Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients

Mariano Méndez-Suárez

doi:10.3390/math9151832

Abstract

Partial least squares structural equations modeling (PLS-SEM) uses sampling bootstrapping to calculate the significance of the model parameter estimates (e.g., path coefficients and outer loadings). However, when data are time series, as in marketing mix modeling, sampling bootstrapping shows inconsistencies that arise because the series has an autocorrelation structure and contains seasonal events, such as Christmas or Black Friday, especially in multichannel retailing, making the significance analysis of the PLS-SEM model unreliable. The alternative proposed in this research uses maximum entropy bootstrapping (meboot), a technique specifically designed for time series, which maintains the autocorrelation structure and preserves the occurrence over time of seasonal events or structural changes that occurred in the original series in the bootstrapped series. The results showed that meboot had superior performance than sampling bootstrapping in terms of the coherence of the bootstrapped data and the quality of the significance analysis.

Highlights

Marketing mix models use multiple regression to measure marketing effectiveness and efficiency [1]
partial least squares structural equation models (PLS-SEM) methodology using i.i.d. data has been very successful in areas such as marketing, strategic management, management information systems, production, and operations or accounting [37], and it is a promising methodology for time series, especially marketing mix modeling [2,3]
To succeed in these areas, the traditional method to measure the significance of the structural model and the outer loadings using sampling bootstrapping should be reconsidered because this method shuffles the data without considering their internal structure or respecting the order of the sequence, the autocorrelation structure, and the moments of occurrence of special events

Summary

Introduction

Marketing mix models use multiple regression to measure marketing effectiveness and efficiency [1]. In the case of multichannel retailers that sell online and offline and advertise on both offline and Internet media, a common solution to the model marketing mix is chaining multiple regression models (based on conversations with consulting experts), i.e., modeling first the impact of advertising on online sales and using this information to model offline sales. Recent research [2] proposed using partial least squares structural equation models (PLS-SEM) to measure the simultaneous impact of advertising in multichannel retailer contexts and to measure the effectiveness of the different advertising campaigns on web and store sales [3]. PLS-SEM is useful for measuring the efficiency of marketing campaigns by attributing sales to each of the advertising channels and calculating marketing ROI [3]

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