How natural language processing and machine learning are changing eCommerce
For the average online shopper, it’s easy not to notice how much ecommerce has evolved over the years, or how that evolution is continually gathering pace. We can all appreciate that online shopping is a far less frustrating experience than it was just a few years ago, but for the majority of shoppers, the technological changes that are taking place behind the scenes are invisible. The adoption of natural language processing (NLP) and machine learning technologies is one of the key drivers of change in ecommerce today, and the impact that these technologies are having on customer experience is both far-reaching and powerful. In this article, we examine some of the key ways that NLP and machine learning are being used within ecommerce, to drive increased sales and improved conversion rates.
Smart on-site search
Online shopping is now part and parcel of everyone’s life, and something we have quickly come to take for granted. Alongside the rapid adoption of ecommerce, the growth of smartphone usage has been exponential, with more than two-thirds of the UK population now owning a smartphone. As mobile devices overtake laptops as the preferred device for accessing the Internet, it’s vital that ecommerce sites adapt, to satisfy the needs of time-pressed consumers who want to shop from their phones or tablets. This has pushed on-site search to the forefront of retailers’ attention, as search has become the main navigation tool for many online shoppers, replacing cumbersome menu-driven navigation structures.
Historically, on-site search has focussed on keyword matching. This has often produced results that were less than optimal, with seemingly random results generated for more descriptive queries, or even no results at all. That’s all changing though, as ecommerce retailers look to NLP-driven site search engines to power their on-site search. NLP search is a game-changer in ecommerce, because it has the capability to base results on semantics, rather than keywords, which produces more relevant results. This smart, meaning-based approach to search is coupled with machine learning abilities, to create an engine that becomes smarter over time, learning more and more about query intent, concept extraction and shopper behaviour. The data mined from an NLP search engine is ploughed back into the system to improve future search performance, but it can also be used in other areas of ecommerce operations, to drive additional benefits.
Product recommendations represent a very important part of an ecommerce store today, particularly for merchants with larger catalogs. Product recommendation blocks are used across all page templates, primarily for up-selling and cross-selling.
The immediate benefits of smarter product recommendations is improved customer satisfaction and improved average order value and conversion rates, which leads to increased sales. The data derived from product recommendations has huge value too. By analysing customer behaviour on a store, understanding much more about shopper intent, and looking at aggregate data for products viewed and similar customers, it is possible to present a highly personalised experience to each individual shopper.
As an example, if a customer searches a fashion website for ‘red dresses’, machine learning technology could enable the store to dynamically recommend highly targeted products to that customer, such as red shoes or a matching bag, even if the recommended products don’t actually contain the keyword ‘red’. This extraction of meaning and context means that the needs and desires of customers can be interpreted in more detail and more accurately than ever before.