There are more online shoppers than ever and even though e-commerce retailers spend huge amounts of money on marketing for users to come to their website, they can easily lose the customer if there is a poor search experience. This is where natural language processing can increase the likelihood of browsers turning into buyers.
Why natural language technology is so important for e-commerce
Here is an example of search results not matching the request. Amazon started as an online bookstore, but soon became an online “store of everything.”
Let’s say you are a musician and you are looking for a new bass guitar. The standard design for an electric bass guitar has four strings, although five strings is also a common option. You will see the search query is basically the same, but results are very different.
We tried these 2 search queries at Amazon:
- 5 string electric bass guitar above $1000
- bass guitar with five strings for more than 1,000 dollars
And these are the results:
Just by looking at these 2 pictures, you will see that the search results are very different, although the search query was basically the same.
Why are e-commerce searches so flawed?
When it comes to e-commerce 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 model as:
“Popular product name” + “Features”
Features and product names can appear in any order, making an e-commerce search particularly challenging.
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 saw a 35% click to cart conversion.
Return on investment
Here at Inbenta, we are devoted to producing the best natural language processing technology for e-commerce. 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 optimized its e-commerce search with Inbenta and saw a 20% conversion from search to cart upon implementation. New features are being implemented in our e-commerce 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, e-commerce websites will be able to provide shoppers the right answers at the right time, to improve conversion rates and drive additional revenue.
If you would like more information about natural language processing for e-commerce, please schedule a demo with us so we can demonstrate how to optimize your customer’s search experience.