Abstract: Approaches are provided that determine and fine-tune product assortments carried by retail stores. In aspects, the number of stores that carry the item, the sales performance of the item, the loyalty of customers to the item, whether substitutes for the item can be found, and other factors are used to determine an actions such as increasing or decreasing the shelf space for an item.

Abstract: A method for augmenting transport services using event detection is provided. The method includes collection of first sensor data generated by various sensors associated with a plurality of vehicles. The first sensor data includes sensor outputs that indicate a plurality of rash driving events. The sensor outputs are augmented based on angular rotation to obtain augmented sensor outputs. A prediction model is trained based on the augmented sensor outputs. Target sensor data associated with a target vehicle is provided as input to the trained prediction model, and based on an output of the trained prediction model an occurrence of a rash driving event is detected in real-time or near real-time. Based on a count of rash driving events associated with the target driver within a cumulative driving distance, a driver score of the target driver is determined.

Abstract: Initial sales cluster is divided by the control circuit into a plurality of velocity buckets. Subsequently, each velocity bucket is divided into a plurality of micro-clusters. The micro-clusters are defined according to demographic information or store characteristic information. A importance score and a performance score for each of the micro-clusters is determined. An optimal sales cluster and a corresponding optimal planogram for each retail store in each micro-cluster are determined based upon the importance score and the performance score.

Abstract: A method for augmenting transport services using event detection is provided. The method includes collection of first sensor data generated by various sensors associated with a plurality of vehicles. The first sensor data includes sensor outputs that indicate a plurality of rash driving events. The sensor outputs are augmented based on angular rotation to obtain augmented sensor outputs. A prediction model is trained based on the augmented sensor outputs. Target sensor data associated with a target vehicle is provided as input to the trained prediction model, and based on an output of the trained prediction model an occurrence of a rash driving event is detected in real-time or near real-time. Based on a count of rash driving events associated with the target driver within a cumulative driving distance, a driver score of the target driver is determined.

Abstract: Based upon the transition information for all the retail stores, a determination is made of an average cluster retention score. The average cluster retention score is a measure of how many retail stores have moved from original to different existing sales clusters in the current sales period. When the average cluster retention score is below a predetermined threshold, a complete re-organization of the existing sales clusters is performed. When the average cluster retention score is above the predetermined threshold, a determination is made as to whether each retail store should be re-classified.

Abstract: Initial sales cluster is divided by the control circuit into a plurality of velocity buckets. Subsequently, each velocity bucket is divided into a plurality of micro-clusters. The micro-clusters are defined according to demographic information or store characteristic information. A importance score and a performance score for each of the micro-clusters is determined. An optimal sales cluster and a corresponding optimal planogram for each retail store in each micro-cluster are determined based upon the importance score and the performance score.

Abstract: Approaches are provided that determine and fine-tune product assortments carried by retail stores. In aspects, the number of stores that carry the item, the sales performance of the item, the loyalty of customers to the item, whether substitutes for the item can be found, and other factors are used to determine an actions such as increasing or decreasing the shelf space for an item.

Abstract: Systems and methods for generating a hierarchical data structure are provided. An example method can include: acquiring sales data associated with items and item pairs in a category for a given period; filtering, based on a set of predetermined criteria, the sales data to obtain the items and the item pairs; generating document term matrices for the item pairs based on item description and item attributes; calculating attribute similarity measures and substitutability measures for the item pairs; performing, based on a set of predefined CBT generating rules, a hierarchical clustering on the document term matrices, item association, the substitutability measures and the attribute similarity measures; generating the CBT with a hierarchical grouping of the items; transforming the CBT to a dendrogram; pruning the dendrogram to generate a wireframe; regrouping the item pairs in CBT nodes with similar and substitutable attributes; and generating a user interface to visualize the CBT.

Abstract: Provided is a system and methods for reducing wait time in a store with a plurality of checkout queues. The system includes an item counting device coupled to a shopping cart. The item counting device includes a weigh sensor and a counter, wherein the counter increments by 1 in response to the weigh sensor detecting an increase in weight as an item is placed within the shopping cart. The system includes an efficiency analyzer that determines a historical efficiency of an employee at a checkout register. Additionally, the system includes proximity processor that receives data from a customer through an input device and outputs a proximity of the customer to a queue. The system has a wait time processor that determines the wait time of a plurality of checkout queues and outputs the same to the customer.

Abstract: Systems and methods for generating a hierarchical data structure are provided. An example method can include: acquiring sales data associated with items and item pairs in a category for a given period; filtering, based on a set of predetermined criteria, the sales data to obtain the items and the item pairs; generating document term matrices for the item pairs based on item description and item attributes; calculating attribute similarity measures and substitutability measures for the item pairs; performing, based on a set of predefined CBT generating rules, a hierarchical clustering on the document term matrices, item association, the substitutability measures and the attribute similarity measures; generating the CBT with a hierarchical grouping of the items; transforming the CBT to a dendrogram; pruning the dendrogram to generate a wireframe; regrouping the item pairs in CBT nodes with similar and substitutable attributes; and generating a user interface to visualize the CBT.

Abstract: Approaches are provided that determine and fine-tune product assortments carried by retail stores. In aspects, the number of stores that carry the item, the sales performance of the item, the loyalty of customers to the item, whether substitutes for the item can be found, and other factors are used to determine an actions such as increasing or decreasing the shelf space for an item.

Abstract: Initial sales cluster is divided by the control circuit into a plurality of velocity buckets. Subsequently, each velocity bucket is divided into a plurality of micro-clusters. The micro-clusters are defined according to demographic information or store characteristic information. A importance score and a performance score for each of the micro-clusters is determined. An optimal sales cluster and a corresponding optimal planogram for each retail store in each micro-cluster are determined based upon the importance score and the performance score.

Abstract: Based upon the transition information for all the retail stores, a determination is made of an average cluster retention score. The average cluster retention score is a measure of how many retail stores have moved from original to different existing sales clusters in the current sales period. When the average cluster retention score is below a predetermined threshold, a complete re-organization of the existing sales clusters is performed. When the average cluster retention score is above the predetermined threshold, a determination is made as to whether each retail store should be re-classified.

Abstract: Provided is a system and methods for reducing wait time in a store with a plurality of checkout queues. The system includes an item counting device coupled to a shopping cart. The item counting device includes a weigh sensor and a counter, wherein the counter increments by 1 in response to the weigh sensor detecting an increase in weight as an item is placed within the shopping cart. The system includes an efficiency analyzer that determines a historical efficiency of an employee at a checkout register. Additionally, the system includes proximity processor that receives data from a customer through an input device and outputs a proximity of the customer to a queue. The system has a wait time processor that determines the wait time of a plurality of checkout queues and outputs the same to the customer.