Estimating Promotion Effects Using Big Data: A Partially Profiled LASSO Model With Endogeneity Correction

53 Pages Posted: 29 Nov 2018

See all articles by Luping Sun

Luping Sun

Peking University - Guanghua School of Management

Xiaona Zheng

Guanghua School of Management Peking University

Ying Jin

Peking University - Guanghua School of Management

Minghua Jiang

Peking University

Hansheng Wang

Peking University - Guanghua School of Management

Date Written: November 22, 2018

Abstract

Retailers are interested in understanding which price promotions are profitable and which are not. However, simultaneously estimating the promotion effects of a large number of products on retailer sales and profits is technically challenging for both researchers and practitioners. To address this challenge, this study proposes a Partially Profiled Least Absolute Shrinkage and Selection Operator (Partially Profiled LASSO) model, which can estimate ultra-high-dimensional regression relationships at a low computational cost and control for the endogeneity of promotion depth. The model can flexibly incorporate the time-varying promotion effects and the cross-over effects among the promotions of different products. We conduct an empirical study using data provided by a large retailer over a five-month period. Our model efficiently identifies products with promotion effects and the promotion effects are significantly associated with certain promotion, product, and category characteristics. The results also show that our model with cross-over effects outperforms the benchmark models that are widely used to handle the high-dimensional predictor matrix (e.g., the standard LASSO and principal component regression methods). This paper contributes to the related literature on price promotion and marketing analytics in data-rich environments, and provides implications for retailers to make more informed promotion strategies.

Keywords: Partially Profiled LASSO, Endogeneity, Promotion, Retailer Sales, Profits

Suggested Citation

Sun, Luping and Zheng, Xiaona and Jin, Ying and Jiang, Minghua and Wang, Hansheng, Estimating Promotion Effects Using Big Data: A Partially Profiled LASSO Model With Endogeneity Correction (November 22, 2018). Available at SSRN: https://ssrn.com/abstract=3289112 or http://dx.doi.org/10.2139/ssrn.3289112

Luping Sun

Peking University - Guanghua School of Management ( email )

Beijing, Beijing 100871
China

Xiaona Zheng

Guanghua School of Management Peking University ( email )

Peking University
Beijing, Beijing 100871
China

Ying Jin

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Minghua Jiang

Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Hansheng Wang (Contact Author)

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

HOME PAGE: http://hansheng.gsm.pku.edu.cn

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