Method and system for predicting behavioral characteristics of customers in physical stores

A system for customer data processing to predict behavioral characteristics of a customer in a physical store, said system comprising:

• a transceiver configured to receive said customer data comprising one or more shopping-related actions, pertaining to a shopping episode, of said customer in said physical store from one or more sensing devices in one or more portable electronic devices associated with said customer;
• a processor configured to:
• segment said shopping episode into one or more segments of time durations based on at least said one or more shopping-related actions;
• categorize each of said one or more segments of time durations into one of an in-aisle category and a non-aisle category based on at least an inter-step time duration of said customer during each of said one or more segments of time durations;
• and
• for each segment of time duration that corresponds to said in-aisle category:
• identify one or more hand-related actions of said customer based on said received customer data associated with said each segment of time duration;
• determine a likelihood of occurrence of an action-related category based on at least said identified one or more hand-related actions;
• and
• predict said behavioral characteristics of said customer based on at least said determined likelihood of occurrence of said action-related category associated with said each segment of time duration.

A computer program product for use with a computing device, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for customer data processing to predict behavioral characteristics of a customer in a physical store, wherein the computer program code is executable by one or more processors in the computing device to:

• receive said customer data comprising one or more shopping-related actions, pertaining to a shopping episode, of said customer in said physical store from one or more sensing devices in one or more portable electronic devices associated with said customer;
• segment said shopping episode into one or more segments of time durations based on at least said one or more shopping-related actions;
• categorize each of said one or more segments of time durations into one of an in-aisle category and a non-aisle category based on at least an inter-step time duration of said customer during each of said one or more segments of time durations;
• and
• for each segment of time duration that corresponds to said in-aisle category:
• identify one or more hand-related actions of said customer based on said received customer data associated with said each segment of time duration;
• determine a likelihood of occurrence of an action-related category based on at least said identified one or more hand-related actions;
• and
• predict said behavioral characteristics of said customer based on at least said determined likelihood of occurrence of said action-related category associated with said each segment of time duration.