Influence of Car Configurator Webpage Data from Automotive Manufacturers on Car Sales by Means of Correlation and Forecasting

Juan Manuel García Sánchez, Xavier Vilasís Cardona and Alexandre Lerma Martín
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Juan Manuel García Sánchez: Data Science for the Digital Society (DS4DS) Research Group, La Salle-Ramon Llull University, 08022 Barcelona, Spain
Xavier Vilasís Cardona: Data Science for the Digital Society (DS4DS) Research Group, La Salle-Ramon Llull University, 08022 Barcelona, Spain
Alexandre Lerma Martín: SEAT S.A., 08760 Martorell, Spain

Forecasting, 2022, vol. 4, issue 3, 1-20

Abstract: A methodology to prove the influence of car configurator webpage data for automotive manufacturers is developed across this research. Firstly, the correlation between online data and sales is measured. Afterward, car variant sales are predicted using a set of forecasting techniques divided into univariate and multivariate ones. Finally, weekly color mix sales based on these techniques are built and compared. Results show that users visit car configurator webpages 1 to 6 months before the purchase date. Additionally, car variants predictions and weekly color mix sales derived from multivariate techniques, i.e., using car configurator data as external input, provide improvement up to 25 points in the assessment metric.

Keywords: forecasting; prediction; machine learning; time series; car configurator data; automotive OEMs; Pearson correlation coefficient; weekly color mix sales (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
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