Target Brands, Inc.

Aarthi Srinivasan

James Neumann

Venkataramana Bantwal Kini

Rebecca Swartz

Methods and systems for facilitating delivery of goods from a retail location to a customer are disclosed. In one method, a request is received from a customer user which includes an order for goods to be delivered from a retail store. One or more delivery users within a predefined geographic area shared with a location of the customer user are identified. A delivery user agrees to pick up the customer's order and deliver the requested goods to the customer's home. Confirmation messages are automatically sent and received when the delivery user picks up the customer's order and again when the order is delivered to the customer. The delivery user can earn rewards or payments upon completion of successful delivery.

System and method of facilitating delivery of goods to a customer

Methods and systems for analyzing and evaluating item recommendations presented on a web site are disclosed. In one aspect, a user interface is generated for display on a website. Item recommendations personalized for an individual user are displayed within in item recommendation region of the website. Impression, clickstream, and sales data are received from user activity on the website. The impression, clickstream, and sales data are displayed on a dashboard of an administrator user interface. An administrator user can select visualizations to display on the dashboard using selectors for impressions, clicks, sales, users, and time. Two or more sets of data can be overlaid to illustrate relationships between user interactions with recommended items and actual sales of those items. The dashboard is dynamically updated in response to new data received from the website in real-time.

Real-time recommendation monitoring dashboard

Methods and systems for recommending items to an unknown user of a website are disclosed. In one aspect, browsing activity of known users is analyzed to determine which items or categories of items are most popular in a given context (time, place, device, etc.). The known user browsing activity (clickstream data) is used to generate a multi-dimensional attribute matrix. Matrix factorization and clustering are used to generate affinity scores for items based on user context. These scores are used to generate item recommendations for an unknown user in a particular browsing context. In some embodiments, personalized item recommendations are updated based on interactions made between the unknown user and content of the website.

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Los Altos, CA, United States of America

Aarthi Srinivasan