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

Author

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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
    These authors contributed equally to this work.)

  • Xavier Vilasís Cardona

    (Data Science for the Digital Society (DS4DS) Research Group, La Salle-Ramon Llull University, 08022 Barcelona, Spain
    These authors contributed equally to this work.)

  • Alexandre Lerma Martín

    (SEAT S.A., 08760 Martorell, Spain
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Juan Manuel García Sánchez & Xavier Vilasís Cardona & Alexandre Lerma Martín, 2022. "Influence of Car Configurator Webpage Data from Automotive Manufacturers on Car Sales by Means of Correlation and Forecasting," Forecasting, MDPI, vol. 4(3), pages 1-20, July.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:3:p:34-653:d:860486
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    1. Sa-ngasoongsong, Akkarapol & Bukkapatnam, Satish T.S. & Kim, Jaebeom & Iyer, Parameshwaran S. & Suresh, R.P., 2012. "Multi-step sales forecasting in automotive industry based on structural relationship identification," International Journal of Production Economics, Elsevier, vol. 140(2), pages 875-887.
    2. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    3. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    4. Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2012. "Web Search Queries Can Predict Stock Market Volumes," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-17, July.
    5. Fogliatto, Flavio S. & da Silveira, Giovani J.C. & Borenstein, Denis, 2012. "The mass customization decade: An updated review of the literature," International Journal of Production Economics, Elsevier, vol. 138(1), pages 14-25.
    6. Linda Zhang & Carman K.M. Lee & Pervaiz Akhtar, 2020. "Towards customization: Evaluation of integrated sales, product, and production configuration," Post-Print hal-03276827, HAL.
    7. Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
    8. Marian Alexander Dietzel & Nicole Braun & Wolfgang Schäfers, 2014. "Sentiment-based commercial real estate forecasting with Google search volume data," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 32(6), pages 540-569, August.
    9. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    10. Zhang, Linda L. & Lee, Carman K.M. & Akhtar, Pervaiz, 2020. "Towards customization: Evaluation of integrated sales, product, and production configuration," International Journal of Production Economics, Elsevier, vol. 229(C).
    11. Marian Alexander Dietzel & Nicole Braun & Wolfgang Schäfers, 2014. "Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data," ERES eres2014_17, European Real Estate Society (ERES).
    12. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
    13. Wochner, Sina & Grunow, Martin & Staeblein, Thomas & Stolletz, Raik, 2016. "Planning for ramp-ups and new product introductions in the automotive industry: Extending sales and operations planning," International Journal of Production Economics, Elsevier, vol. 182(C), pages 372-383.
    14. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
    15. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    16. Matthias Holweg & Frits K. Pil, 2005. "The Second Century: Reconnecting Customer and Value Chain through Build-to-Order Moving Beyond Mass and Lean in the Auto Industry," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262582627, December.
    17. repec:arz:wpaper:eres2014-17 is not listed on IDEAS
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