Any e-commerce marketer will vouch for the fact that giving a unique, highly personalized experience to any shopper who comes to their platform is a key driving force behind their efforts. BloomReach seems to know this rather well.
It has launched BloomReach Search, Navigation and Personalization (SNAP), an application that personalizes site discovery for all consumers on any device or channel in real time. BloomReach SNAP also provides its big-data insights to marketers and merchants so that they can drive more revenue and better site-wide consumer engagement. It’s an application that not only empowers the end user to make an informed buying decision but also equips marketers to make intelligent analysis based on consumer behaviour data that it generates on real time basis.
For consumers, BloomReach SNAP connects user experiences anonymously as they shop across devices, and then dynamically adapts sites’ search, navigation and content uniquely to each visitor. Using web-search algorithms, it combines behavioural patterns, natural-language processing, machine-learning and other signals of intent to present the most relevant search results, left-navigation and contextual filter.
According to the company, unlike other onsite and personalization applications that mirror early internet search technology with manual tags, rules and hierarchies, BloomReach SNAP uses the industry’s most powerful natural-language processor to extract and connect product attributes to the countless ways that consumers describe and discover online – learning from all channels, visitors and devices. Merchants can tailor the experience without writing extensive rules, manually tagging content or testing proscribed combinations for quality search results or ‘personalization.’
Remarking on this development, Vinodh Kumar Head of BloomReach India, says, “The entire product was built out of the India engineering centre. What’s encouraging is the fact that the product was delivered on time and immediately impacted the user experience and revenue results of the early access customers.”
BloomReach SNAP solves a very complex big data problem. For instance, a fashion retailer has 1000 different types of shoes from 100 different designers. Deciding which shoes to show in the top 20 – 30 slots is a very hard and complex problem. BloomReach SNAP can optimize those rankings in two ways. The first weighs relevance of the product for a particular query and the performance of that product. This ranking algorithm also introduces new products that are highly relevant and are expected to perform as well as the current top performers, thus giving new products a great chance to succeed. The second optimization includes another variable in the ranking – the personal preferences expressed by that individual shopper. For example, if she favours a certain brand of jeans (with the products she views on her mobile, tablet and desktop), that brand would feature those jeans higher in the search results for ‘women’s jeans’.
According to Vinodh, two of the biggest strengths of BloomReach’s technology are “our deep understanding of how consumers express their intent (through natural language and how they engage with a website) and our ability to extract attributes about the products our customers sell. Our system then continuously tests, learns and optimizes to ensure we can match the consumer’s demand with the retailer’s supply.”