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Ask Less: Scale Market Research Without Annoying Your Customers

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Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications (AISGSC 2019 2019)

Abstract

Market research is generally performed by surveying a representative sample of customers with questions that include contexts such as psychographics, demographics, attitude, and product preferences. Survey responses are used to segment the customers into various groups that are useful for targeted marketing and communication. Reducing the number of questions asked to the customer has utility for businesses to scale the market research to a large number of customers. In this work, we model this task using Bayesian networks. We demonstrate the effectiveness of our approach using an example market segmentation of broadband customers.

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Notes

  1. 1.

    Ipsos and TNS are well-known market research experts in the industry.

Abbreviations

AIC:

Akaike information criterion

B2C:

Business to customer

BIC:

Bayesian information criterion

BN:

Bayesian network

DAG:

Directed acyclic graph

ISP:

Internet service provider

References

  1. Wind Y, Douglas SP (1972) International market segmentation. Eur J Mark 6(1):17–25

    Article  Google Scholar 

  2. Bradburn NM, Sudman S, Wansink B (2004) Asking questions: the definitive guide to questionnaire design – for market research, political polls, and social and health questionnaires. Wiley, New York

    Google Scholar 

  3. Cremonezi L (2016) High definition customers – a powerful segmentation. White paper, Ipsos MORI

    Google Scholar 

  4. Andrew Z, Peter D (2011) A guide to getting the best out of your segmentation analyses

    Google Scholar 

  5. Fricker Jr, Kulzy W, Appleget J (2012) From data to information: Using factor analysis with survey data, pp 30–34

    Google Scholar 

  6. Ehrenberg ASC, Goodhardt GJ S. I. M: Factor analysis: limitations and alternatives. Marketing Science Institute, Cambridge

    Google Scholar 

  7. Beri G (2007) Marketing research. Tata McGraw-Hill Education, New Delhi

    Google Scholar 

  8. Karvanen J, Rantanen A, Luoma L (2014) Survey data and Bayesian analysis: a cost-efficient way to estimate customer equity. QME Quant Mark Econ 12:305–329

    Article  Google Scholar 

  9. Constantinou A, Fenton N, Marsh W, Radlinski L (2016) From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artif Intell Med 67:75–93

    Article  Google Scholar 

  10. Toyinbo P, Vanderploeg R, Belanger H, Spehar A, Lapcevic W, Scott S (2017) A systems science approach to understanding polytrauma and blast-related injury: Bayesian network model of data from a survey of the Florida National Guard. Am J Epidemiol 185(2):135–146

    Article  Google Scholar 

  11. Salini S, Kenett R (2009) Bayesian networks of customer satisfaction survey data. J Appl Stat 36(11):1177–1189

    Article  MathSciNet  Google Scholar 

  12. Friedman N, Murphy K, Russell S (1998) Learning the structure of dynamic probabilistic networks. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp 139–147

    Google Scholar 

  13. Heckerman D, Geiger D, Chickering D (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20(3):197–243

    MATH  Google Scholar 

  14. Steck H (2001) Constraint-based structural learning in Bayesian networks using finite data sets

    Google Scholar 

  15. de Campos C, Ji Q (2011) Efficient structure learning of Bayesian networks using constraints. J Mach Learn Res 12(Mar):663–689

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

We thank Prasad Garigipati, Henrik Palson, Andreas Timglas, and Roy Ollila for their help and support. Both the authors were introduced to the area of Market Research during their tenure at Xoanon Analytics. The value in asking fewer questions in a Market Research Survey was recognized by the authors based on their practical experience.

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Correspondence to Venkatesh Umaashankar .

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Umaashankar, V., Girish Shanmugam, S. (2020). Ask Less: Scale Market Research Without Annoying Your Customers. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_56

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  • DOI: https://doi.org/10.1007/978-3-030-24051-6_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24050-9

  • Online ISBN: 978-3-030-24051-6

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