eCommerce Search Engine: How Does the Algorithm Work?

Trying to understand the complex inner workings of search algorithms isn't easy—they're often closely guarded secrets. But whether it's Google or an internal eCommerce search engine, most of these algorithms operate on the same basic principles: finding and organizing online content by crawling the web, indexing found content, and ranking it based on search queries.

Knowing what makes your internal eCommerce search engine tick is key for your online store. After all, how can you optimize search rankings and nail personalization unless you know how it works? Below, we'll take a look under the hood of the search engine, breaking down its components so you can maximize your eCommerce shopping experience.

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What Is an eCommerce Search Engine Algorithm?

Modern eCommerce search engines rest on two technologies:

1. Machine learning (ML) creates engines that can learn and make predictions. ML helps engines offer relevant search results, a useful technology in predictive analytics for inventory management, customer segmentation, and demand forecasting.
2. Artificial intelligence (AI) creates algorithms that can do human-like tasks. This means understanding natural language, patterns, and making decisions. AI like chatbots can give personalized product recommendations based on customer behavior.

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1. Indexing and cataloging: This involves cataloging and indexing available products, including their descriptions and categories.
2. Semantic search: Understands the context and relationships between searched words to provide accurate results aligned with the search intent.
3. Natural language processing (NLP): NLP enables search engines to understand more conversational and complex queries, making the search more user-friendly and accurate.
4. Auto-complete and error correction: Auto-complete suggestions predict and complete user queries, offers suggestions, corrects errors, and displays available products at the start of the customer journey.
5. Personalization: By analyzing each user’s search history and behavior, the algorithm provides personalized results, enhancing customer experience and conversion rates.

» Here are the benefits of personalized search

How Does the eCommerce Search Engine Algorithm Work?



The parts of the search engine algorithm are one thing, but the process in action is much more seamless and multifaceted. Let's get you acquainted with the steps getting triggered with each new user and how they build on each other.

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1. It Collects Data

eCommerce search algorithms use a variety of data to generate search results. The main data usually consists of individual user behavior information, such as:

• Clicks
• Conversions
• Views
• Browsing history
• Purchasing history
• Customer demographics

In other words, when you open an account on an eCommerce store and start clicking, viewing, or buying products, that's when the data-collecting magic starts.

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But your decisions and preferences are only half of the story. The search engines need to ensure you get the right products. To that end, algorithms also consider product data so there is a high relevance and a good match to the search query. Product data includes:

• Keyword density
• Title tags
• Meta descriptions
• Product descriptions
• Content quality

The combination of user behavior and product data translates into relevant and effective search results​​​​.

» Get a good start with eCommerce product data enrichment

2. It Ensures Relevance



Search algorithms also factor in profitability by considering elements like pricing and profit margins, so businesses can meet KPIs alongside customer satisfaction.

Popularity of products is often assessed by:

• Views
• Purchases
• Customer reviews
• Ratings

In addition to all of the above, stores often use A/B testing to evaluate and improve the effectiveness of these algorithms​​. In A/B testing, two versions of algorithms are tested with segmented audiences to see which algorithm yields better results.

» Here are our best tips for segmenting your audience

3. It Analyzes Behavior and Preferences

With enough data—previous searches, click-through rates, purchase history, and feedback on search relevance—eCommerce search algorithms can observe customer behavior and preferences, helping them personalize the shopping experience for each user.

For example, if a user has previously searched for and bought a raincoat, the algorithm can use this data to recommend similar or complementary products during other searches.

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Strong customer behavior analytics results in fewer cart abandonments, higher conversions, and better customer satisfaction.

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4. It Personalizes

Personalization in eCommerce search engines works by analyzing individual user data, like search history, clicks, and purchases, to tailor search results for each user.

» Learn more about the benefits of AI in personalization

But the personalized recommendations can go beyond the scope of products. Search engines can also include upselling & cross-selling or informative and educational content on topics you like, such as:

• News
• Blog posts
• Articles
• Videos
• Product reviews

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This personalized approach creates a more intuitive and satisfying search experience for users, leading to better engagement and potentially higher sales​​.



