Skip to main content
SpringerLink
Log in

Machine Learning and Big Data

  • Chapter
  • First Online:
Advanced Methods for Modeling Markets

Part of the book series: International Series in Quantitative Marketing ((ISQM))

Abstract

The last 10 years saw a remarkable increase of data available to marketers. What was considered 5 years ago as big data (see e.g., Vol. I, Sect. 3.5.6) was based on hundreds of thousands of observations (e.g., Reimer et al. 2014). Today this is considered by data scientists as an average sample size. Consumer handscan panels and regular brand tracking allowed us to build models combining consumer actions with mindset metrics (e.g., Hanssens et al. 2014; Van Heerde et al. 2008). And of course, click stream data inspired a brave new world of modeling (prospective) customers’ online decision journeys (Bucklin and Sismeiro 2003; Pauwels and Van Ewijk 2014). Within the last two years we see social media interaction data coming more and more into marketing research (see e.g., Borah and Tellis 2016; Ilhan et al. 2016). Through this source marketers get access to customer and user generated content. Especially service and fast mover consumer goods as entertainment brands enjoy a high volume of interactions (see e.g., Henning-Thurau et al. 2014) in different social media channels that is linked with the company’s future sales performance. Social media again boosts the number of data available to marketers. Instead of now facing hundreds of thousands of observations, researchers are very likely to encounter millions of comments, likes and shares in even short observation periods. Combining this new data with existing sources, will provide new opportunities and further develop marketing research (Sudhir 2016). Verhoef et al. (2016) provide many examples how big data (analytics) can be used to create value for customers and firms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    See also Chap. 17.

  2. 2.

    Some tree-structured models use different splitting algorithms such as Gini-Impurity (see e.g., Kuhn and De Mori 1995) or Variance Reduction (see e.g., Gascuel 2000). Despite formal differences, the main concept behind this is still to improve in group homogeneity by comparing purity before and after the split.

  3. 3.

    Compare the split in data into an estimation (training set) and a validation sample (hold out set): Vol. I, Sect. 5.7.

References

  • Anderson, C.R.: A Machine Learning Approach to Web Personalization. University of Washington Press, Washington, DC (2002)

    Google Scholar 

  • Bennett, K.P., Wu, D., Auslender, L.: On support vector decision trees for database marketing. Neural Netw. 2, 904–909 (1999)

    Article  Google Scholar 

  • Biggs, D., De Ville, B., Suen, E.: A method of choosing multi-way partitions for classification and decision trees. J. Appl. Stat. 18, 49–62 (1991)

    Article  Google Scholar 

  • Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97, 245–271 (1997)

    Article  Google Scholar 

  • Borah, A., Tellis, G.J.: Halo (spillover) effects in social media: do product recalls of one brand hurt or help rival brands? J. Mark. Res. 53, 143–160 (2016)

    Article  Google Scholar 

  • Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory—COLT, pp. 144–146 (1992)

    Google Scholar 

  • Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)

    Google Scholar 

  • Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, CA (1984)

    Google Scholar 

  • Buckinx, W., Moons, E., Van den Poel, D., Wets, G.: Customer-adapted coupon targeting using feature selection. Expert Syst. Appl. 26, 509–518 (2004)

    Article  Google Scholar 

  • Bucklin, R.E., Sismeiro, C.: A model of web site browsing behavior estimated on clickstream data. J. Mark. Res. 40, 249–267 (2003)

    Article  Google Scholar 

  • Chan, P.K., Stolfo, S.J.: Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection. KDD. 98, 164–168 (1998)

    Google Scholar 

  • Chen, Y., Pavlov, D., Canny J.F.: Large-scale behavioral targeting. In: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining ACM (2009)

    Google Scholar 

  • Cheng, J., Greiner, R.: Comparing Bayesian network classifiers. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 101–108 (1999)

    Google Scholar 

  • Chevalier, J.A., Mayzlin, D.: The effect of word of mouth on sales: online book reviews. J. Mark. Res. 43, 345–354 (2006)

    Article  Google Scholar 

  • Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    Google Scholar 

  • Cover, T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electron. Comput. 14, 326–334 (1965)

