Patent ID: 8386284

Claim:
A computer-implemented method for accurate demand modeling in a retail category, the method comprising: selecting a product set for a demand modeling analysis in a chosen retail category; obtaining a retail-level sales data set for the product set in the chosen retail category having data elements representing a time series of unit-prices and unit-sales for the plurality of products “p” in a chosen retail category taken over a plurality of time periods “t”, and over a plurality of retail chains and stores “s”, said sales data set having a combination of data elements (p, s, t); identifying any missing data element corresponding to a specific (p, s, t) combination in the retail sales data set and determining a root cause type for each said missing data element, a corresponding root cause type for said missing data element comprising: a first type root cause for a missing data element due to a unit-sales being zero, or a second type root cause for the missing data element that does not have a first type root cause, but for which the corresponding data element would have had a non-zero unit-sales value that is omitted from the sales data set; encoding dummy variables for the missing data elements in the retail sales data set, wherein for any missing elements corresponding to specific (p, s, t) combinations in the data set, said dummy variable value encoding performed according to the corresponding root cause for the said missing data element; and, generating a demand model for the products in the retail category from the sales data set containing the missing data elements corresponding to the root causes for certain product, store and time-period combinations, wherein a computing system including at least one processor and at least one memory device connected to the processor performs the selecting, obtaining, identifying, encoding and the generating.