eCommerce Search Engine: How Does the Algorithm Work
Get a close look at the core functions of eCommerce search engine algorithms to optimize your customers' shopping experience.
Published February 7, 2024.
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, most of these algorithms operate on the same basic principles. They find and organize online content by crawling the web, indexing found content, and ranking it based on your search query.
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.
» Find inspiration in these tried-and-tested searchandising strategies.
Meet the Expert
What Is an eCommerce Search Engine Algorithm?
An eCommerce search engine algorithm is a tool that helps customers find products on a website using keywords. It uses advanced algorithms to deliver relevant results based on what the user is searching for.
Modern eCommerce search engines rest on two technologies.
Machine learning (ML) creates engines that can learn and make predictions. ML helps engines offer relevant search results. This technology is useful in predictive analytics for inventory management, customer segmentation, and demand forecasting.
Artificial intelligence (AI) creates algorithms that can do human-like tasks. This means understanding natural language, patterns, and making decisions. Thanks to AI, chatbots, for example, can give personalized product recommendations based on customer behavior.
These technologies are used in five key components of any search engine algorithm:
- Indexing and Cataloging: This involves cataloging and indexing available products, including their descriptions and categories.
- Semantic Search: Understands the context and relationships between searched words to provide accurate results aligned with the search intent.
- Natural Language Processing (NLP): NLP enables search engines to understand more conversational and complex queries, making the search more user-friendly and accurate.
- Auto-Complete and Error Correction: Auto-complete predicts and completes user queries, offers suggestions, corrects errors, and displays available products at the start of the customer journey.
- Personalization: By analyzing each user’s search history and behavior, the algorithm provides personalized results, enhancing customer experience and conversion rates.
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.
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:
So, in other words, when you open an account on an eCom store and start clicking, viewing, or buying products, that's when the data-collecting magic starts.
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
- Content quality
The combination of user behavior and product data translates into relevant and effective search results.
If a user often clicks, buys, or searches for "organic products", the system will give higher relevance to products tagged as "organic", "sustainable" and "eco-friendly."
The next time when the user searches for "snacks", the algorithm will likely push organic and eco-friendly snacks to the top of search results.
2. It Ensures Relevance
Search engines use ‘word embeddings’ to achieve accurate and relevant results. This means they learn about products not just by individual words, but by their relations to other words.
Take a fashion eCommerce store as an example. The system learns that ‘winter jacket’ is more closely related to ‘boots’ than ‘swimwear’. So, if you search for a winter jacket, the system is more likely to suggest boots than swimwear.
Popularity of products is often assessed by:
- Customer reviews
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.
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.
This information helps them personalize the search results 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.
Besides picking a product the user will most probably like, the algorithm also saves time of not having to go through a bunch of other products, and offers deals on relevant articles.
Strong customer behavior analytics results in fewer cart abandonments, higher conversions, and better customer satisfaction.
4. It Personalizes
Personalization in eCommerce search engines works by analyzing individual user data, like search history, clicks, and purchases, in order to tailor search results for each user.
For instance, an AI-based search engine might use a user's past behavior to suggest products that fall within their preferences. Or it might adjust the search results based on the user's previous interactions with the website.
But the personalized recommendations can go beyond the scope of products. Search engine can also include upsells, or informative and educational content on topics you like, such as:
- Blog posts
- Product reviews
This personalized approach creates a more intuitive and satisfying search experience for users, leading to better engagement and potentially higher sales.
AmerCareRoyal is a disposables supplier that uses Fast Simon for B2B search, merchandising collections, and personalized product recommendations.
The search algorithm for product recommendations includes personalized upsells based on customer behavior, resulting in higher AOV and conversion rates.
How to Further Ensure Success of Search Engine Algorithm
eCommerce search algorithms get updated regularly. They adapt to changes in user behavior, market trends, and tech advancements.
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
Optimizing your search algorithm and introducing AI-driven search results can do wonders for your business, including:
- Improved Site Ranking: Effective search algorithms promote a business's website ranking on search engines, so it is more likely potential customers would find them.
- Enhanced eCommerce Experience: Search algorithms help guide customers to products they are most interested in, improving their shopping experience and increasing sales.
- Increased Visibility on Social Media: By understanding the algorithms of social media platforms, businesses can achieve higher visibility for their content and marketing efforts.
- Informed Business Decisions: Sophisticated algorithms, especially those incorporating AI, can provide insights for future business and merchandising strategies, helping in prospecting new business and identifying customer churn patterns.
Direct Bed used Fast Simon's advanced search for shoppers to find products easily. Customers were able to navigate through the catalog seamlessly and see variant products that would have otherwise remained hidden.
Optimized search bar and advanced filters resulted in better conversion and higher overall customer satisfaction.
Common Misconceptions and Challenges
When it comes to AI search engine algorithms, two common misconceptions can cause a lot of misunderstanding:
- eCommerce search engines function exactly like conventional search engines, like Google. They don't. In reality, eCommerce search engines have unique requirements designed for product discovery and user experience.
- Adding more filters always improves the searching. Filters are crucial for narrowing down results. However, overwhelming users with too many options can confuse them and make it a negative experience.
In addition to these two misconceptions, you should note the challenges that come with AI search tools. These need to be considered carefully:
- 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.