    Article  Google Scholar 

  • Cruz, J.A., Wishart, D.S.: Applications of machine learning in cancer prediction and prognosis. Cancer Informat. 2, 105–117 (2006)

    Google Scholar 

  • Dietterich, T.: Overfitting and undercomputing in machine learning. Complicat. Surg. 27, 326–327 (1995)

    Google Scholar 

  • Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40, 139–157 (2000)

    Article  Google Scholar 

  • Elith, J., Leathwick, J.R., Hastie, T.: A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008)

    Article  Google Scholar 

  • Esposito, F., Malerba, D., Semeraro, G., Kay, J.: A comparative analysis of methods for pruning decision trees. IEEE. 19, 476–491 (1997)

    Google Scholar 

  • Fayyad, U.M.: Data mining and knowledge discovery–making sense out of data. Intell. Syst. Appl. 11, 20–25 (1996)

    Google Scholar 

  • Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics. 7, 179–188 (1936)

    Article  Google Scholar 

  • Gamon, M., Aue, A., Corston-Oliver, S., Ringger, E.: Pulse: mining customer opinions from free text. In: A.F. Famili, J.N. Kok, J.M. Pena, A. Siebes, A. Feelders (eds.): Advances in Intelligent Data Analysis VI. Springer Berlin 121–132 (2005)

    Google Scholar 

  • Gascuel, O.: On the optimization principle in phylogenetic analysis and the minimum-evolution criterion. Mol. Biol. Evol. 17, 401–405 (2000)

    Article  Google Scholar 

  • Guido, G., Prete, M.I., Miraglia, S., De Mare, I.: Targeting direct marketing campaigns by neural networks. J. Mark. Manag. 27, 992–1006 (2011)

    Article  Google Scholar 

  • Hanssens, D.M., Pauwels, K.H., Srinivasan, S., Vanhuele, M., Yildirim, G.: Consumer attitude metrics for guiding marketing mix decisions. Mark. Sci. 33, 534–550 (2014)

    Article  Google Scholar 

  • Hastie, T., Tibshirani, R., Friedman, J.H.: Boosting and Additive Trees. The Elements of Statistical Learning (2nd ed.). Springer, New York (2009)

    Google Scholar 

  • Henning-Thurau, T., Wiertz, C., and Feldhaus, F.: Does twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. J. Acad. Mark. Sci. 43, 375–394 (2014)

    Google Scholar 

  • Hinton, G.E.: Learning multiple layers of representation. Trends Cogn. Sci. 11, 428–434 (2007)

    Article  Google Scholar 

  • Ho, T.K.: A data complexity analysis of comparative advantages of decision forest constructors. Pattern. Anal. Applic. 5, 102–112 (2002)

    Article  Google Scholar 

  • Homburg, C., Ehm, L., Artz, M.: Measuring and managing consumer sentiment in an online community environment. J. Mark. Res. 52, 629–641 (2015)

    Article  Google Scholar 

  • Ilhan E., Pauwels, K.H., Kübler, R.V.: Dancing with the enemy: broadened understanding of engagement in rival brand dyads, MSI Working Paper Series (2016)

    Google Scholar 

  • Jaworska, J., Sydow, M.: Behavioral targeting in on-line advertising: An empirical study. In: Web Information Systems Engineering-WISE 2008, pp. 62–76. Springer, Berlin (2008)

    Chapter  Google Scholar 

  • Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Appl. Stat. 28, 119–127 (1980)

    Article  Google Scholar 

  • Kim, Y., Street, W.N.: An intelligent system for customer targeting: a data mining approach. Decis. Support. Syst. 37, 215–228 (2004)

    Article  Google Scholar 

  • Kim, Y., Street, W.N., Russell, G.J., Menczer, F.: Customer targeting: a neural network approach guided by genetic algorithms. Manag. Sci. 51, 264–276 (2005)

    Article  Google Scholar 

  • Kübler, R.V., Colicev, A., Pauwels, K.H.: User generated content as a predictor for brand equity. In: Proceedings of the Informs Marketing Science Conference (2016)

