Discover What You Mean, Not Just What You Type
Finding the right product in an e-commerce store can be frustrating. Traditional keyword searches often miss relevant results because they rely on exact text matches rather than true product similarity.
With Strife and RavenDB 7’s vector search, you can take product discovery to the next level. Instead of just matching words, vector search understands context, meaning, and relationships between products, helping customers find exactly what they need—even if they don’t know how to describe it.
Let’s explore how vector search can transform e-commerce.
Why Traditional Search Falls Short
Most e-commerce search engines rely on keyword matching, which can be limiting. Consider these scenarios:A customer searches for “comfortable running shoes”, but the product descriptions only mention “lightweight performance sneakers”—no match, no results.
A shopper looks for “minimalist leather backpack”, but the best options are labeled as “sleek urban carry bag”—again, the perfect product is hidden.
Vector search solves this by understanding the true meaning behind queries and finding products that are semantically similar, not just those with matching words.
Where Vector Search Shines in E-Commerce
1. “Find Similar” Shopping
A common frustration for shoppers is finding a product they like but wanting slight variations—maybe a different color, material, or price point.
With vector search, you can offer a “Find Similar” button on product pages that retrieves items with similar design, style, or function, even if the product titles and descriptions are different.
Example: A customer loves a pair of white minimalist sneakers, but they want a black version. Instead of manually searching for “black sneakers,” vector search finds visually and stylistically similar shoes with just one click.
2. Image-Based Search
Sometimes, customers don’t know how to describe what they’re looking for—but they have a photo. With vector search, they can upload an image, and the system will return visually similar products.
Example: A shopper sees a stylish leather jacket in a social media post. They upload the image to your store’s search bar, and vector search finds jackets with the same cut, texture, and design, even if the product descriptions don’t mention the exact same words.
3. Smarter Search Suggestions
Vector search helps customers find what they mean, even when they don’t phrase it perfectly.
Example: A customer searches for “cozy winter hoodie”. Instead of showing only products with the exact phrase, vector search returns:
Fleece-lined zip-ups
Oversized pullover hoodies
Thermal sweatshirts
Sherpa-lined hooded jackets
Even if the word “hoodie” isn’t in some product titles, vector search recognizes that these items match the intent of the query.
4. Personalized Product Recommendations
Traditional recommendation engines often rely on purchase history or basic filtering (e.g., “customers who bought this also bought…”). Vector search takes it further by matching products based on deeper similarities.
Example: A shopper browses a modern wooden dining table. Instead of just recommending other dining tables, vector search suggests:
Chairs with a matching wood finish
Minimalist table lamps that fit the aesthetic
Tableware with a similar design language
This leads to a more intuitive and personalized shopping experience, increasing engagement and sales.
The Future of E-Commerce Search with Strife and RavenDB 7
Vector search isn’t just an improvement—it’s a fundamental shift in how product discovery works. By understanding context and similarity, it helps customers find what they truly want, faster and easier than ever before.
With Strife and RavenDB 7, your e-commerce store can deliver:
More relevant search results
Smarter recommendations
A seamless visual shopping experience