Semantic Search and Discovery for Online Commerce Revenue Growth

In the fast-paced world of online retail, ecommerce organizations and platforms are constantly seeking ways to enhance the shopping experience. Text search and discovery have become pivotal elements in connecting customers with the products they desire. As consumer expectations evolve, ecommerce marketing strategies must adapt to provide more intuitive and efficient ways for shoppers to find what they’re looking for.

This blog explores the transformative impact of semantic search on ecommerce success. It delves into the mechanics of building a robust search engine, highlighting the importance of understanding user intent. The article also examines methods to measure the effectiveness of semantic search implementations. Additionally, it touches on the growing trend of multi-modality search experiences, offering insights into how these advanced technologies are shaping the future of online shopping.

Semantic Search: Revolutionizing E-commerce Discovery

Semantic search in eCommerce is a search approach that focuses on understanding the meaning and context of user queries rather than solely relying on keyword matching. With semantic search, the engine goes beyond the specific terms and comprehends the customer’s intent. For instance, if a user searches for “comfortable running shoes,” the semantic search engine recognizes that the user is looking for running shoes that prioritize comfort.

It intelligently presents a range of products that meet the customer’s criteria, even if they don’t explicitly mention “comfortable” in their search query. By leveraging semantic analysis, the engine understands the underlying context and delivers relevant results, ensuring a more accurate and satisfying shopping experience.

How Semantic Search Works

Semantic search is a type of AI-powered search. It employs natural language processing (NLP) and artificial intelligence techniques to comprehend user queries in a human-like manner. By considering context, word relationships, and overall meaning, semantic search delivers highly accurate and relevant results, enhancing the search experience with its advanced understanding of user intent.

To understand how semantic search works, you need to know about vector embedding. In this process, the search data and its context are represented in a numeric format. This representation is called vector embedding. The vector then uses the kNN algorithm to find the content that is in the closest proximity of the search data.

Advantages Over Traditional Search

Semantic Search and Keyword Search differ in their approach and output.

Semantic Search utilizes AI, natural language processing, and context analysis to understand query meaning and intent, while Keyword Search relies on exact keyword matching and basic word-matching algorithms.

Semantic search focuses on understanding context and intent, delivering more precise results, while Keyword search relies solely on matching keywords, potentially leading to less accurate results.

Keyword matching has long been a staple of any website or app with a search bar, but it is defined by its limitations. A user searching for a specific keyword will typically only be presented with products that contain that exact keyword, leaving out words that might be synonyms or slightly different phrasing. Semantic search came as a solution to this problem, which is a more contextual approach to understanding the search intent of the user.

Building a Robust Semantic Search Engine

Data Preparation and Indexing

Relevance is the core part of information retrieval. A relevant search result is one that provides what the user is searching for. To address relevance issues, researchers propose retrieval models – formal representations of the process of matching a query and a document. A good retrieval model finds documents likely to be considered relevant by the user who submitted the query.

One popular model is TF-IDF, which aggregates contributions from individual terms but ignores phrasal or proximity signals between query terms in the document. Another is the bag-of-words model, which calculates the probability of generating the query words from the document’s “bag of words,” modeling topical relevance.

Query Understanding and Processing

Semantic search algorithms have complex architectures integrating machine learning branches like NLP, question answering, and knowledge graphs. When a query is received, NLP techniques like tokenization, part-of-speech tagging, and dependency parsing break it down and analyze relationships. Word embeddings map words with similar meanings close together, aiding context understanding.

The algorithm then performs semantic analysis – word sense disambiguation, concept extraction, query expansion to include synonyms and related terms . Entity recognition identifies and classifies key query components like people, places, and products, facilitating accurate results . Contextual understanding ascertains meaning and intent by looking at words, search history, and trends .

Ranking and Relevance Algorithms

Some retrieval models focus on topical relevance, but deployed search engines must incorporate user relevance in ranking algorithms. Evaluation metrics like precision (proportion of retrieved relevant documents) and recall (proportion of relevant documents retrieved) assess how well results match user expectations.

The current evaluation focuses on clickthrough data. Adversarial information retrieval deals with spam—misleading or irrelevant information meant for commercial benefit. Learning-to-rank (LTR) models represent query-document pairs as feature vectors and learn to combine features for optimal ranking through discriminative training.

Knowledge graphs organize information as interconnected entities and relationships, helping understand query context. Machine learning algorithms analyze user interactions and feedback to continuously improve result relevance.

Measuring the Impact of Semantic Search

Key Performance Indicators

Measuring the impact of semantic search implementation is crucial for understanding its effectiveness and making data-driven decisions. Several key performance indicators (KPIs) can provide insights into how consumers interact with your ecommerce platform and the value delivered by semantic search. These include:

⦁ Exit Rate: This metric refers to the percentage of visitors who leave the page after completing a search. A high exit rate could indicate that users are not finding relevant content, leading to less traffic and conversion on your site.

⦁ Conversion Rate: Conversion doesn’t necessarily mean purchasing a product; it can also include actions like adding to a wishlist or cart or clicking an ad. A higher conversion rate signifies successful content and marketing efforts, potentially indicating high customer satisfaction.

