TECHNICAL FIELD

The subject matter disclosed herein generally relates to the technical field of special-purpose machines that facilitate adding new features to a payment processor or payment device. The subject matter also relates to an improved payment processor that implements such new features and includes software-configured computerized variants of such special-purpose machines and improvements to such variants, and to the technologies by which such special-purpose machines become improved compared to other special-purpose machines that facilitate adding the new features.

Aspects of the present disclosure use data signals uniquely observable from a checkout page of a purchase transaction funnel to make inferences about a customer's experience in the transaction. In one aspect, an improved payment processor can dynamically optimize a checkout page using a machine-learned model.

BACKGROUND

The present subject matter seeks to address technical problems existing in conventional payment processors and systems. For example, a service provider may seek to improve a technical service or user interface but may lack or be denied access to sufficient data to implement analysis and change. As one example, a payment processor may be driven to provide world-class online payment services but may only be allowed a transactional presence by an online merchant as the very last step in a purchase transaction funnel. In typical arrangements, the very last page is a checkout page, and data can generally only be sourced from this page.

Thus, the requisite data that might otherwise allow an informed analysis of a user experience is limited in such conventional arrangements. This technical shortcoming can present a significant challenge to the continued development of online payment services or other technical services.

Moreover, although a customer may ultimately make a purchase, not all purchases are made with a positive mindset. That is, a customer may make a purchase but nevertheless be frustrated, upset, or uncertain about the purchase just made. A present technical inability to detect a customer mindset based on data sourced from a checkout page can result in compromised transactional results and system inefficiency. A comprised system can lead to a significant waste of technical resources and technical inefficiency. Other drawbacks can include increases in purchase returns, chargebacks, loss of future or recurring business, and the like.

