Business

Qubit unveils new CommerceAI – Headline 4 Ever

With online sales rising 44 percent year-over-year, the highest e-commerce growth in two decades – according to data from DigitalCommerce360 – Qubit aims to help brands take advantage of the huge growth through its new CommerceAI.

Qubit, the AI-powered personalization company, has announced its new CommerceAI model and promised to help retailers deliver advanced one-to-one personalization to unleash more value from entire product catalogs.

“Although the growth of e-commerce exceeded all expectations last year without signs of slowing down, many customers are still frustrated by the online shopping experience because they either cannot easily find what they are looking for or the products recommended are not individually targeted. towards the shop, ”said Graham Cooke, CEO and founder of Qubit. Brands can remedy this by using modern technologies such as deep learning to better understand customer behavior and create truly tailored shopping experiences; those that connect the visitor directly with the products they are most interested in. ”

By combining customer data – including intent and design tools, the company says that Qubit CommerceAI will increase the conversion rate and the customer’s lifetime value, while reducing the reduction. And further, through the use of deep learning, the solution aims to help brands learn more about their own customers to determine the products that will drive sales.

“How to CommerceAI enhances the customer experience by making the online journey truly seamless within the first few seconds after a visitor lands on the site, ”said Tracey Ryan O’Connor, Chief Income Officer at Qubit. “Since customers can jump fast, it is imperative. With CommerceAI, the visitor is immediately able to find products that capture their interest, as the technology enables an unsurpassed understanding of both the entire product catalog and the individual visitor. This makes it possible brand to respond in real time to present the most relevant products to each individual person. ”

According to the company, deep learning also utilizes “more sophisticated algorithms than machine learning, enabling huge amounts of data to be processed and understood in real time to enhance the entire end-user experience and result in superior e-commerce results.” And the data can be leveraged for insights that e-commerce teams are able to use when making important business decisions about merchandize, inventory, promotions and more.

Qubit’s differentiator lies in its ten-year plus of expertise in the retail sector combined with its real-time industry-specific data scheme and new application of deep learning through a unique partnership with Google Cloud – including the integration of Google’s recommendations AI – and other innovative ways the company enables processing and interpretation of huge amounts of data within a split second, ”O’Connor said.

Qubit’s personalized one-on-one approach marks a step away from a segmentation-first strategy, while facilitating a broader understanding of the retailer’s entire product catalog, the vast majority of which – approx. 80 percent – traditionally may not have been found and accessed through visitors. . As a result, this method engages and drives shoppers more directly to purchase by deeply understanding both the individual shopper and the product catalog at the same time. ”

As each brand’s needs vary, Qubit modules can be built to the specification that has the most impact through improved customer engagement and e-commerce performance, including one for a product recommendation, one for a product badge, personalized content, reset, visitor pulse, and replenishment.

FOR MORE Headline 4 Ever BUSINESS NEWS:

Scalefast data identifies what creates beauty and wellness consumers to try new products

The Klarna report reveals how consumers plan to act after the pandemic

PayPal and BigCommerce highlight post-COVID-19 consumer behavior in new report

Leave a Comment

Your email address will not be published. Required fields are marked *

*