DATA-DRIVEN PRESCRIPTIVE RECOMMENDATIONS

DATA-DRIVEN PRESCRIPTIVE RECOMMENDATIONS

Metrics are captured from a variety of systems associated with stores of a retailer. Values for factors or benchmarks are calculated per store from their corresponding metrics. Each of the stores are labeled as successful or unsuccessful. Factors for which high values are correlated with successful...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Abadi, Shiran, Laserson, Itamar David
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:Metrics are captured from a variety of systems associated with stores of a retailer. Values for factors or benchmarks are calculated per store from their corresponding metrics. Each of the stores are labeled as successful or unsuccessful. Factors for which high values are correlated with successful stores and low values are correlated with unsuccessful stores are clustered together. Similarly, factors for which low values are correlated with successful stores and high values are correlated with unsuccessful stores are clustered together. A set of clustered factors associated with the success, or the failure of stores are reported to the retailer in a data model that also comprises the various degrees to which the various clusters of the factors relate to or correlate with both the successful stores and the unsuccessful stores. Prescriptive recommendations are derived from the data model to improve metrics associated with successful factors.