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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Predicting Automotive Sales using Pre-Purchase Online Search Data

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DOI: http://dx.doi.org/10.15439/2019F239

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 569–577 ()

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Abstract. Sales forecasting is an essential element for implementing sustainable business strategies in the automotive industry. Accurate sales forecasts enhance the competitive edge of car manufacturers in the effort to optimize their production planning processes. We propose a forecasting technique that combines keyword-specific customer online search data with economic variables to predict monthly car sales. To isolate online search data related to pre-purchase information search, we follow a backward induction approach and identify those keywords that are frequently applied by search engine users. In a set of experiments using real-world sales data and Google Trends, we find that our keyword-specific forecasting technique reduces the out-of-sample error by 5\% as compared to existing techniques without systematic keyword selection. We also find that our regression models outperform the benchmark model by an out-of-sample prediction accuracy of up to 27\%.

References

  1. J.-H. Thun and D. Hoenig, “An empirical analysis of supply chain risk management in the German automotive industry,” International Journal of Production Economics, vol. 131, no. 1, pp. 242–249, 2011, http://dx.doi.org/10.1016/j.ijpe.2009.10.010.
  2. J. Roehrich, G. Parry, and A. Graves, “Implementing build-to-order strategies: enablers and barriers in the European automotive industry,” International Journal of Automotive Technology and Management, vol. 11, no. 3, pp. 221–235, 2011, http://dx.doi.org/10.1504/IJATM.2011.040869.
  3. D. Fantazzini and Z. Toktamysova, “Forecasting German car sales using Google data and multivariate models,” International Journal of Production Economics, vol. 170, pp. 97–135, 2015, http://dx.doi.org/10.1016/j.ijpe.2015.09.010.
  4. J. Leukel, A. Jacob, P. Karaenke, S. Kirn, and A. Klein, “Individualization of goods and services: towards a logistics knowledge infrastructure for agile supply chains,” in Proceedings of the 2011 AAAI Spring Symposium on AI for Business Agility, Stanford, CA, USA, 2011, pp. 36–49.
  5. T. Widmer, A. Klein, P. Wachter, and S. Meyl, “Predicting Material Requirements in the Automotive Industry Using Data Mining,” in Business Information Systems, Seville, Spain, 2019, pp. 147–161.
  6. G. Nunnari and V. Nunnari, “Forecasting Monthly Sales Retail Time Series: A Case Study,” in 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, Greece, 2017, pp. 1–6.
  7. K. Akalamkam and J. K. Mitra, “Consumer Pre-purchase Search in Online Shopping: Role of Offline and Online Information Sources,” Business Perspectives and Research, vol. 6, no. 1, pp. 42–60, 2018, http://dx.doi.org/10.1177/2278533717730448.
  8. K. Kandaswam and A. Tiwar. "Driving through the consumer’s mind: Steps in the buying process." https://www2.deloitte.com/content/dam/Deloitte/in/Documents/manufacturing/in-mfg-dtcm-steps-in-the-buying-process-noexp.pdf (accessed Apr. 18, 2019).
  9. EY. "Future of automotive retail Shifting from transactional to customer-centric." https://www.ey.com/Publication/vwLUAssets/EY-future-of-automotive-retail/%24FILE/EY-future-of-automotive-retail.pdf (accessed Apr. 18, 2019).
  10. S. Shahabuddin, “Forecasting automobile sales,” Management Research News, vol. 32, no. 7, pp. 670–682, 2009, http://dx.doi.org/10.1108/01409170910965260.
  11. R.M.J. Heuts and J.H.J.M. Bronckers, “Forecasting the Dutch heavy truck market,” International Journal of Forecasting, vol. 4, no. 1, pp. 57–79, 1988, http://dx.doi.org/10.1016/0169-2070(88)90010-6.
