts referenced in this article, defined.
Run rigorous A/B tests and personalize every visit on Shopify or any storefront โ no engineers required.
Site search is your highest-intent channel โ visitors who search are actively looking for something to buy. Yet most ecommerce stores serve identical search results to every user for the same query, ignoring all the behavioral data that could make those results dramatically more relevant. Search results personalization reranks results for each user based on their history, preferences, and current context โ turning your search bar into a conversion engine.
Users who engage with site search convert at 2-5x the rate of non-searchers. They know what they want. They're further along in the purchase decision. They have declared intent.
For most D2C stores, site search users represent 10-15% of visitors but 30-40% of revenue. Improving the experience for this segment has outsized commercial impact compared to optimizing for the lower-intent 85-90% of visitors who don't search.
The problem: most stores show the same search results to everyone for a given query. "Vitamin C" returns the same ranked list for the first-time visitor and the returning customer who bought your face wash and is now interested in serum. But these two users want completely different things from that search.
Search results personalization adjusts the ranking and composition of results for each user based on:
Behavioral signals:
Profile signals:
Query signals:
Popularity and availability signals:
See also: Behavioral Targeting glossary | Dynamic Content glossary | First-Party Data glossary
The same products appear for a query, but their order changes based on user profile. The top result for "vitamin" for a skincare-browsing user is a vitamin C serum; for a fitness-browsing user, it's a multivitamin.
Some products from the generic result set are excluded based on the user profile. A user who has already purchased a specific supplement won't see that supplement in their "vitamin" search results โ instead seeing complementary options.
Specific products are surfaced higher for specific user profiles. A user who has repeatedly viewed a product but not purchased it gets that product promoted in relevant searches โ a gentle nudge toward conversion.
Personalized query expansion suggests additional search terms or autocomplete options based on user history. A returning buyer sees their previously-searched terms first in autocomplete.
When a search returns no results, personalized fallback โ showing products from categories the user has browsed โ keeps them on the site rather than bouncing on an empty results page.
Data: Behavioral data from the current session and past sessions (cookies/logged-in profile). Without behavioral signals, personalization defaults to relevance-based ranking (same for everyone).
Volume: Search personalization improves with more data. A store with 100,000 monthly sessions has more behavioral signal to work with than one with 5,000. Very small stores may not have enough data for meaningful ML-based personalization.
A search tool that supports personalization: Native Shopify search has limited personalization capability. Third-party options:
First-party data infrastructure: For deeper personalization (purchase history, loyalty status), the search tool needs access to your customer data โ either via Shopify's customer profiles or a connected CDP.
See also: Real-Time Personalization glossary | Audience Segmentation glossary | Visitor Segments glossary
Before personalizing, understand your baseline:
Set up a Search Console report in GA4 or use your search tool's analytics to get this data.
Personalization amplifies a good search experience โ it doesn't fix a broken one. Before personalizing:
Start with simpler, rule-based personalization before ML-based:
Tools like Klevu use machine learning to personalize based on behavioral patterns without explicit rules. Configure:
Key metrics:
Run an A/B test if your tool allows: personalized ranking vs. generic ranking for a controlled segment. The lift in search CVR is your quantified personalization impact.
Language and transliteration: Indian shoppers often search in a mix of English and transliterated Hindi or regional language. "Kesh tel" and "hair oil" should return the same results. "Ashwagandha" and "ashwagandha" and "ashvagandha" (alternate spelling) should all surface the same product.
Regional product preferences: A visitor from Maharashtra may search for "poha" while one from UP searches for a similar grain product by a different regional name. Geo-personalization can surface regionally relevant products for ambiguous searches.
Price-sensitive queries: "Cheap," "affordable," "under 500" โ Indian shoppers frequently qualify searches with price. Personalization can weight budget-friendly options for users who have historically purchased in lower price brackets.
Festive search patterns: During Diwali, "gift," "hamper," "combo" search volume spikes. Personalizing search results during festive periods to surface gifting options performs well.