Why Are Most Ecommerce Searches So Flawed?
When it comes to ecommerce search, most websites focus on SKU (Stock Keeping Units) to manage their catalog, as this is the logical component that can be bought by the user. However, users don’t look for SKUs: users search for products and services that they need, describing the attributes and characteristics.
Search queries with 3 or more words are considered “Long Tail” and online retailers invest a respectable amount of money trying to find the keywords that would deliver what the customer is looking for, but this “Long Tail” is really the external form of a much more complex structure, that we could modeled as:
“Popular product name” + “Features”
Features and product names can appear in any order, making an ecommerce search particularly challenging.
Franklin Planner’s Ecommerce Search Saw a 35% Increase in Click-to-Cart Conversion
“FranklinCovey Organizational Products recently partnered with Inbenta as we were looking for a new search tool that would enhance customer experience on our website. Inbenta has been one of the best and easiest partners that I have ever worked with. When we were going through a presentation of their software, they told us that the implementation was simple and only required two lines of code to get underway. I was skeptical, but they were true to their word. The implementation met our project deadlines and has exceeded my expectations.Even though we only recently deployed Inbenta – we already have proven positive results such as 20% conversion from search to cart and 35% click to cart efficiency. The most impressive aspect of the implementation was their follow up. The Inbenta team was responsive to all of our requests and questions and the turn-around time was within 24 hours.”
Vice President, Ecommerce
FC Organizational Products
How Does Natural Language Processing Help?
Natural Language Processing looks at the overall meaning of the search query instead of individual keywords. Thanks to the use of these NLP theoretical frameworks and computer models, Inbenta has been able to create a Semantic Search Engine, which allow their users to efficiently search for complex information using incomplete, ambiguous, unstructured questions in their own [natural] language.
The “Search to Cart” Rate
“Search to Cart” rate or S2C rate is computed based on how many search results end up in the shopping cart.
It is computed as: s2c = cart / search where “cart” is the total number of products shown in the search results page that where put in the shopping cart divided by the total number of search requests in the product search engine.
Using Natural Language Processing we have measured a great increase in S2C rate. For example, Franklin Planner has seen a 35% increase in click to cart conversion.
Return on Investment
Here at Inbenta, we are devoted to producing the best Natural Language Processing technology for ecommerce. This allows our customers to:
- Increase revenue online by increasing conversion ratio
- Increase revenue online by increasing “search-to-cart” efficiency
- Increase customer satisfaction by providing an incredible user experience for product searches
Franklin Planner has recently optimized their ecommerce search with Inbenta and have seen a 20% conversion from search to cart since implementation. New features are being implemented in our ecommerce search engine, to constantly optimize our customer’s search results.
Natural Language Search is a concept that every business will need to become familiar with in the near future. By integrating it properly, ecommerce websites will be able to provide shoppers the right answers at the right time, to improve conversion rates and drive additional revenue.
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