US 11,727,427 B2
Systems and methods for assessing, correlating, and utilizing online browsing and sales data
Sohini Roy Chowdhury, Santa Clara, CA (US); Jon Seneger, Boulder Creek, CA (US); Ebrahim Alareqi, Santa Clara, CA (US); Joakim Soderberg, Gothenburg (SE); and Ao Liu, Mountain View, CA (US)
Assigned to Volvo Car Corporation, Gothenburg (SE)
Filed by Volvo Car Corporation, Gothenburg (SE)
Filed on Oct. 27, 2020, as Appl. No. 17/80,909.
Prior Publication US 2022/0129937 A1, Apr. 28, 2022
Int. Cl. G06Q 30/02 (2023.01); G06Q 30/0207 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0601 (2023.01); G06Q 50/04 (2012.01); G06F 16/9536 (2019.01); G06Q 30/0204 (2023.01); G06Q 10/04 (2023.01); G06N 3/08 (2023.01); G06N 20/20 (2019.01); G06Q 10/087 (2023.01)
CPC G06Q 30/0222 (2013.01) [G06F 16/9536 (2019.01); G06N 3/08 (2013.01); G06N 20/20 (2019.01); G06Q 10/04 (2013.01); G06Q 10/087 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0204 (2013.01); G06Q 30/0621 (2013.01); G06Q 30/0623 (2013.01); G06Q 30/0631 (2013.01); G06Q 50/04 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving web analytics data comprising identification information from a web analytics database of a web interface;
filtering the web analytics data comprising the identification information to generate subset of the web analytics data that indicates a probability for completion of a sales transaction above a predetermined probability taking into account product configurations that do not correlate with any sales data;
offering users or sessions associated with the subset of the web analytics data an offer inducement to complete sales transaction via the web interface;
correlating historical sales transaction data to the web analytics data to determine which product types are more probable to be involved in the sales transaction and which product types are less probable to be involved in the sales transaction; and
adjusting the product configurations of one or more of a manufacturing operation and an inventory allotment based on the determination as to which product types are more probable to be involved in the sales transaction and which product types are less probable to be involved in the sales transaction;
wherein correlating the historical sales transaction data to the web analytics data comprises:
encoding the web analytics data and the historical sales transaction data such that a resulting feature space represents product configurations, price per product configuration, and a fraction of each product configuration sold in a time period, T, for machine language processing by a neural network;
clustering web analytics records associated with the web analytics data and sales transaction records associated with the sales transaction data;
for each cluster, assigning a nearest cluster identification using a k-nearest neighbor method;
establishing a cost function between clusters;
selecting a training cluster and a plurality of validation clusters;
training the neural network using the training cluster and validating output of the neural network using the plurality of validation clusters;
based on the output of the neural network, generating and displaying on a display a joint distribution heat map indicating correlations between the web analytics data and the historical sales transaction data; and
using the joint distribution heat map, identifying the product configurations that do not correlate with any sales data.