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Types of Queries Handled By eCommerce Search Algorithms

Efficient search engine algorithms understand the different types of queries that users might search for in order to better index products and improve product discovery. The main queries eCommerce customers might search for include the following:

1. Product Types (General Searches)

Product type searches, also called "category searches", are general commercial queries customers might use when they have identified a need for a product or tool but are unsure which specific brand or model they might need. Understanding these queries is one of the most important parts of eCommerce search algorithms as they can provide customers with a list of different products to solve a potential need and offer them the chance to compare brands and items for themselves.

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2. Brand/Model Names (Exact Searches)

Exact searches are specific commercial queries customers might use when they want to make a purchase and have already researched different products to find out what works best for them. This could be brand names or even specific models of certain products.

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3. Problem Solving Searches

These are even less specific commercial or informational queries that a customer might use when they've identified a problem but aren't sure what the solution is. Although the customer may still be at the top of the sales funnel and not necessarily looking for a product, it's essential that the search algorithm understands the intent behind these queries as customers may be influenced to make a purchase if they a find a product that solves their problem.

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4. Non-Product Searches

These queries are not related to products at all. Instead, they are navigational queries that customers might use to get around your site if they are having difficulty with your menus. Examples include "shipping fees", "returns policy", and "about {company name}".

Although it's not essential that your search algorithm can handle these searches, having this functionality can improve the customer experience.

» Ready to improve your eCommerce search functionality? Book a demo with us now

How to Ensure the Success of Search Engine Algorithms

eCommerce search algorithms get updated regularly to adapt to changes in user behavior, market trends, and tech advancements. When you're updating your search functionality, be sure to implement the following strategies:

• Improve product descriptions and metadata: Use relevant keywords throughout your product descriptions, titles, and metadata that accurately reflect the product's features and benefits. This can help with all types of customer queries as the search algorithm can better understand a a product's features and the problems it might solve.
• Utilize product categories and tags: Making sure your products have the right tags and are nested under broader categories and collections can help the search algorithm understand which products to show customers who utilize general queries.
• Implement filters: Although you want your search algorithm to be able to push customers in the right direction towards your best sellers, you should still improve the customer experience by allowing them to filter and sort those results based on popular methods like pricing, brands, color, and even type (such as wired or wireless keyboards).
• Internal linking: Not only does linking products together help customers navigate your store and find related products, but using the right anchor texts and carefully selecting which pages should link together, you can help your search algorithm understand which products are related and why.
• User behavior analysis: Making use of AI to track user behavior can help your algorithm understand how customers typically navigate through products, which products are generally purchased together, and which searches don't yield good enough results.

» Learn about the best tools and strategies in our guide to eCommerce product search engines

Continuous monitoring and updating are essential to maintain relevance, effectiveness, and success of the search engine algorithm. Don’t forget to track key internal search metrics to stay on top of your website's performance. The most important ones are:

• Conversion rates
• Click-through rate (CTR)
• Time spent on search results page
• Customer satisfaction and feedback 

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Common Challenges With AI Search Tools

• Balancing search speed and results accuracy: Speed is crucial for user satisfaction, but not at the cost of delivering irrelevant results​​.
• Personalization vs. privacy: Collecting and using user data for personalization needs to be balanced with maintaining user trust and complying with privacy regulations.
• Adapting to user behavior and preferences: Algorithms need to always be updated and refined to stay relevant and effective in an ever-evolving market​​.

» Start with best practices for eCommerce site search

From Keywords to Key Trends: Upcoming Search Engine Algorithms

Search algorithms play a big role in eCommerce. They shape the customer experience and can make or break your business.

AI-driven search engines are now leading the pack. They use important analytical data and internal search metrics to make smart tweaks to the search experience. Use AI solutions to analyze customer data for more personalized search results.

Expect to see more AI-enhanced personalization in the future. Also, keep an eye out for visual or voice-based search experiences.

» Book a demo to start leveraging the power of AI eCommerce site search tools