JOURNAL OF MARKETING ANALYTICS

Special Issue:
“New Frontiers in Forecasting, Predicting, and Explaining”

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While data continues to proliferate in the current century, the marketing analytical toolkit associated with forecasting and predicting continues to be rooted in previous centuries. Software titles continued to add functionality for practitioners and academicians alike. These tools can help novice and experienced researchers tackle large datasets with more accessible functionality associated with organizing, analyzing, and visualizing data. However, easier does not mean better, and we need more significant insights into the phenomenon of predictive analytics (Brynjolfsson, Wang, & McElheran, 2021).

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Larger datasets allow researchers to develop models that provide more explanatory power and test models for better predictiveness (Sarstedt & Danks, 2021). Those models contain biases or can reinforce biasedness in the absence of ethical or legal considerations (Petrescu & Krishen, 2020). Analytical techniques should also extend into working with data from structured and unstructured sources, which are estimated to account for about 80% of available data (Petrescu & Krishen, 2019b). Hair (2007) estimated that predictive analytics would use more mixed-data models that examine both structured and unstructured data and estimated that an effective application of quantitative techniques would depend on improved qualitative research. Model development and testing should strongly bridge the gap between academic inquiry and managerial application to ensure that the marketing analytical toolkit remains relevant (Petrescu & Krishen, 2019a). Moreover, leading marketing analytics scholars have been encouraging more theory-based criteria for managers concerning marketing analytics use and interpretation (Iacobucci et al., 2019).

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This special issue on forecasting, predictiveness, and explanation for the Journal of Marketing Analytics seeks to increase our understanding of how current analytical techniques can be improved and new analytical techniques can be applied. Specifically, we are seeking papers that address such issues, including:

• Differentiating between prediction and explanation

• Predicting with unstructured data

• Theory development and testing with big data

• Moving toward more robust means of forming groups or segments

• Incorporating time series to analysis

• Bias explicit and/or implicit in forecasting and predicting techniques

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Other topics related to forecasting, predicting, or explaining in the context of marketing analytics are encouraged.

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Authors should submit their conceptual or empirical pieces to the Marketing Analytics track as part of the annual Society for Marketing Advances conference 2022. This year’s conference will be held Nov. 2-5, in Charlotte, North Carolina.

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Special issue editors, Journal of Marketing Analytics:

Dr. Michael A. Levin

Otterbein University, Westerville, OH
mlevin@otterbein.edu

Dr. John Gironda
University of North Carolina Wilmington, NC
girondaj@uncw.edu

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References:

Brynjolfsson, E., Wang, J., & McElheran, K. (2021). The power of prediction: Predictive analytics, workplace complements, and business performance. Business Economics, 56(4), 217-239.

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Hair, J. F. Jr. (2007). Knowledge creation in marketing: The role of predictive analytics. European Business Review, 19(4), 303-315

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Iacobucci, D., Petrescu, M., Krishen, A. and M. Bendixen (2019). The state of marketing analytics in research and practice. Journal of Marketing Analytics, 7, 152–181.

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Petrescu, M., & Krishen, A. S. (2020). The dilemma of social media algorithms and analytics. Journal of Marketing Analytics, 8(4), 187-188.

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Petrescu, M., & Krishen, A. S. (2019a). Software and data in analytics: lending theory to practice. Journal of Marketing Analytics, 7(3), 125-126.

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Petrescu, M., & Krishen, A. S. (2019b). Strength in diversity: methods and analytics. Journal of Marketing Analytics, 7(4), 203-204.

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Sarstedt, M., & Danks, N. P. (2021). Prediction in HRM research–a gap between rhetoric and reality. Human Resource Management Journal, 1-29. https://doi.org/10.1111/1748-8583.12400

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