The client is one of the world's fastest-growing bath & light brands. They cater to this industry's luxury, premium, and value segments and currently operate in 55+ countries. They also own one of the world's largest chains of branded showrooms in complete bathroom solutions across global markets.
Every year, retailers miss out on over $2 trillion in potential sales in the U.S. alone due to poor online search experiences. Our client faced a similar challenge with their site search experience. Only 10-15% of users searching for products actually made a purchase.
They also lost potential sales due to irrelevant search results. Precise queries like "hand faucets for kitchen sink, 1 meter long, with long flexible tube & wall hook" often displayed unrelated items, and broad queries such as "sink water outlet" or "kitchen faucet" overwhelmed users with too many options.
To address these challenges, the client needed a product discovery solution to:
Our primary challenge was to create a search system that returned comprehensive yet precise results and continued to improve over time. To achieve this, we needed to
We have built a product discovery engine with AI and natural language processing capabilities. This engine can understand user queries and identify the underlying intent. This allows for a more personalized search experience, with more relevant results. This engine also continues to refine its understanding of individual user preferences over time and improves its ability to anticipate their desired results. This has led to more relevant search experiences, increased customer satisfaction, and higher conversion rates.
We have also developed a Recommendation Engine that has significantly enhanced the user experience by improving engagement, basket size, and average order value. As users type their search query, the engine provides intelligent auto-fill suggestions, guiding them towards the most relevant results. It also leverages the user's search intent to dynamically recommend related and complementary products, encouraging them to add more items to their cart. Even if the user's initial search query does not yield an exact match, the engine can provide relevant product suggestions, eliminating the frustration of encountering a "no results found" page.
Data enrichment was required to equip the product discovery engine with the necessary information to deliver relevant search results. We appended the client's catalog dataset with additional attributes, descriptions, categorizations, and metadata. This has provided the AI engine with the context to match products to user queries. User data like browsing history, purchase patterns, and demographics were integrated to help the engine personalize recommendations. We constructed comprehensive taxonomies mapping relationships between products, categories, and concepts, which has provided the AI model with a better semantic understanding. Incorporating external knowledge bases such as glossaries and manuals has further enhanced the engine's understanding of products and terms used in queries.
We assembled a couple of nopCommerce developers (since the client's website was built on nopCommerce, hosted on Oracle cloud) and our AI/ML consultants and developers. This team spent the initial week analyzing the scope and creating blueprints. One we finalized the plan of action, we-
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