Online from: 1986
Subject Area: Marketing
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|Title:||B2B relationship marketing analytical support with GBC modeling|
|Author(s):||Wojciech Peter Latusek, (Barclays Bank plc, London, UK)|
|Citation:||Wojciech Peter Latusek, (2010) "B2B relationship marketing analytical support with GBC modeling", Journal of Business & Industrial Marketing, Vol. 25 Iss: 3, pp.209 - 219|
|Keywords:||Business-to-business marketing, Marketing strategy, Modelling, Relationship marketing|
|Article type:||Research paper|
|DOI:||10.1108/08858621011027803 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||Received: April 2008Revised: January 2009Accepted: March 2009|
Purpose – Discrete choice modeling has been discussed by both academics and practitioners as a means of analytical support for B2C relationship marketing. This paper aims to discuss applying this analytical framework in B2B marketing, with an example of cross-selling high-tech services to a large business customer. This example is also used to show how an algorithm of genetic binary choice (GBC) modeling, developed by the author, performs in comparison with major techniques used nowadays, and to analyze the financial impact of these different approaches on profitability of B2B relationship marketing operations.
Design/methodology/approach – Predictive models based on the regression analysis, the classification tree and the GBC algorithm are built and analyzed in the context of their performance in optimizing cross-selling campaigns. An example of business case analysis is used to estimate the financial implications of the different approaches.
Findings – B2B relationship marketing, although differing from B2C in many aspects, can also benefit from analytical support with discrete choice modeling. The financial impact of such support is significant, and can be further increased by improving the predictive accuracy of the models. In this context the GBC modeling algorithm proves to be an interesting alternative to the algorithms used nowadays.
Research limitations/implications – The generalizability of the findings, concerning performance characteristics of the algorithms, is limited: which method is best depends, for example, on data distributions and the particular relationships being modeled.
Practical implications – The paper shows how B2B marketing managers can increase the profitability of relationship marketing using discrete choice modeling, and how implementing new algorithms like the GBC model presented here can allow for further improvement.
Originality/value – The paper bridges the gap between research on binary choice modeling and the practice of B2B relationship marketing. It presents a new possibility of analytical support for B2B marketing operations together with financial implications. It also includes a demonstration of an algorithm newly developed by the author.
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