Incremental AI Adoption for E-commerce
When you think of e-commerce, your mind is probably drawn to Amazon.com as "the definitive" example. But it's actually the exception. The internet is filled with tons of small- and medium-sized e-commerce sites. These sites typically follow the same pattern - a search page with a search box at the top, selectable filters along the left side, and results filling the remainder of the screen. And the whole goal is to quickly usher customers to the products they seek.
For most of these sites, the implementation is quite simple. Product metadata is indexed into a search engine such as Elasticsearch or Algolia. This includes fields like the title of the product, its description, its price, and other relevant features (sizes for shoes, square feet for houses, etc.) And the application is typically quite simple – the user submits a search, and the backend issues a query that hopefully captures the customers intent, and then captures the responses and sends them to the frontend for display in the search results.
Unfortunately "right-out-of-the-box" search results are often not that great, and fixing the problem often requires hiring a team of search experts – something that smaller shops are unable to afford. Fortunately, modern AI is coming to the rescue! In this post we'll demonstrate how e-commerce shops can incrementally adopt AI and explore improvements in search which would have been unbelievable just 5 years ago.