  12. F.-K. Wang, K.-K. Chang, and C.-W. Tzeng, “Using adaptive network-based fuzzy inference system to forecast automobile sales,” Expert Systems with Applications, vol. 38, no. 8, pp. 10587–10593, 2011, http://dx.doi.org/10.1016/j.eswa.2011.02.100.
  13. H. Choi and H. Varian, “Predicting the Present with Google Trends,” Google Inc, 2009.
  14. C. Seebach, I. Pahlke, and R. Beck, “Tracking the Digital Footprints of Customers: How Firms can Improve their Sensing Abilities to Achieve Business Agility,” Proceedings of the 19th European Conference on Information Systems (ecis)), 2011.
  15. Y. Carrière-Swallow and F. Labbé, “Nowcasting with Google Trends in an Emerging Market,” Journal of Forecasting, vol. 32, no. 4, pp. 289–298, 2013, http://dx.doi.org/10.1002/for.1252.
  16. J. Benthaus and C. Skodda, “Investigating consumer information search behavior and consumer emotions to improve sales forecasting,” in Proceedings of the 21 st Americas Conference on Information Systems, Puerto Rico, 2015.
  17. J. Otterbacher, “Searching for product experience attributes in online information sources,” in Proceedings of the International Conference on Information Systems (ICIS 2008), 2008, paper 207.
  18. N. Kumar, K. R. Lang, and Q. Peng, “Consumer Search Behavior in Online Shopping Environments,” in Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA, Jan. 2005, 175b-175b.
  19. statcounter. "Search Engine Market Share United States Of America." http://gs.statcounter.com/search-engine-market-share/all/united-states-of-america (accessed Apr. 18, 2019).
  20. L. Vaughan and Y. Chen, “Data mining from web search queries: A comparison of google trends and baidu index,” Journal of the Association for Information Science and Technology, vol. 66, no. 1, pp. 13–22, 2015, http://dx.doi.org/10.1002/asi.23201.
  21. T. Geva, G. Oestreicher-Singer, N. Efron, and Y. Shimshoni, “Using forum and search data for sales prediction of high-involvement products,” MIS Quarterly, vol. 41, no. 1, pp. 65–82, 2017, http://dx.doi.org/10.25300/MISQ/2017/41.1.04.
  22. M. Banbura, D. Giannone, and L. Reichlin, “Nowcasting,” ECB Working Paper No. 1275, 2010.
  23. J. Ginsberg et al., “Detecting influenza epidemics using search engine query data,” Nature, vol. 457, no. 7232, pp. 1012–1014, 2009, http://dx.doi.org/10.1038/nature07634.
  24. P. M. Polgreen, Y. Chen, D. M. Pennock, and F. D. Nelson, “Using internet searches for influenza surveillance,” (eng), Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, vol. 47, no. 11, pp. 1443–1448, 2008, http://dx.doi.org/10.1086/593098.
  25. A. F. Dugas et al., “Influenza forecasting with Google Flu Trends,” (eng), PloS one, vol. 8, no. 2, e56176, 2013, http://dx.doi.org/10.1371/journal.pone.0056176.
  26. J. Pavlicek and L. Kristoufek, “Nowcasting unemployment rates with google searches: Evidence from the visegrad group countries,” PloS one, vol. 10, no. 5, e0127084, 2015, http://dx.doi.org/10.1371/journal.pone.0127084.
  27. Y. Fondeur and F. Karamé, “Can Google data help predict French youth unemployment?,” Economic Modelling, vol. 30, no. C, pp. 117–125, 2013, http://dx.doi.org/10.1016/j.econmod.2012.07.017.
  28. N. Askitas and K. F. Zimmermann, “Google econometrics and unemployment forecasting,” Applied Economics Quarterly, vol. 55, no. 2, pp. 107–120, 2009, http://dx.doi.org/10.2139/ssrn.1465341.
  29. F. D’Amuri and J. Marcucci, “The predictive power of Google searches in forecasting US unemployment,” International Journal of Forecasting, vol. 33, no. 4, pp. 801–816, 2017, http://dx.doi.org/10.1016/j.ijforecast.2017.03.004.
  30. N. Barreira, P. Godinho, and P. Melo, “Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends,” NETNOMICS: Economic Research and Electronic Networking, vol. 14, no. 3, pp. 129–165, 2013, http://dx.doi.org/10.1007/s11066-013-9082-8.
