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Article
Publication date: 4 November 2014

Keith Becker, Jim Sprigg and Alex Cosmas

The purpose of this paper is to estimate individual promotional campaign impacts through Bayesian inference. Conventional statistics have worked well for analyzing the…

Abstract

Purpose

The purpose of this paper is to estimate individual promotional campaign impacts through Bayesian inference. Conventional statistics have worked well for analyzing the impact of direct marketing promotions on purchase behavior. However, many modern marketing programs must drive multiple purchase objectives, requiring more precise arbitration between multiple offers and collection of more data with which to differentiate individuals. This often results in datasets that are highly dimensional, yet also sparse, straining the power of statistical methods to properly estimate the effect of promotional treatments.

Design/methodology/approach

Improvements in computing power have enabled new techniques for predicting individual behavior. This work investigates a probabilistic machine-learned Bayesian approach to predict individual impacts driven by promotional campaign offers for a leading global travel and hospitality chain. Comparisons were made to a linear regression, representative of the current state of practice.

Findings

The findings of this work focus on comparing a machine-learned Bayesian approach with linear regression (which is representative of the current state of practice among industry practitioners) in the analysis of a promotional campaign across three key areas: highly dimensional data, sparse data and likelihood matching.

Research limitations/implications

Because the findings are based on a single campaign, future work includes generalizing results across multiple promotional campaigns. Also of interest for future work are comparisons of the technique developed here with other techniques from academia.

Practical implications

Because the Bayesian approach allows estimation of the influence of the promotion for each hypothetical customer’s set of promotional attributes, even when no exact look-alikes exist in the control group, a number of possible applications exist. These include optimal campaign design (given the ability to estimate the promotional attributes that are likely to drive the greatest incremental spend in a hypothetical deployment) and operationalizing efficient audience selection given the model’s individualized estimates, reducing the risk of marketing overcommunication, which can prompt costly unsubscriptions.

Originality/value

The original contribution is the application of machine-learning to Bayesian Belief Network construction in the context of analyzing a multi-channel promotional campaign’s impact on individual customers. This is of value to practitioners seeking alternatives for campaign analysis for applications in which more commonly used models are not well-suited, such as the three key areas that this paper highlights: highly dimensional data, sparse data and likelihood matching.

Details

Journal of Consumer Marketing, vol. 31 no. 6/7
Type: Research Article
ISSN: 0736-3761

Keywords

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Article
Publication date: 11 September 2007

Craig R. Carter, Lutz Kaufmann and Alex Michel

The purpose of this paper is to review and integrate the extensive literature base which examines judgment and decision‐making biases, to introduce this literature to the…

Abstract

Purpose

The purpose of this paper is to review and integrate the extensive literature base which examines judgment and decision‐making biases, to introduce this literature to the field of supply management, to create a valid, mutually exclusive, and exhaustive taxonomy of decision biases that can affect supply managers, and to provide guidance for future research and applications of this taxonomy.

Design/methodology/approach

The authors use a qualitative cluster analysis, combined with a Q‐sort methodology, to develop a taxonomy of decision biases.

Findings

A mutually exclusive, and exhaustive taxonomy of nine decision biases is developed through a qualitative cluster analysis. The Q‐sort methodology provides initial confirmation of the reliability and validity of the cluster analysis results. The findings, along with numerous examples provided in the text, suggest that supply management decisions are vulnerable to the described biases.

Originality/value

This paper provides a comprehensive review of the judgment and decision bias literature, and creates a logical and manageable taxonomy of biases which can impact supply management decision making. The introduction and organization of this vast extant literature base provides a contrasting perspective to much of the existing supply management research, which has incorporated the assumption of the rational agent, or what is known in the economics literature as homo economicus. In addition, the authors describe the use of qualitative cluster analysis and the Q‐sort methodology, techniques which have been used rarely if at all in within the field of supply chain management.

Details

International Journal of Physical Distribution & Logistics Management, vol. 37 no. 8
Type: Research Article
ISSN: 0960-0035

Keywords

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