For marketers and sales professionals, estimating price elasticities of their products is crucial for understanding sales and setting pricing strategies. Yet, given the variety of possible econometric models, the central question that arises as which one of them would be the most appropriate for elasticity measurement. This paper conducts a comprehensive empirical study of 104 weeks of sales (January 2016 to December 2017) for 340 Hair Care products sold in 11 retailers. Our first findings show that considering breakpoints and outliers ahead of using any econometric model significantly improves the output from the classical and most widely used models such as Ordinary Least Squares (OLS) and Quantile Regressions (QR). Moreover, we present two other innovative models, Quantile on Quantile Regression (QQR) and Gravity Center Regression (GCR) which could further eliminate the measurement bias given limited or even aggregated data and, assist with the marketing decision making processes.
JORM introduces peer-review from its first Edition onwards. The researchers submitting their papers for publication should review atleast one technical paper from their domain. The manuscript also undergoes mandatory procedural review with JORM review and scholar panel.