Abstract: Item codes are mapped to multidimensional space as item vectors based on each item codes context relevant to other item codes in a product catalogue. A transaction history for a given customer is obtained and each item vector associated with a corresponding item purchase made by that customer is obtained. All item vectors per customer are summed to create an aggregated and single vector representing the purchase history of each customer. The aggregated customer-item vectors for the customers are plotted in the multidimensional space. The plotted customer-item vectors are then clustered into groupings based on their distances from one another in the multidimensional space; the groupings representing data-driven customer segments. The data-driven customer segments along with customer identifiers for the customers comprising each segment are provided as input to promotional engines and/or loyalty systems.

Abstract: Item codes are mapped to multidimensional space as item vectors based on each item codes context relevant to other item codes in a product catalogue. A transaction history for a given customer is obtained and each item vector associated with a corresponding item purchase made by that customer is obtained. All item vectors per customer are summed to create an aggregated and single vector representing the purchase history of each customer. The aggregated customer-item vectors for the customers are plotted in the multidimensional space. The plotted customer-item vectors are then clustered into groupings based on their distances from one another in the multidimensional space; the groupings representing data-driven customer segments. The data-driven customer segments along with customer identifiers for the customers comprising each segment are provided as input to promotional engines and/or loyalty systems.

Abstract: A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.

Abstract: A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.

Abstract: Item codes are mapped to multidimensional space as item vectors based on each item codes context relevant to other item codes in a product catalogue. A transaction history for a given customer is obtained and each item vector associated with a corresponding item purchase made by that customer is obtained. All item vectors per customer are summed to create an aggregated and single vector representing the purchase history of each customer. The aggregated customer-item vectors for the customers are plotted in the multidimensional space. The plotted customer-item vectors are then clustered into groupings based on their distances from one another in the multidimensional space; the groupings representing data-driven customer segments. The data-driven customer segments along with customer identifiers for the customers comprising each segment are provided as input to promotional engines and/or loyalty systems.

Abstract: Transaction item codes for transactions are mapped to item vectors within multidimensional space. Each transaction defines a plurality of item vectors for transaction item codes that are mapped within the multidimensional space. Any given item vector's positions within the multidimensional space can have distances calculated to other specific item vectors plotted within the multidimensional space. The distances between the other specific item codes and a given item vector's positions represent probabilities that specific items are likely to be associated with the corresponding item associated with the transaction. Item vector distances below a predefined threshold for a given transaction having a given set of items represent items that are not present in the given transaction but should be recommended to be included with the given transaction.

Abstract: A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.