Systems and methods of sourcing hours of operation for a location entity

A computing device, comprising:

• one or more processors;
• and
• one or more memory devices, the one or more memory devices storing computer-readable instructions that when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
• receiving a request to update the hours of operation for a location entity;
• determining a number of mobile devices present at the location entity during one or more periods of time based at least in part on a collection of time-stamped mobile device location data, wherein the collection of time-stamped mobile device location data identifies one or more mobile devices associated with one or more users as present at the location entity;
• providing the number of mobile devices and initial operating hours for the location
• entity as input to a model, wherein the model comprises a statistical model trained to generate one or more predictive outputs indicative of change to initial operating hours of other location entities based on detected patterns of in-store mobile device activity learned from training data, wherein the training data comprises a collection of mobile device location data including number of mobile devices and verified results for updated operating hours for a plurality of other location entities;
• determining an output of the model indicative of a likelihood that operating hours for the location entity have changed relative to the initial operating hours;
• and
• updating the operating hours for the location entity based at least in part on the output of the model.

One or more tangible, non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations, the operations comprising:

• receiving a request to update the hours of operation for a location entity;
• determining a number of mobile devices present at the location entity during one or more periods of time based at least in part on a collection of time-stamped mobile device location data, wherein the collection of time-stamped mobile device location data identifies one or more mobile devices associated with one or more users as present at the location entity;
• providing the number of mobile devices and initial operating hours for the location entity as input to a model, wherein the model comprises a statistical model trained to generate one or more predictive outputs indicative of change to initial operating hours of other location entities based on detected patterns of in-store mobile device activity learned from training data, wherein the training data comprises a collection of mobile device location data including number of mobile devices and verified results for updated operating hours for a plurality of other location entities;
• determining an output of the model indicative of a likelihood that operating hours for the location entity have changed relative to the initial operating hours;
• and
• updating the operating hours for the location entity based at least in part on the output of the model.