At the third stage, the in-sample forecasts errorfrom the second stage are used as the dependent variable, and the variables from SKUs in the identified influential categories are used as the explanatory variables, (4) where Sk is the influential category set of category k and the disturbance term is represented by.We calculate the out-of-sample forecasts for the third stage. The final out-sample forecasts are the sum of the forecasts in the three stages. We add one-half the mean-squared error to the forecasts before transforming them back into sales units (Cooper et al. 1999). Empirical study 4.1 Data The empirical data comes from the IRI dataset (Bronnenberg et al., 2008). The IRI dataset includes grocery and drug chain data from a sample of stores in 50 markets and 30 categories, involving approximately 25%-30% of the consumer packaged goods sales in a grocery store. This is weekly data by SKU and includes information on sales, price, features and displays. Based on the objectives of this research, the records from a medium size grocery store in Chicago as the empirical sample were selected for fifteen product categories concerned with food and drink. Low-movement SKUs or SKUs which may have been introduced or discontinued were excluded. Our criterion was that at least 80% of the weeks must have positive movement a SKU to be included. The empirical dataset includes the weekly units sold, prices, displays and features of 926 SKUs in 15 food categories for 320 weeks. Table 1 presents the means and medians of units sold per week and percentages of weeks concerning promotional activities, including price reductions (more than 5 percent), displays and features across fifteen categories. It is clear that the price reduction is the most frequent type of promotion across all the categories. Feature advertising is also frequently used in many categories, such as frozen pizza and carbonated beverages. Display is only used occasionally for most of the categories except beer. As an initial analysis, we can use prior experience to suggest some potential relationships among categories. For example, substitution might exist in beer and carbonated beverages,
At the third stage, the in-sample forecasts errorfrom the second stage are used as the dependent variable, and the variables from SKUs in the identified influential categories are used as the explanatory variables, (4) where Sk is the influential category set of category k and the disturbance term is represented by.We calculate the out-of-sample forecasts for the third stage. The final out-sample forecasts are the sum of the forecasts in the three stages. We add one-half the mean-squared error to the forecasts before transforming them back into sales units (Cooper et al. 1999). Empirical study 4.1 Data The empirical data comes from the IRI dataset (Bronnenberg et al., 2008). The IRI dataset includes grocery and drug chain data from a sample of stores in 50 markets and 30 categories, involving approximately 25%-30% of the consumer packaged goods sales in a grocery store. This is weekly data by SKU and includes information on sales, price, features and displays. Based on the objectives of this research, the records from a medium size grocery store in Chicago as the empirical sample were selected for fifteen product categories concerned with food and drink. Low-movement SKUs or SKUs which may have been introduced or discontinued were excluded. Our criterion was that at least 80% of the weeks must have positive movement a SKU to be included. The empirical dataset includes the weekly units sold, prices, displays and features of 926 SKUs in 15 food categories for 320 weeks. Table 1 presents the means and medians of units sold per week and percentages of weeks concerning promotional activities, including price reductions (more than 5 percent), displays and features across fifteen categories. It is clear that the price reduction is the most frequent type of promotion across all the categories. Feature advertising is also frequently used in many categories, such as frozen pizza and carbonated beverages. Display is only used occasionally for most of the categories except beer. As an initial analysis, we can use prior experience to suggest some potential relationships among categories. For example, substitution might exist in beer and carbonated beverages,