    Google Scholar 

  • Kuhn, R., De Mori, R.: The application of semantic classification trees to natural language understanding. Trans. Pattern Anal. Mach. Intell. 17, 449–460 (1995)

    Article  Google Scholar 

  • Levandowsky, M., Winter, D.: Distance between sets. Nature. 234, 34–35 (1971)

    Article  Google Scholar 

  • Li, T., Liu, N., Yan, J., Wang, G., Bai, F., Chen, Z.: A Markov chain model for integrating behavioral targeting into contextual advertising. In: Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising, pp. 1–9 (2009)

    Google Scholar 

  • Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. Internet Comput. 7, 76–80 (2003)

    Article  Google Scholar 

  • Liu, K., Tang, L.: Large-scale behavioral targeting with a social twist. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1815–1824 (2011)

    Google Scholar 

  • Loh, W.Y., Shih, Y.S.: Split selection methods for classification trees. Stat. Sin. 117, 815–840 (1997)

    Google Scholar 

  • Marlin, B.: Collaborative filtering: a machine learning perspective. Dissertation University of Toronto (2004)

    Google Scholar 

  • Martin, J.H., Jurafsky, D.: Speech and Language Processing. Prentice-Hall, Pearson, GA (2000)

    Google Scholar 

  • Mayer-Schönberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work and Think. John Murray, London (2013)

    Google Scholar 

  • Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. AAAI/IAAI. 23, 187–192 (2002)

    Google Scholar 

  • Montgomery, D.C., Peck, E.A.: Introduction to Linear Regression Analysis. Springer, Berlin (1992)

    Google Scholar 

  • Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 427–436 (2015)

    Google Scholar 

  • Nielsen, M.A.: Neural Network and Deep Learning. Determination Press (2015)

    Google Scholar 

  • Osuna, E., Freund, R., Girosit, F.: Training support vector machines: an application to face detection. In: Proceedings of Computer Vision and Pattern Recognition Conference, pp. 130–136 (1997)

    Google Scholar 

  • Pandey, S., Aly, M., Bagherjeiran, A., Hatch, A., Ciccolo, P., Ratnaparkhi, A., Zinkevich, M.: Learning to target: what works for behavioral targeting. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1805–1814 (2011)

    Google Scholar 

  • Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2, 1–135 (2008)

    Article  Google Scholar 

  • Pauwels, K.H., Van Ewijk, B.: Do online behavior tracking or attitude survey metrics drive brand sales? An integrative model of attitudes and actions on the consumer boulevard. Mark. Sci. Inst. Rep. 4, 13–118 (2014)

    Google Scholar 

  • Perlich, C., Dalessandro, B., Hook, R., Stitelman, O., Raeder, T., Provost, F.: Bid optimizing and inventory scoring in targeted online advertising. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 804–812 (2012)

    Google Scholar 

  • Provost, F., Fawcett, T.: Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media, Quinlan, TX (2013)

    Google Scholar 

  • Quinlan, J.R.: Simplifying decision trees. Int. J. Man-Mach. Stud. 27, 221–234 (1987)

    Article  Google Scholar 

  • Reimer, K., Rutz, O.J., Pauwels, K.H.: How online consumer segments differ in long-term marketing effectiveness. J. Interact. Mark. 28, 271–284 (2014)

    Article  Google Scholar 

  • Reutterer, T., Mild, A., Natter, M., Taudes, A.: A dynamic segmentation approach for targeting and customizing direct marketing campaigns. J. Interact. Mark. 20, 43–57 (2006)

    Article  Google Scholar 

  • Ritschard, G.: CHAID and earlier supervised tree methods, accessed online June 12, 2016 at http://www.unige.ch/ses/dsec/repec/files/2010_02.pdf (2010)

  • Rokach, L., Maimon, O.: Data mining with decision trees: theory and applications. World Scientific (2007)

    Google Scholar 

  • Schlangenstein, M.: UPS crunches data to make routes more efficient, save gas, Bloomberg, accessed online, June 7, 2016 at http://www.bloomberg.com/news/articles/2013-10-30/ups-uses-big-data-to-make-routes-more-efficient-save-gas (2013)

