Qubit Aura uses AI machine learning to enhance the mobile shopping experience. Built to work in tandem with an existing e-commerce sites, it helps people browse products that are relevant to their preferences and personal style. By using data from their history on the site, it intuitively adapts suggestions to curate a feed of potential purchases, meaning that they don’t need to trawl through endless product categories and irrelevant items. Although the ‘trending today’ and ‘save for later’ features are nods to image-based social apps, Qubit’s product director Simon Jaffrey notes that the innovation is a way for brands to “take control back from Instagram and Pinterest,” keeping product discovery within their mobile sites.

"Qubit Aura is an industry-first which tackles the challenge of discoverability on mobile web," says Graham Cooke, CEO of Qubit. "In the same way Netflix tailors the experience to each and every user based on their watching habits... Qubit Aura does the same for e-commerce; no two experiences are the same.”

Working with brands such as Colourpop, Net-A-Porter, and John Lewis, Qubit unifies browsing and shopping. Yet while 45% of all retail journeys now contain a mobile element, with smartphones allowing people to buy from anywhere, the conversion rate is only about half of that on desktop. By integrating an app-like interface that creates a unique experience for everyone, Aura benefits the 70% of Britons who expect personalised services from the brands they shop with, as well as the retailers that stand to increase sales by up to 7.8% with the integration of personalisation.

Mica Anthony is an editorial assistant at Canvas8, which specialises in behavioural insights and consumer research. She’s passionate about uncovering the newest electronic music producers, re-living ‘90s fashion and championing the voices often left out of mainstream media.