Although a Shopify shop can utilize AI technology to conduct searches, it can still generate irrelevant results. That is because providing the results may not equal in providing relevant results.

Let’s say a person enters a request:

The system will generate a list of 48 goods. But even at first sight, it might seem as though the search is efficient.

What if the first search results include non-waterproof shoes? Or else if leisure shoes come before hiking shoes? If the results include items exceeding 1500? Or if customers see the results but do not pick any item?

Technically, the search may have worked. However, it could fail in terms of retrieval, relevance, and ranking.

This is the reason why Shopify vendors need to assess the performance of AI search in a broader context than just checking whether there are any results produced by a query.

An effective approach takes into account all stages of the search process:

This article provides explanations for Shopify vendors on how to perform search quality testing. Also, the ExpertRec product can support continuous search optimization.

1. Search Success Is More Than Avoiding Zero Results

When success in searching is concerned, it is more than simply not showing nil results. Indeed, if we look at what zero results mean, we can see the whole lot of problems at once.

The query is “a 15-inch laptop backpack”.

The conclusion is obvious – the merchant has serious issues.

On the other hand, another type of search failure is a search that finds many products, yet does not help the customer.

The query is “ergonomic chair for back pain”.

The system produced 126 results, but customers clicked on nothing.

This can mean that:

• Some irrelevant items are ranked too high
• The essential features of the products searched for are not present in the search or product characteristics
• The wording used by customers is different from that used in the catalogue
• Relevant results are appearing lower in lists of search results
• The results do not correspond to the customer needs

The third type of failure is search effectiveness tests that include no-result, retrieval, and ranking failures. Each type of failure requires its own approach for optimization.

2. Build a Shopify Search Test Set

Among the available methods to evaluate AI search, one of the most efficient requires generating a controlled list of important customer queries. This is what is called a search test set.

The queries to be tested should reflect real customer behavior and not simply be random ones.

• Frequent search queries
• Relevant product categories
• Failed queries
• Long-tail queries
• Seasonal queries
• Queries with low click rates

All this will make the evaluation of search actions more systematic and not dependent upon random experiments.

3. Test Retrieval Quality: Did Search Find Relevant Products?

The first question that comes to mind is:

For example, let’s take a hypothetical Shopify store with 20 waterproof hiking boots.

A customer types in a request:

“waterproof hiking boots”

If only two products are found, there is a problem with retrieving.

This can happen when:

• There is a lack of product characteristics;
• The necessary fields cannot be searched;
• The product information is inconsistent;
• The words used in the catalog are different than the ones used by the customers;
• The relevant information is hidden in an unstructured manner.

A concept that may be encountered here is recall.

Put simply, recall indicates if the search function can find the relevant products from the catalog.

Vendors do not always need to measure the recall in a formal manner. The practical issue is:

Are relevant products missing from the search results?

If yes, then the issue occurs before the ranking process.

This is when product data, searchable fields, tags, characteristics, and metafields come into play.

For Shopify vendors, ExpertRec can offer a configurable search layer that defines the product data used for searching.

4. Test Ranking Quality: Are the Best Products Appearing First?

Retrieval alone will not suffice.

A search engine might retrieve 100 pertinent products, but the majority of consumers will pay attention only to the first few results.

Let’s take a look at the following case:

There are relevant products, but their ranking leaves much to be desired.

This brings us to the ranking issue.

A term that can be helpful in this case is precision.

In the context of ecommerce search, one can understand precision as:

How significant are products in the search results that are displayed in the prominent search positions?

Search testing ought to identify:

• Are the first listings appropriate?
• Are perfect matches attributed the value they deserve?
• Are relevant product features taken into consideration for ranking purposes?
• Are irrelevant products occupying better places than higher-quality products?
• Do users scroll down too much before getting to appropriate products?

The configurable relevance controls and search weights from ExpertRec can help companies.

5. Treat Low-Click Searches as Warning Signals

A search query may bring back results but still leads to failure.

This is important since it could mean that the problem is hidden.

Searches with zero results have a problem identified easily.

Low clicks are far more serious since a seller may think that the search is efficient simply due to the appearance of the results.

Merchants should watch for:

• Searches with no clicks at all
• Search queries with an unexpectedly low click rate
• Most searched queries
• Products most clicked after doing the search
• Queries consumers reformulate often

For instance:

Consumers reformulating their searches could mean that they don’t manage to express what they are looking for.

6. Conduct Tests with Queries in Complex Form

Most merchants test search using exact queries on the names of products.

Example:

“Nike Air Zoom Pegasus”

While the use of exact searches is necessary, it does not provide a good representation of how all customers perform their searches.

Customers typically perform searches that involve problems, use cases, features, budgets, or compatibility requirements.

Example:

In this case, this particular search includes:

• Product type – running shoes
• Requirement – flat feet
• Price constraint – under $120

Testing complex queries will help merchants find out if the search experience truly allows customers to search for products.

A good set of tests for Shopify search would involve:

• Direct queries, i.e., product names or SKUs.
• Category queries, for instance, “wireless headphones.”
• Attribute queries, for example, “waterproof jacket.”
• Use-case queries, for instance, “laptop bag for travel.”
• Constraint queries, for example, “black shoes under $100.”

This provides a better evaluation of the search quality.

7. Introduce Search Regression Testing

Search effectiveness is very changeable over time.

New goods come up on the market. Titles of products change. Metafields have alterations. Product specifications return to being inconsistent. Search fields and filters undergo reconfiguration.

A search configuration that provided satisfactory results last month may yield dissimilar results in the present.

That is where search regression testing comes in handy.

The procedure is simple:

For instance, before the catalog update, the search for “waterproof backpack” would show waterproof things on top.

While in the observed situation, casual bags can appear higher than waterproof backpacks.

The search works indeed, but the quality of its results is reduced.

8. Create a Shopify AI Search Quality Scorecard

Searching for products is easier with a structured methodology.

An example of a simple search quality scorecard might include:

Where ExpertRec Fits Into Shopify Search Testing

Shopify provides the original product catalog along with the e-commerce platform. But analyzing how customers use that catalog is key to optimizing search.

ExpertRec adds a configurable layer of search and product discovery that allows the store owner to control search relevance, searchable fields, filters, and search behavior.

The optimization process can be illustrated as follows:

This provides a more systematic approach to optimization of Shopify AI search rather than assuming that the search works since the products are visible on the screen.

Final Thoughts

An assessment of Shopify AI search cannot only come down to a straightforward question:

Some better questions can include:

• Did it show relevant results?
• Did it display the best products at the top?
• Did the customers click on the results?
• Are critical queries always working effectively?
• Did the quality of search change after database updates?

Successful AI search requires constant monitoring.

Creating a search validation tool, monitoring retrieval and ranking quality, analyzing low-click queries, and conducting search regression tests will allow Shopify merchants to discover problems with product discovery before their transformation into bigger conversion problems.

With ExpertRec, merchants can have configurable search, control over relevance, and detailed analytics for performance measurement and optimization of their product discovery process.

Finally, successful Shopify AI search is not determined by the number of products returned; it is determined by the ability of the customers to find what they need.