  • Schweidel, M., Moe, W.: Listening in on social media: a joint model of sentiment and venue format choice. J. Mark. Res. 51, 387–399 (2014)

    Article  Google Scholar 

  • Shannon, C.E.: A note on the concept of entropy. Bell Syst. Tech. J. 27, 379–423 (1948)

    Article  Google Scholar 

  • Sidorov, G., Miranda-Jiménez, S., Viveros-Jiménez, F., Gelbukh, A., Castro-Sánchez, N., Velásquez, F., Gordon, J.: Empirical study of machine learning based approach for opinion mining in tweets. In: Famili, A.F., Kok, J.N., Pena, J.M., Siebes, A., Feelders, A. (eds.) Advances in Intelligent Data Analysis VI, pp. 1–14. Springer, Berlin (2012)

    Google Scholar 

  • Skiera, B., Abou Nabout, N.: Practice prize paper-PROSAD: a bidding decision support system for profit optimizing search engine advertising. Mark. Sci. 32, 213–220 (2013)

    Article  Google Scholar 

  • Skurichina, M.: Bagging, boosting and the random subspace method for linear classifiers. Pattern. Anal. Applic. 5, 121–135 (2002)

    Article  Google Scholar 

  • Sudhir, K.: The exploration-exploitation tradeoff and efficiency in knowledge production. Mark. Sci. 52, 1–14 (2016)

    Article  Google Scholar 

  • Tang, J., Liu, N., Yan, J., Shen, Y., Guo, S., Gao, B., Zhang, M.: Learning to rank audience for behavioral targeting in display ads. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 605–610 (2011)

    Google Scholar 

  • Van Heerde, H.J., Gijsbrechts, E., Pauwels, K.H.: Winners and losers in a major price war. J. Mark. Res. 45, 499–518 (2008)

    Article  Google Scholar 

  • Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)

    Book  Google Scholar 

  • Verhoef, P.C., Kooge, E., Walk, N.: Creating Value with Big Data Analytics. Rootledge, New York, NY (2016)

    Google Scholar 

  • Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)

    Article  Google Scholar 

  • Wang, C., Raina, R., Fong, D., Zhou, D., Han, J., Badros, G.: Learning relevance from heterogeneous social network and its application in online targeting. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 655–664 (2011)

    Google Scholar 

  • Wasserman, L.: Statistics and Machine Learning, accessed online June, 5 2016 at https://normaldeviate.wordpress.com/2012/06/12/statistics-versus-machine-learning-52 (2012)

  • Wedel, M., Kamakura, W.A.: Market Segmentation: Conceptual and Methodological Foundations. Kluwer Academic, Boston, MA (2000)

    Book  Google Scholar 

  • Wu, C.H., Kao, S.C., Su, Y.Y., Wu, C.C.: Targeting customers via discovery knowledge for the insurance industry. Expert Syst. Appl. 29, 291–299 (2005)

    Article  Google Scholar 

  • Xia, G.E., Jin, W.D.: Model of customer churn prediction on support vector machine. Syst. Eng. Theory Prac. 28, 71–77 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Marketing,  Özyeğin University, Istanbul, Turkey

    Raoul V. Kübler

  2. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Jaap E. Wieringa

  3. Department of Marketing, Northeastern University, Boston, USA

    Koen H. Pauwels

Authors
  1. Raoul V. Kübler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaap E. Wieringa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Koen H. Pauwels
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raoul V. Kübler .

Editor information

Editors and Affiliations

  1. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Peter S. H. Leeflang

  2. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Jaap E. Wieringa

  3. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Tammo H.A Bijmolt

  4. Department of Marketing, D’Amore-McKim School of Business, Northeastern University, Boston, USA

    Koen H. Pauwels

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Kübler, R.V., Wieringa, J.E., Pauwels, K.H. (2017). Machine Learning and Big Data. In: Leeflang, P., Wieringa, J., Bijmolt, T., Pauwels, K. (eds) Advanced Methods for Modeling Markets. International Series in Quantitative Marketing. Springer, Cham. https://doi.org/10.1007/978-3-319-53469-5_19

Download citation

Publish with us

Policies and ethics

Search

Navigation