London-based ecommerce personalization tech company Qubit has raised $26 million in a Series B round led by new investor Accel Partners.
Existing investors Balderton Capital and Salesforce Ventures also participated in the round, which brings Qubit’s total financing to $36.5 million to date.
“Our new funding is for continued investment in R&D, and we have some very exciting developments in predictive data and empowering marketers to take control of their optimization strategies,” company CEO Graham Cooke said. “We’re also continuing to scale up our sales, professional service and marketing teams in the US and Europe… (and) the plan is to IPO when the business is ready.”
Qubit is among a handful of tag management systems (TMS) like Ensighten, Signal (formerly BrightTag) and Tealium, whose technologies track and codify data signals and, oftentimes, run advanced analytics on top of that traffic flow. In Qubit’s case, tag management is a part of a broader portfolio of audience segmentation and online personalization apps developed by a bunch of Google alums.
Qubit helps retail customers ranging from Staples to Jimmy Choo create what company Cooke calls “Visitor Clouds” of first-party customer data.
For the sake of comparison, Qubit functions similarly to marketing cloud tools like Adobe Target (a website personalization engine) and Experience Manager (a content management system), but its differentiator, according to Graham, is Qubit was built from the ground up with advanced Hadoop and Javascript code designed for high-speed heavy lifting.
“We create detailed profiles for each visitor, whether it’s their first time to the site or they have browsed 1,000 products and bought one,” Cooke noted. “Our customers use our analytics and personalization applications to analyze this data to create cohorts based on (real-time) behavior to deliver a more personalized experience, using A/B testing to prove the uplift.”
For instance, a majority of analytics systems discard product detail on a retailer’s product pages like item size, weight, category color, price and availability, Cooke claimed. This data, collected through tags, becomes especially important if a user wants to implement recency, frequency and monetary value (RFM) modeling, an ecommerce tactic that focuses on customer lifecycle management and is more advanced than click-based reporting.
If a shopper views 10-15 items before converting, or otherwise engages in a series of actions that do (or don’t) lead to a purchase, marketers need a more finely tuned system for tracking that. Although Qubit hasn’t bucketed itself in the data management platform (DMP) category, the use case for tag managers, attribution systems and DMPs are blurring and the company offers up some semblance of all three capabilities.
Qubit on Monday also rolled out Revenue Impact, a statistical A/B testing model for marketers that detects when average order value or items per visit increase, and the subsequent uplift in sales. Revenue impact is a frequently overlooked indicator of campaign success.
Additionally, Qubit is wiring its Opentag TMS and Deliver A/B testing platform to run control group tests for third-party platforms to determine bottom-line impact of “retargeting,” or “ratings and reviews” implementations, for instance.
