“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.