Table of Contents

1. A Customer Query Is an Input, Not a Final Search Instruction
2. Query Processing Determines What the Customer Is Looking For
3. Retrieval and Ranking Are Different Technical Problems
4. Search Relevance Requires Multiple Signals
5. Filters Should Reduce Search Space, Not Replace Relevance
6. Search Analytics Shows Where the Search Pipeline Breaks
7. Build a Continuous Search Optimization Loop
8. Where ExpertRec Fits Into the Shopify AI Search Pipeline
9. Final Thoughts

A customer searching on the Shopify website types in the query:

“comfortable office chair for back pain under 3000 rupees”

Normal methods of site search usually try to find products that have similar words.

The customer has done more than just searching for one meaningful word.

The given query has some facets:

• Product: Office chair
• Application: Long working hours
• Criterion: Comfort
• Issue: Back pain
• Limitation: Under 3000 rupees

In case of a Shopify shop that has hundreds or thousands of items, the problem is not just determining that there are office chairs.

The task is to understand:

What should be retrieved, what should be left out, and what should come to the surface. Making the most of Shopify with AI search doesn’t stop with having the right product data. Merchants must also know about the steps taking place between user requests and the results of the search.

A simplified process of searching can be described as follows:

Now let’s consider every stage of the process.

1. A Customer Query Is an Input, Not a Final Search Instruction

Shoppers do not often input search terms that reflect the product name exactly the way it is stated in the catalog of Shopify.

For example, the merchant sells a model of the product that is called:

‘ErgoFlex Adjustable Mesh Task Chair.’

But a customer searches:

• chair for working from home
• chair that provides comfort for hours
• office chair for back aches
• ergonomic chair that costs less than $300.

These examples do not share identical terms as the one used in the product title.

Thus, starting from recognizing how the customers express their needs, one can arrive at a well-designed search optimization strategy.

Complex queries can have several components:

comfortable office chair for back pain under $300

• Product type – Office chair
• Feature – Comfortable
• Use case – Working
• Requirement – Back support
• Price constraint – Less than 300 USD.

At this point, it becomes the responsibility of the search system to introduce available product details to determine relevant options, which is why search query optimization is directly related to catalog optimization, as it is impossible to make a proper match between requirements and characteristics of the product, which can be missing, inaccessible, or poorly structured in the underlying catalog.

2. Query Processing Determines What the Customer Is Looking For

The search system must analyze the customer’s input before displaying the results. Let us use the search request “black waterproof shoes for hiking” as an example.

In this case, the search request includes:

• Search category: Shoes
• Color: Black
• Feature: Waterproof
• Purpose of use: Hiking

It is essential to ensure that not all of the words in the search request are treated the same way. For instance, the word black included in the information about a certain product can’t guarantee the relevance of this footwear. The task is to find the product candidates that meet only the most significant conditions of the query.

To ensure that, ExpertRec offers semantic and artificial intelligence-based search capabilities for ecommerce search. These are of great importance for Shopify platforms and online stores. With it, they can always get the most relevant product results according to the queries provided by the customers.

The search behavior can be adjusted via introduced rules and strategies to improve the relevance of the results offered.

3. Retrieval and Ranking Are Different Technical Problems

A major error in ecommerce search optimization is viewing search as a single task.

Instead, it is more effective to divide it into two parts.

Retrieval

The retrieval process tells you:

What products match this inquiry?

For example, in the case of

“wireless headphones for video calls”

the system will retrieve a wide range of products again.

Ranking

The ranking phase answers the question:

Which items of these retrieved ones should be displayed first?

The whole workflow looks like this:

The search system can deliver the relevant results, but the product presentation may be poor because some good products can appear at the end of the results list.

Therefore, optimization of ranking is as important as optimization of retrieval.

4. Search Relevance Requires Multiple Signals

The relevance of a search result in Shopify does not depend on a single product field.

While a product can have a match in the title of the query, it may:

• Be out of stock
• Have a key feature missing
• Have a lower relevance ranking
• Not be suitable for the required purpose

Multiple signals must be used to determine search relevance.

In theory, the process of ranking may include:

The exact configuration will depend on the shop and the search solution used.

