Find products faster
Search, synonyms and filters guide customers to the right product more quickly.
Personalisation & search
Search, sorting, recommendations. Built around your range.
Most stores show everyone the same thing - and sort by whatever the last collection edit left behind. Search, merchandising and recommendations belong together.
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Sell more deliberately
Sorting and merchandising connect customer relevance with campaign and margin goals.
Encourage discovery
Relevant recommendations expand product choice on product pages, in the basket and customer account.
- Since 2019
- 150+ Migrations and Relaunches
- Official Shopify Plus Partner
What do we improve in search and merchandising?
Search and filters
Synonyms, misspellings, weighted fields and filters that match your range. Plus the question of whether the built-in tools suffice or a dedicated search index is needed.
Merchandising and sorting
Sort logic per collection, promoting campaign and margin products, handling sold-out items and automated collections instead of manual curation.
Recommendations and segments
Related and complementary products on product and cart pages, recommendations in the customer account, and segments for different treatment by market, customer group or purchase history.
What pays off first - and what is expensive for little effect?
First: product data
Search, filters and recommendations work with what sits in titles, types, tags and metafields. If the data is thin, no software helps - it only makes the gap visible faster.
Then: collection sorting
The default sort decides what thousands of visitors see first. It is the lever with the best effort-to-effect ratio - and in many stores it has been untouched for years.
Then: search within the built-in tools
Synonyms, field weighting, filters and complementary products can be set up at no extra cost. That carries surprisingly far for ranges up into the low five-figure product count.
Only later: a dedicated search product
An external search index pays off with large ranges, many attributes, multilingual catalogs or hard relevance requirements - not as the first response to a poor result list.
Sold-out items crowd out revenue
Collections keep showing unavailable products in the top rows. Automated collections and sort rules solve that, but it does not happen on its own.
Zero results are data
Searches with no result reveal missing synonyms, wrong terms or genuine gaps in the range. Not evaluating them wastes the best source of improvements you have.
Filters with empty values
Filters built from inconsistently maintained attributes produce options with no matches. That is a data quality problem, not a front-end problem.
Personalisation without use
Recommendations showing what the customer just bought cost trust. Rules for exclusions and complements matter more than the algorithm behind them.
How do we approach search and personalisation?
01
Evaluate search behaviour
We look at top search terms, zero results, drop-off after search and the conversion rate of search users. In many stores, search users buy far more often than average.
02
Check product data
Titles, product type, tags, attributes and metafields get checked for completeness and consistency. Without that step, any relevance tuning stays guesswork.
03
Set up search and filters
Configuring synonyms, field weighting, filter axes and complementary products. Then we review the result lists for the terms that actually generate revenue.
04
Merchandising rules
Defining the default sort per collection, factoring in availability, defining campaign slots. Rules rather than manual ordering, so it still holds next season.
05
Place recommendations
Building related and complementary products in where they answer a question: product page, cart, and after purchase in the customer account.
06
Measure and refine
Watching search-user conversion, click rate on recommendation areas and revenue per collection. Personalisation without measurement is just a claim.
Where does differentiated treatment really pay off?
New versus returning
First-time buyers need orientation and trust; repeat buyers need the shortest path to a reorder.
Market and language
Range, availability and recommendations differ per market - the form of personalisation with the clearest business case.
B2B versus D2C
Company accounts see their own catalogs and prices. Search and sorting should follow that split rather than fight it.
Purchase history
Consumables, spare parts and accessories are far better recommended from past orders than from behaviour within a session.
When does work on search and recommendations pay off?
Then this is the right move
Search gets heavy use
A noticeable share of your visitors starts at the search bar. The result list then decides your revenue, not the homepage.
The range has grown
What was sortable by hand at a hundred products is not at a thousand. From there, rules replace manual curation.
Collections show sold-out items
Unavailable products keep sitting in the top rows and crowd out exactly the items you can actually ship.
Several customer groups
Company accounts, markets or repeat buyers need their own view of the same range - and currently all see the same order.
Then the groundwork comes first
Product data is thin
Titles, product types, tags and metafields are inconsistent or half empty. Search, filters and recommendations cannot build anything reliable from that.
Connect product data from PIM and ERPToo few visitors
Without a meaningful volume of searches there are no zero-result queries to evaluate and no basis for testing sort rules against each other.
Build visibility firstPeople already find it
Your visitors land on the right product page and still leave. The problem then sits behind the result list, not in front of it.
Go to CRO optimisationWorking together
How do we work on search and personalisation?
Three formats - from evaluating your search data through to ongoing upkeep of relevance and sorting.
Search analysis
Who it is for
For stores that suspect search is costing revenue but have no numbers in front of them.
What is included
- Evaluate search terms and zero results
- Check product data fields for consistency
- Compare collection sorting against availability
How it ends
Ends with a ranked set of levers: what works first, what can wait, what you maintain yourself.
Search and merchandising rebuild
Who it is for
For stores where the priorities are settled and search, filters and sorting are to be genuinely rebuilt.
What is included
- Set up synonyms and field weighting
- Align filter axes with the range
- Sort rules per collection instead of manual work
How it ends
Ends with rules that still hold after the next delivery and into the next season.
Relevance upkeep
Who it is for
For stores with a frequently changing range whose result lists would otherwise be stale again half a year later.
What is included
- Work through zero results regularly
- Fold new product groups into filters
- Test sort and recommendation rules against each other
How it ends
The result is a result list that grows with the range instead of trailing behind it.
E-commerce for brands with bigger ambitions.
Brands with €950M+ GMV trust us
From migration to scale, NICCOS combines sophisticated design, robust technology and data-driven growth on Shopify.
Frequently asked questions about personalisation and search on Shopify
Is Shopify search enough or do we need an additional system?
For most ranges the built-in tools carry further than expected: synonyms, field weighting, filters and complementary products are configurable at no extra cost. An external search index pays off with very large catalogs, many technical attributes, multilingual content or when you need fine-grained relevance control.
Why don't you split search and personalisation into two topics?
Because both live off the same source: your product data. A recommendation is a filtered, sorted list - exactly like a search result. Splitting the topics means building the same data work twice and later wondering why different parts of the store disagree.
How do we push sold-out products down the list?
Shopify offers no built-in sort order for that. The usual route runs through automated collections with an inventory condition, plus rules in the theme or an app. What matters is that the rule applies automatically - a one-off manual sort is wrong again after the next delivery.
What do product recommendations actually deliver?
They work mainly where they answer an open question: matching accessories on the product page, complements in the cart, reorders in the customer account. As a decorative carousel at the bottom of a page they deliver little. Placement and exclusion rules matter more than the logic behind them.
How does this relate to consent and privacy?
Personalisation based on behaviour and profiles touches consent and data processing. Recommendations derived from the catalog itself - related products, complements, sort rules - need no personal profiles. That is why we build what works without profiling first. Anything built on profiles comes afterwards, and only with a properly wired consent state.
How do we measure whether it worked?
Through the conversion rate of search users against all users, the share of zero-result searches, the click rate on recommendation areas and revenue per collection before and after the change. Where traffic allows, we test sort and recommendation rules against each other instead of asserting them.
Do your customers find what they came for?
We review search terms, zero results and collection sorting and tell you which lever works first for your range.
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