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Research paper

Data-driven attribute selection for hardware technology products: A multi-criteria framework

Evgeny A. Antipov and Elena B. Pokryshevskaya
Applied Marketing Analytics: The Peer-Reviewed Journal, 9 (2), 173-181 (2023)
https://doi.org/10.69554/ZAXX1677

Abstract

This paper outlines a multiple-criteria approach for supporting manufacturers in making decisions about tech products' technical, aesthetic and price characteristics. The authors propose a predictive modelling approach that shortlists efficient product designs based on their expected profit margin, consumer rating and demand. The method involves collecting SKU (stock keeping unit)-level data on product features from an online marketplace and estimating regression models. These models include a hedonic pricing model, a demand model and a satisfaction model to identify the factors that drive sales, prices and satisfaction. Analysing the model coefficients and their significance allows for identifying cost-efficient product features that positively impact sales and satisfaction. The models also enable predicting the outcomes for various new specifications making it possible to shortlist Pareto-efficient product designs. The approach uses publicly available data and allows for frequent updates, although it has some limitations, such as omitted variable bias and the use of a demand proxy. The authors suggest ways to extend the framework to account for uncertainty in predictions and include more outcomes of interest.

Keywords: product design; consumer preferences; demand estimation; hedonic pricing; satisfaction; regression analysis; machine learning; Pareto efficiency; multi-criteria comparison

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Author's Biography

Evgeny A. Antipov is an associate professor of data analytics at HSE University and the CEO of Smart Data Products — a Dubai Internet City-based analytics consultancy firm specialising in hands-on training and profit-maximising business analytics. Dr Antipov's research is focused on actionable applications of publicly available data.

Elena B. Pokryshevskaya is an associate professor of marketing at HSE University and the Marketing Director of Smart Data Products. Dr Pokryshevskaya's research interests include optimisation and econometric modelling in marketing.

Citation

Antipov, Evgeny A. and Pokryshevskaya, Elena B. (2023, October 1). Data-driven attribute selection for hardware technology products: A multi-criteria framework. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 9, Issue 2. https://doi.org/10.69554/ZAXX1677.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 9 / Issue 2
© Henry Stewart
Publications LLP

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