For example, an electronics shop may give more priority to attributes of exact compatibility while a clothing shop may give more importance to the category, color, or style of a product.

ExpertRec provides configurable weights and relevance controls for search that allow merchants to determine how product fields should contribute to search results, thus enabling greater flexibility for Shopify stores.

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5. Filters Should Reduce Search Space, Not Replace Relevance

Search relevance and filters apply different approaches.

Search relevance identifies valuable products.

Filters assist in limiting choice.

For example:

“Bluetooth headphones”

The search engine could return 700 results.

To limit results, the customer can filter search results with:

• Price
• Brand
• Performance
• Color
• Variants

However, filters should not take over the function of poor search results.

If the customer searches for:

“noise suppression headphones up to $150”

the search must recognize the important specifications and price restrictions automatically, instead of making the customer modify search queries repeatedly through filters.

Hence, it is necessary to combine the three processes of query processing, product specification, and filtering.

According to ExpertRec, Shopify merchants can set up searching filters and faceted navigation according to the available catalog data.

6. Search Analytics Shows Where the Search Pipeline Breaks

The best search optimization information frequently comes from consumer behavior.

A retailer may assume that a search term is effective simply because it yields results.

However, producing results does not guarantee that the search was successful.

Take this example:

While this example is not a zero-result issue,

it is instead a relevance issue.

Possible implications are:

• Critical products have a low ranking.
• The leading products are not waterproof.
• The terminology used by the consumer is different from that used for the product.
• The necessary attributes of the product are lacking.
• The importance of the search field is incorrectly set.

For this reason, the shop owner has to pay attention not only to zero-result queries.

Valuable indicators include:

• Frequently searched phrases
• Queries with no results
• Queries with a low number of clicks
• Popular products
• Search results
• Trends of conversions after search

ExpertRec developed search analytics to help merchants study consumers’ search behavior and use data for enhancing product discoverability through search.

7. Build a Continuous Search Optimization Loop

The optimization of search on AI cannot be understood as a one-off action done in Shopify.

The language of customers is changing.

New items are being introduced into the catalog.

The products’ availability might differ.

Seasonal queries are the new normal.

Search configurations that have been effective last year are not likely to work today.

Thus, a rather complicated optimization process looks as follows:

For instance:

Issue

Customers are constantly looking for the following request:

“desk for small flat”

Diagnosis

There is a product matching such a request, but it is too problematic to find “small space” issues in the catalog with its help.

Optimization

Provide relevant information on dimensions, as well as the way the product can be used.

Measurement

Watch the increase in the number of clicks the item receives after the above-mentioned changes have been applied.

Thus, the search optimization process transforms from love for a chance to a process based on actual customer behavior.

Where ExpertRec Fits Into the Shopify AI Search Pipeline

Shopify is responsible for developing e-commerce infrastructure and product catalog.

However, ExpertRec has provided a configurable search and product discovery architecture that is essential for merchants to manage the interaction with their catalog.

The process can be summarized in the following way:

As such, the merchants using Shopify can employ a non-static approach to search.

This means that they can analyze customers doing searches and use it to improve product discovery.

ExpertRec offers functions including AI search, semantic search, configurable relevance, filtering, merchandising, and analytics, which allow merchants using Shopify to implement a more sophisticated product search experience.

Final Thoughts

Shopify AI search’s technical hurdle lies not only in analyzing the customer’s query, but also in determining what to do next. A good search user interface calls for several processes to function seamlessly together that include:

• delay elimination
• candidate search
• product matching
• responsiveness score calculation
• ranking
• filtering
• behavioral analysis.

The first step to take is to ensure that the Shopify database aims to put together the information needed for successful searching. The appropriate measures can be undertaken afterwards to ensure that customers get the appropriate results they are looking for and find what they need using the simple search queries.

ExpertRec allows you to use customizable searching, usefulness monitoring, filtering, and analysis possibilities, greatly improving your search process. They say that Shopify search optimization is rather a state-of-the-art system than a standard feature that comprises the following elements: Query – Retrieval – Ranking – Customer Behaviour – Optimization.