⦁ Bounce Rate: While often confused with exit rate, bounce rate measures the percentage of visitors who leave your website immediately after entering, without browsing further. This could indicate that users misclicked or realized it wasn’t the website they were looking for.

⦁ Click-Through Rate (CTR): CTR is the ratio of users who click on a specific link or call-to-action (CTA) to the total number of users who view a particular page. A low CTR may suggest a lack of interest or unclear CTAs.

⦁ Average Order Value (AOV): AOV measures the average amount spent per transaction. Tracking AOV can help determine critical business decisions, such as ad spending, store design, or price points.

⦁ Customer Retention Rate: This metric represents the number of customers who continue to do business with you over a certain period, providing insights into customer satisfaction and areas for improvement.

⦁ Cart Abandonment Rate: This rate measures the percentage of customers who add items to their cart but leave without completing the transaction, indicating potential friction in the checkout process.

⦁ Customer Lifetime Value (CLV): CLV measures the total revenue generated by a customer, allowing you to predict how much they will spend with your business over time and focus marketing efforts accordingly.

⦁ Mobile Conversion Rate: Tracking mobile conversion rates helps measure the mobile shopping experience, allowing you to identify and address any pain points for mobile users.

A/B Testing Strategies

A/B testing is a critical tool for improving search products and measuring the impact of changes. However, there are various approaches to A/B testing for search, each with its own implications.

⦁ Scope of A/B Tests: It is recommended to scope A/B tests by the search queries they affect. This allows for testing targeted improvements on a fraction of search queries, which can yield higher returns on investment (ROI) than testing broader changes across all queries.

⦁ Test Targets: The target set for an A/B test determines how long the test needs to run before achieving statistical significance. More aggressive targets, such as doubling conversion rates, require shorter test durations but are more challenging to achieve. Smaller, incremental improvements may have a higher ROI by targeting specific query subsets.

⦁ Test Analysis: When analyzing A/B test results, it is crucial to consider the scope in terms of search sessions rather than individual queries. This prevents scenarios where improvements on targeted queries come at the expense of performance on other queries within the same session.

⦁ Rapid Testing: To maximize ROI, it is recommended to test and deliver rapid, targeted improvements by keeping the scope of A/B tests as narrow as possible while considering potential effects on behavior within search sessions and long-term searcher behavior.

Long-term ROI Analysis

While A/B testing provides insights into the immediate impact of semantic search implementation, it is also essential to analyze the long-term return on investment (ROI).

⦁ Revenue Impact: Companies like Rakuten have reported a 5% increase in gross merchandise sales (GMS) from implementing semantic search, attributing the improvement to a deeper understanding of user intent and inventory.

⦁ Business Benefits: Semantic search can provide other business benefits by combining natural language processing (NLP) with user-friendly interfaces. It makes it easier for users to find accurate results quickly, which can inform decision-making and boost productivity across various business sizes.

⦁ Data Unification: Semantic search can unify unstructured data from diverse sources, drawing insights that drive business growth and development.

⦁ Smooth Implementation: For successful implementation, businesses need to clearly understand the desired outcomes and ensure that the search engine is fed appropriate data in the right quantity.

It is important to note that while semantic search promises significant benefits, careful planning, data preparation, and continuous monitoring are essential for realizing its full potential and maximizing long-term ROI.

Case Studies of Successful Implementations

Major e-commerce players like Rakuten have successfully implemented semantic search, with impressive results. Rakuten saw a 5% revenue boost from providing more relevant recommendations.

“It’s because we have a deeper understanding of both our users and our inventory,” explains Xiong. “Now that our results have become a lot more relevant to what users want, they are able to find what they need and make the order.”

Semantic search provides a more intuitive and contextual approach to understanding customer queries. Instead of relying solely on keyword matching, semantic search leverages AI and natural language processing to comprehend the true intent behind searches. This has an influence on delivering highly relevant product recommendations, even when queries don’t exactly match product descriptions.

One key advantage of semantic search over traditional keyword-based approaches is its ability to handle synonyms, misspellings, and different phrasings. A user searching for “comfy running sneakers” would still be matched with relevant “comfortable running shoes,” thanks to semantic understanding.

The technology works by converting queries and product data into numeric vector representations and then finding the closest matches in that vector space. This allows for nuanced conceptual comparisons beyond just literal text overlaps.

ViSenze offers an AI-powered multi-search platform. Our technology allows retailers with multiple discovery experiences across the consumer journey to further improve conversions and revenue

Conclusion

Semantic search is causing a revolution in the ecommerce world, transforming how customers find and interact with products online. By grasping the context and intent behind user queries, this technology delivers more relevant results, enhancing the overall shopping experience. What’s more, the rise of multi-modal search, which combines text and visual inputs, is opening up new possibilities for intuitive product discovery.

As ecommerce shopping behaviors continue to evolve, implementing and fine-tuning semantic search capabilities will be crucial for staying competitive. The potential benefits, from increased conversion rates to higher customer satisfaction, make it a worthwhile investment for online retailers of all sizes.

To see the power of multi-modal search in action, Try ViSenze’s Multi-Search Demo and experience firsthand how it can transform product discovery.