Semantic Search: What Is It and Its Impact on eCommerce?
Implementing semantic search can drive higher conversion rates and average order values by improving product discovery and suggesting related items.
Published June 5, 2024.
In this article
What Is Semantic Search?
Who Is Semantic Search for?
The Technology Behind Semantic Search
Benefits of Implementing Semantic Search
Show More
Excelling in delivering an exceptional user experience is essential in eCommerce. Traditional keyword-based search methods have their limitations, often failing to fully understand that capturing the shopper's intent is important to grasp the nuanced desires behind a user's query.
Semantic search is an innovative technology that aims to enhance customers' discovery of products online. This approach goes beyond mere keywords to understand the context and meaning of search queries, offering a more accurate, efficient, and satisfying shopping experience.
Meet the Expert
Jameela Ghann is an eCommerce and marketing expert with more than 10 years of experience. Apart from running her own successful online store, Alora Boutique, she's the marketing manager of Fera.ai.
What Is Semantic Search?
Semantic search is an approach that uses natural language processing (NLP) and machine learning algorithms to understand the contextual meaning behind what someone's searching for.
This method does not focus solely on specific keywords; instead, it examines the relationships between words to deliver more relevant and accurate search results.
This is especially useful when the words someone uses don't perfectly align with product descriptions. It allows for more nuanced search results based on the intended meaning, rather than just exact keyword matches.
Who Is Semantic Search for?
Semantic search is invaluable for eCommerce platforms with extensive catalogs and individuals unsure of their exact needs. It enhances the search experience by focusing on:
- User intent
- Considering synonyms and related terms
- Analyzing user behavior and search patterns
- Natural language understanding
- The relationship between words and phrases
- Attributes and metadata like product descriptions
Semantic search is also helpful for users coming from chatbots or social media because, unlike traditional search engine users, their queries might be more conversational or less precise. Using this approach allows for a deeper understanding of their needs, leading to a more tailored and improved personalized shopping experience.
The Technology Behind Semantic Search
The technology behind semantic search relies on NLP and machine learning, enabling it to understand:
- Spelling mistakes and synonyms
- Speech patterns
- Search queries
- Sentiment
Machine Learning Improvements
Over time, semantic search's machine learning capabilities enable it to become smarter by learning from data and user interactions, resulting in improved search accuracy and better click-through rates. This can help increase the conversion rate of eCommerce businesses.
Named Entity Recognition
Put simply, named entity recognition is the process of identifying separate entities within queries. For example, a search query like "Red high heels New York" could be broken down by semantic search into "New York" as a location and "high heels" as a footwear product, leading to improved merchandising and product recommendations.
Topic Modeling
Topic modeling is similar to building a mind map in order to identify the specifics of what's being searched for even if they aren't explicitly mentioned. For example, if a user searches for "Nike running shoes", effective topic modeling in the context of semantic search might identify related high-volume keywords, such as "arch support" or specific sports like "football".
» Want to take search to the next level? Check out the best practices for eCommerce site search
Benefits of Implementing Semantic Search
Improved User Experience and Product Discovery
Semantic search enhances user experience by helping them discover products more easily, even with spelling mistakes in their queries. This improvement in search functionality not only boosts satisfaction and overall experience but also aids in product discovery.
Example: Canada's Fragrance Buy experienced a 3X increase in overall search shopper conversion rates and a 4X increase in mobile app search after integrating Fast Simon Search. This tool helped overcome challenges like spelling errors by showing customers the best matches and popular searches relevant to their query. Evidently, suggesting related items based on search queries increased average order value, click-through rates, and conversion rates.
Increased Efficiency for Website Managers
Implementing semantic search reduces the time spent on manual product optimization, increasing website managers' efficiency. This not only streamlines workflows but also delivers personalized product recommendations, leading to higher engagement.
Strategic Product Placement and Inventory Management
By adopting a more data-driven approach to product placement and merchandising, you can significantly reduce inventory costs and maximize profitability, ultimately leading to a more sustainable and customer-centric retail environment.
With Fast Simon's Smart Collections, you can automate or manually execute rules within collections, strategically highlighting slower-selling or end-of-season items. This helps clear out inventory more efficiently while also enhancing sales opportunities through intelligent upsells and cross-sells.
Example: Ally Fashion enhanced their product visibility through our advanced Smart Collection capabilities, which enable filtering beyond the common limit of 100 products. After transitioning from basic non-semantic search systems, our customers have experienced significant improvements in search speed, relevance, and feature sophistication.
Practical Advice for Implementing Semantic Search
To implement semantic search effectively, start with a pilot program on a development or clone store. This allows for testing with friends, family, or employees to assess user experience and search relevance without the risk of hurting your real store.
Starting small and gradually scaling up is key to avoiding disruptions to existing themes or workflows. It's also advisable to update your product listings to include commonly searched terms.
Lastly, continuous improvement is important, so conduct A/B tests, monitor search performance, and analyze user interactions to identify areas for enhancement.
» Struggling with search relevance? See our ultimate guide to internal site search
Overcoming Challenges in Implementing Semantic Search
One of the primary challenges in implementing semantic search is the lack of expertise and understanding of how to integrate such solutions effectively for personalization. Partnering with a provider that offers semantic search solutions, along with user-friendly interfaces and strong customer support, can help overcome this challenge.
Another challenge relates to data quality, a vital aspect of a user-focused approach. Great data quality can help you handle customer search behaviors and preferences more accurately. To achieve this, consider the following strategies:
- Ensure accurate, detailed product descriptions
- Refine category structures for better navigation
- Add descriptive alt text for image accessibility
- Incorporating long-tail keywords where appropriate
- Adopt a user-centric design approach
- Increase the reliability of reviews and ratings
- Enhance search functionality with advanced filters
- Improve mobile responsiveness for seamless use on all devices
The Future of eCommerce Search
Semantic search is not merely a passing trend; it represents the future of eCommerce search. By leveraging the power of natural language processing and machine learning to predict customer's needs using personalization, businesses can provide a more intuitive search experience for their customers.
As technology continues to evolve, those who embrace semantic search to improve the user search experience will undoubtedly gain a competitive edge in the ever-changing eCommerce field.
