“Advanced data analysis techniques with marketing applications”

Globalization and digitalization drive a rapidly increasing amount of all kinds of data, e.g., customer reviews from online shopping platforms, posts on social media, store-level sales data, or promotional data, that are particularly relevant for marketing applications. The increasing availability of marketing data poses a huge challenge for marketing managers. In particular, marketing managers are nowadays expected to rely on marketing data in order to derive and/or improve marketing decisions. Beyond managerial experiences, it is therefore necessary to continuously develop advanced data analysis techniques to extract the relevant information from marketing data for further use in quantitative marketing models, which in turn serve as decision support systems for final marketing decisions. Especially, the estimation of data using appropriate models and the development of accurate predictions based on the estimated model outcomes build the basis for successful marketing activities.

All these research issues can be bundled in so-called quantitative marketing research, and especially data analysis with marketing applications gained in popularity over the last decades. Overviews of discussions of quantitative marketing models can be found in several textbooks, e.g., Franses and Paap (2001), Franses and Montgomery (2002), Diamantopoulos, Fritz and Hildebrandt (2013), Moutinho and Huarn (2015), and date back to the 1980’s with textbooks of, for example, Maddala (1983) or Lilien and Kotler (1983).

In general, data analyses can be categorized into three kinds of analyses, i.e., descriptive analyses, predictive analyses and prescriptive analyses (Akerkar 2013; Šikšnys and Pedersen 2016). Descriptive analyses focus on data mining techniques and the aggregation of data. This kind of analyses helps marketing managers to review past situations, e.g., the number of consumer purchases in a particular time period or in a particular store.

Descriptive analyses focus on data from the time period between an event root cause and an event indication (cp. Figure 1). Predictive analyses rely on forecasting techniques and statistical modelling, i.e., contrary to controlling past marketing activities, these kinds of analyses help marketing managers to predict certain events. Prescriptive analyses incorporate the output from predictive analyses and use optimization methods, decision trees etc. to proactively suggest the best marketing decision option and its implementation.

Predictive analyses significantly contribute to business value, as is revealed by the temporal sequence of data analyses steps in Fig. 1. However, a loss in business value between the event prediction and the proactive (marketing) decision becomes obvious. This loss results from a particular time interval between these two dates. Hence, timely predictions of emerging events are key for marketing managers in order to minimize the potential loss of business value over time (cp., Lepenioti et al. 2020, p. 58). To facilitate this, advanced data analysis techniques are mandatory.

Besides the categorization of data analysis techniques, marketing data itself can be classified according to several dimensions: on the one hand, they can represent either revealed preferences from real purchasing activities of consumers (e.g., based on scanner data or click stream data), or stated preferences gained from experimental settings (e.g., based on conjoint or discrete choice approaches). On the other hand, marketing data can be collected at the disaggregate consumer level (e.g., household panel data) or at the aggregate consumer level (e.g. sales data). Once the marketing data is thoroughly analyzed, the results can be used for descriptive or predictive analyses and for improving or optimizing marketing decisions, i.e., prescriptive analyses.

Sophisticated models, that are able to process complex data, e.g., high volume data or data with missing values, are nowadays state-of-the-art in marketing theory and practice. Besides the application of these established models to new data sets, e.g., data from new application fields, further development of advanced model estimation techniques is highly crucial to improve parameter estimates. Those estimates build the basis for further predictions and marketing actions. Hence, research in these fields is highly valuable for sound managerial implications.

The five papers of this special issue cover such developments in quantitative marketing research by addressing key theoretical and practical aspects that are highly relevant for marketing managers to derive the best marketing decisions in varying application fields in marketing. The articles cover the fields of predictive and prescriptive analytics as well as proceed varying kinds of marketing data. The topics of the contributions are therefore up-to-date to the ongoing stream in the relevant marketing literature.

The five articles in this special issue give an impression on the variety of theoretical issues in quantitative marketing research as well as on the broad spectrum of applications in marketing practice. Furthermore, they highlight the importance of research for advanced data analysis techniques in different fields of data analysis.

We would like to sincerely thank all authors as well as all reviewers for their contributions to this special issue and wish readers an insightful reading.