Alba J. Collart

Consumer Response to Point of Purchase Advertising for Local Brands

This study evaluates the effectiveness of a point of purchase advertising program conducted for two local horticultural brands in Texas. The results based on surveys gathered before and after the program was launched suggest that the campaign size was not sufficient to significantly increase brand awareness and overall demand, yet it increased willingness to pay by 5.5% for those consumers aware of one of the brands. A major factor found to increase willingness to pay and likelihood of brand awareness was purchase frequency measured in transactions per month, which suggests that other advertising methods aimed to increase buying frequency might affect demand more effectively.

Using eye-tracking to model attribute non-attendance in choice experiments

ABSTRACT We investigate revealed attribute attendance in discrete choice experiments using eye-tracking. A simple theoretical framework is proposed in which choices are a function of visual attention. Consistent with the existing literature, the assumption that participants use all the available information to make their decisions does not hold. The results also illustrate that model fit and predictive power are greatly increased by using visual attendance measures as regressors. The use of eye-tracking technology has value for measuring revealed attention and to benchmark with existing choice models.

Insights into the appropriate level of disaggregation for efficient time series model forecasting

This paper provides a potentially valuable insight on how to assess if the forecasts from an autoregressive moving average model based on aggregated data could be substantially improved through disaggregation. It is argued that, theoretically, the absence of moving average (MA) terms indicates that no forecasting efficiency improvements can be achieved through disaggregation. In practice, it is found that there is a strong correlation between the statistical significance of the MA component in the aggregate model and the magnitude of the forecast mean square error (MSE) decreases that can be achieved through disaggregation. That is, if a model includes significant MA terms, the forecast MSE improvements that may be gained from disaggregation could be substantial. Otherwise, they are more likely to be relatively small or non-existent.