A very nice product in a Shopify store won’t necessarily mean a search engine will give visitors good search results. The reason for poor search results may not always be connected to the search bar.

Sometimes, the problem may start much earlier, with product information.

For instance, if a potential customer makes a search request:

“waterproof black hiking shoes less than 1000,”

this is not a single keyword request.

Actually, this request consists of different keywords:

• Product type: hiking shoes
• Feature: waterproof
• Color: black
• Price limit: less than 1000

If an online Shopify store keeps the data inconsistent across titles, descriptions, tags, and meta descriptions, even the most advanced search engine will have a problem.

This is why, when it comes to optimizing Shopify for AI search, it is important not to start with adding more keywords.

It is more important to understand how the product discovery process works:

In this article, it will be demonstrated how Shopify sellers can prepare their shops for AI search technically, as well as where ExpertRec fits into this process.

1. Treat Your Product Data on Shopify as a Search Schema

Merchants tend to see product data as something to be used for creating their product pages.

In reality, product data has more of a function as a search schema than it has to do with creating content.

Product data can include the following types of information:

• Product name
• Product description
• Product category
• Brand
• Collections
• Tags
• SKU
• Variants
• Price
• Availability
• Custom metatags

The main question isn’t whether the information is there. It is whether the search engine can really use the information for retrieval or filtering purposes.

For example, let’s talk about a laptop bag.

If a store relies only on product titles for search purposes, all the product data in its catalog simply goes to waste. ExpertRec gives merchants the possibility to set up searchable product attributes in their panel, such as search titles, SKU data, categories, and tags.

2. Normalize Product Attributes Before Optimizing Search

Data inconsistencies rank among the most ignored AI challenges.

For instance, consider the case of three products.

A → Color = Blue

B → Colour = Navy

C → Shade = Midnight Blue

The customer could search for the term:

“blue shoes”

But this inconsistency creates confusion.

The following cases represent the same problem:

• cm vs inches
• Men’s vs Mens
• 15 inch vs 15-inch
• Water resistant vs water resistant
• Laptop bag vs laptop case

Before launching into search optimization, merchants need to solidify their attribute structures.

For example:

Attribute: Colour

Values:

• Black
• White
• Blue
• Navy
• Red

Thus, customers would only have to work with one attribute structure in the database.

This is a more efficient approach since it separates between:

Product data → What it is.

Customer terminology → How the customer describes it.

This separation is crucial in configuring an AI-driven search system

3. Configure Fields for Different Search Jobs

Not every product subclass has the same value.

A product name, SKU, and detail have different weights in a search process.

Thus, a more successful search process will differentiate between fields according to their function:

Searchable fields

Help to find a product.

• Product name;
• Product detail;
• SKU;
• Brand;
• Tags;
• Custom features.

Filterable fields

Help to limit the number of all relevant results.

• Price;
• Size;
• Color;
• Material;
• Availability

Ranking fields

Help to organize the results.

There can be an example of matching the title to the query that can be more valuable than matching the description but not the title itself.

Hence, search weighting is in play. ExpertRec gives configuration of the search fields and uses weighting in the search process, enabling merchants to decide which product features take part in search and how important they are in the search order.

The task is not to give high importance to every field. With the equality of the importance of everything, there’re not many mechanisms for assessing search features.

AI-powered Shopify search & merchandising

Optimize Shopify search with ExpertRec

ExpertRec helps Shopify merchants make product data more searchable with intelligent search, autocomplete, typo tolerance, customizable search fields, filters, ranking controls, and search analytics helping shoppers find relevant products faster.

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Quick Shopify integration • Search field controls • Search analytics

4. Understand the Difference Between Retrieval and Ranking

When these two processes are separated, it becomes easier to understand what search optimization involves.

The retrieval process is about asking:

Which products might match this search?

The ranking process is about asking:

Which of those products should be shown first?

For instance, if someone searches for wireless headphones, the search will yield 150 different headphone products. However, not all of them are equal.

The ranking can go through many factors such as relevance signals and business priorities.

For example, a product may be relevant because:

• Title matches search
• Product features match the use case
• Product is available to purchase
• Product matches weighted ranking
• Product is promoted

ExpertRec’s search configuration includes search weights and customizable search and merchandising capabilities, allowing Shopify merchants to control how different product information influences result relevance.

5. Convert Complex Queries Into Product Constraints

Optimizing AI search becomes vital if shoppers no longer use short keywords.

Let’s make an example of the following search request:

“black office bag for 15-inch laptop for a price under $80”.

The request includes several constraints:

• Type → Office bag
• Color → Black
• Compatibility → 15-inch laptop
• Price → Under $80

The challenge here lies in ensuring that these characteristics can be found in the product catalog in the right format.

When “15-inch laptop compatibility” is mentioned just once in a long description that has 500 words, you might think that the product has less structured information about compatibility compared to having a separate metafield or attribute for it.

It means that when optimizing Shopify AI search, it’s important to incorporate query-to-attribute analysis.

Take a look at the most frequent customer queries and analyze the query for product attributes.

For example:

This is the Product Understanding Gap that appears between the way people describe products and the format of the information about them in the Shopify catalog.

6. Use Zero-Result Searches as Data Diagnostics

A zero-result search does not mean the search engine has failed.

Rather, it might be a signal about a data issue.

For example, say customers search for “bag for 16-inch laptop” and get no results.

Some of the issues that may arise are:

1. There are no products available for that size of laptop.
2. There are products available, but the information about compatibility is lacking.
3. There is enough information, but it is not searchable.
4. Customers use different terminology than what is used in the catalog.

Thus, a cycle for optimization comes into being.

Through analytics by ExpertRec, merchants can learn what their customers are looking for in terms of search queries and assess the searches that do not yield useful results.

Where ExpertRec Fits Into Shopify AI Search Optimization

Shopify acts as a hub for the product catalog, while ExpertRec acts as an AI inbound layer for customers accessing the catalog.

This means that for store owners, using AI for search optimization is a constant process of enhancements of:

• Product parameters;
• Searchable parameters;
• Filters;
• Search priorities;
• Coverage of queries;
• Zero-result searches;
• Product search performance.

ExpertRec provides features for search setup and analytics in the Shopify search engine, including settings of search parameters, different kinds of filters, and search prioritization.

Final Thoughts

The optimization of Shopify for AI search does not mean the inclusion of the term “AI” in the name of your shop or simply the usage of as many keywords as possible in product descriptions.

The essence is much more practical.

The effectiveness of the searching process depends on whether the information about the product is organized, searchable, and fits in the language the customers use.

You should start by checking the current product characteristics. After that, indicate what particular fields will be searchable, filterable, and significant for ranking. It is necessary to analyze the users’ queries for unset characteristics and to treat no-result requests as diagnostics of the catalog.

If the Shopify system and search system are well integrated, it will provide a better basis for AI search realization.

ExpertRec provides the possibility of controlling the search performance for those Shopify stores that would like to have such an opportunity.