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Machine Learning and Big Data

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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.

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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.

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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
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  2. Jaap E. Wieringa
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  3. Koen H. Pauwels
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Correspondence to Raoul V. Kübler .

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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

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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

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