  31. T. Preis, H. S. Moat, and H. E. Stanley, “Quantifying trading behavior in financial markets using Google Trends,” Scientific reports, vol. 3, p. 1684, 2013, http://dx.doi.org/10.1038/srep01684.
  32. L. Bijl, G. Kringhaug, P. Molnár, and E. Sandvik, “Google searches and stock returns,” International Review of Financial Analysis, vol. 45, no. C, pp. 150–156, 2016, http://dx.doi.org/10.1016/j.irfa.2016.03.015.
  33. H. Choi and H. Varian, “Predicting the Present with Google Trends,” Economic Record, vol. 88, no. 1, pp. 2–9, 2012, http://dx.doi.org/10.1111/j.1475-4932.2012.00809.x.
  34. L. Wu and E. Brynjolfsson, “Chapter 3 - The Future of Prediction,” in Economic Analysis of the Digital Economy, A. Goldfarb, S. M. Greenstein, and C. E. Tucker, Eds.: University of Chicago Press, 2015, pp. 89–118.
  35. G. Chamberlin, “Googling the present,” Economic & Labour Market Review, vol. 4, no. 12, pp. 59–95, 2010, http://dx.doi.org/10.1057/elmr.2010.166.
  36. R. Y. Du and W. A. Kamakura, “Quantitative trendspotting,” Journal of Marketing Research, vol. 49, no. 4, pp. 514–536, 2012, http://dx.doi.org/10.1509/jmr.10.0167.
  37. A. Inoue and L. Kilian, “In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?,” Econometric Reviews, vol. 23, no. 4, pp. 371–402, 2005, http://dx.doi.org/10.1081/ETC-200040785.
  38. F. Wijnhoven and O. Plant, “Sentiment analysis and Google trends data for predicting car sales,” in 38th International Conference on Information Systems, 2017.
  39. P. Nymand-Andersen and E. Pantelidis, “Google econometrics: nowcasting euro area car sales and big data quality requirements,” ECB Statistics Paper, 2018.
  40. G. von Graevenitz, C. Helmers, V. Millot, and O. Turnbull, “Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK,” CGR Working Paper, 2016, http://dx.doi.org/10.2139/ssrn.2832004.
  41. Y. Hu, R. Y. Du, and S. Damangir, “Decomposing the Impact of Advertising: Augmenting Sales with Online Search Data,” Journal of Marketing Research, vol. 51, no. 3, pp. 300–319, 2014, http://dx.doi.org/10.1509/jmr.12.0215.
  42. Google. "How Trends data is adjusted." https://support.google.com/trends/answer/4365533?hl=en&ref_topic=6248052 (accessed Apr. 18, 2019).
  43. A. Ross, “Nowcasting with Google Trends: a keyword selection method,” Fraser of Allander Economic Commentary, vol. 37, no. 2, pp. 54–64, 2013.
  44. L. R. Klein and G. T. Ford, “Consumer search for information in the digital age: An empirical study of prepurchase search for automobiles,” Journal of Interactive Marketing, vol. 17, no. 3, pp. 29–49, 2003, http://dx.doi.org/10.1002/dir.10058.
  45. A. F. Siegel, “Multiple Regression,” in Practical Business Statistics: Elsevier, 2016, pp. 355–418.
  46. Carsalebase. "Automotive Industry analysis, opinions and data." carsalesbase.com/ (accessed Apr. 18, 2019).
  47. K. Afrin, B. Nepal, and L. Monplaisir, “A data-driven framework to new product demand prediction: Integrating product differentiation and transfer learning approach,” Expert Systems with Applications, vol. 108, pp. 246–257, 2018, http://dx.doi.org/10.1016/j.eswa.2018.04.032.
  48. M. Hülsmann, D. Borscheid, C. M. Friedrich, and D. Reith, “General Sales Forecast Models for Automobile Markets and their Analysis,” Transactions on Machine Learning and Data Mining, vol. 5, no. 2, pp. 65–86, 2012.
  49. A. Sa-ngasoongsong, S. T.S. Bukkapatnam, J. Kim, P. S. Iyer, and R. P. Suresh, “Multi-step sales forecasting in automotive industry based on structural relationship identification,” International Journal of Production Economics, vol. 140, no. 2, pp. 875–887, 2012, http://dx.doi.org/10.1016/j.ijpe.2012.07.009.
  50. J. Gao, Y. Xie, X. Cui, H. Yu, and F. Gu, “Chinese automobile sales forecasting using economic indicators and typical domestic brand automobile sales data: A method based on econometric model,” Advances in Mechanical Engineering, vol. 10, no. 2, 1-11, 2018, http://dx.doi.org/10.1177/1687814017749325.