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Towards an Efficient Method of Modeling “Next Best Action” for Digital Buyer’s Journey in B2B

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10357))

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Abstract

The rise of Digital B2B Marketing has presented us with new opportunities and challenges as compared to traditional e-commerce. B2B setup is different from B2C setup in many ways. Along with the contrasting buying entity (company vs. individual), there are dissimilarities in order size (few dollars in e-commerce vs. up to several thousands of dollars in B2B), buying cycle (few days in B2C vs. 6-18 months in B2B) and most importantly a presence of multiple decision makers (individual or family vs. an entire company). Due to easy availability of the data and bargained complexities, most of the existing literature has been set in the B2C framework and there are not many examples in the B2B context. We present a unique approach to model next likely action of B2B customers by observing a sequence of digital actions. In this paper, we propose a unique two-step approach to model next likely action using a novel ensemble method that aims to predict the best digital asset to target customers as a next action. The paper provides a unique approach to translate the propensity model at an email address level into a segment that can target a group of email addresses. In the first step, we identify the high propensity customers for a given asset using traditional and advanced multinomial classification techniques and use non-negative least squares to stack rank different assets based on the output for ensemble model. In the second step, we perform a penalized regression to reduce the number of coefficients and obtain the satisfactory segment variables. Using real world digital marketing campaign data, we further show that the proposed method outperforms the traditional classification methods.

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References

  1. Imber, J., Toffler, B. A.:  Dictionary of marketing terms. Barron’s snippet (2008)

    Google Scholar 

  2. Farhoomand, A. F., Lovelock, P.:. Global e-Commerce: Text and Cases Plus Instructor’s Manual (2001)

    Google Scholar 

  3. Edelman, D. C.: Four ways to get more value from digital marketing. McKinsey Quarterly 6 (2010)

    Google Scholar 

  4. Strauss, J.:  E-marketing. Routledge (2016)

    Google Scholar 

  5. Kian Chong, W., Shafaghi, M., Woollaston, C., Lui, V.: B2B e-marketplace: an e-marketing framework for B2B commerce. Marketing Intelligence & Planning 28(3), 310–329 (2010)

    Article  Google Scholar 

  6. Miller, M.:. B2B digital marketing: Using the web to market directly to businesses. Que Publishing (2012)

    Google Scholar 

  7. Järvinen, J., Tollinen, A., Karjaluoto, H., Jayawardhena, C.: . Digital and Social Media Marketing Usage in B2B Industrial Section. Marketing Management Journal 22(2) (2012)

    Google Scholar 

  8. Leeflang, P.S., Verhoef, P.C., Dahlström, P., Freundt, T.: Challenges and solutions for marketing in a digital era. European Management Journal 32(1), 1–12 (2014)

    Article  Google Scholar 

  9. Burez, J., Van den Poel, D.: CRM at a pay-tv company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications 32(2), 277–288 (2007)

    Article  Google Scholar 

  10. Nafis, S., Makhtar, M., Awang, M. K., Rahman, M. N. A., Deris, M. M.: Feature Selections and Classification Model for Customer Churn. Journal of Theoretical & Applied Information Technology 75(3) (2015)

    Google Scholar 

  11. Xie, Y., Li, X., Ngai, E.W.T., Ying, W.: Customer churn prediction using improved balanced random forests. Expert Systems with Applications 36(3), 5445–5449 (2009)

    Article  Google Scholar 

  12. Balakrishnan, R., Parekh, R.:. Learning to predict subject-line opens for large-scale email marketing. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 579–584. IEEE, October 2014

    Google Scholar 

  13. Szücs G.: Decision trees and random forest for privacy-preserving data mining. In: Research and Development in E-Business through Service-oriented Solution, pp. 71–90. IGI Global, Hershey, PA,USA

    Google Scholar 

  14. Pushpavathi, T. P., Suma, V., Ramaswamy, V.: Defect Prediction in Software Projects-Using Genetic Algorithm based Fuzzy C-Means Clustering and Random Forest Classifier. International Journal of Scientific & Engineering Research 5(9) (2014)

    Google Scholar 

  15. Sinha, R., Saini, S., Anadhavelu, N.: Estimating the incremental effects of interactions for marketing attribution. In: 2014 International Conference on Behavior, Economic and Social Computing (BESC), pp. 1–6. IEEE, October 2014

    Google Scholar 

  16. Yadagiri, M.M., Saini, S.K., Sinha, R.: A non-parametric approach to the multi-channel attribution problem. In: Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y. (eds.) WISE 2015. LNCS, vol. 9418, pp. 338–352. Springer, Cham (2015). doi:10.1007/978-3-319-26190-4_23

    Chapter  Google Scholar 

  17. Opitz, D., Maclin, R.: Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 11, 169–198 (1999)

    MATH  Google Scholar 

  18. Koh, E., Gupta, N.: . An empirical evaluation of ensemble decision trees to improve personalization on advertisement. In: Proceedings of KDD 14 Second Workshop on User Engagement Optimization (2014)

    Google Scholar 

  19. Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., Provost, F.: Machine learning for targeted display advertising: Transfer learning in action. Machine Learning 95(1), 103–127 (2014)

    Article  MathSciNet  Google Scholar 

  20. Shao, X., Li, L.: Data-driven multi-touch attribution models. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 258–264. ACM (August 2011)

    Google Scholar 

  21. Farahat, A., Shanahan, J.: Econometric analysis and digital marketing: how to measure the effectiveness of an ad. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 785–785. ACM (February 2013)

    Google Scholar 

  22. Lu, N., Lin, H., Lu, J., Zhang, G.: A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics 10(2), 1659–1665 (2014)

    Article  Google Scholar 

  23. Wang, J., Zhang, Y., Chen, T.: Unified recommendation and search in e-commerce. In: Hou, Y., Nie, J.-Y., Sun, L., Wang, B., Zhang, P. (eds.) AIRS 2012. LNCS, vol. 7675, pp. 296–305. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35341-3_25

    Chapter  Google Scholar 

  24. Addicam, S., Balkan, S., Baydogan, M.: Adaptive Advertisement Recommender Systems for Digital Signage (2015)

    Google Scholar 

  25. Zhang, W., Enders, T., Li, D.: GreedyBoost: An accurate, efficient and flexible ensemble method for B2B recommendations. In: Proceedings of the 50th Hawaii International Conference on System Sciences, January 2017

    Google Scholar 

  26. Ho, T. K.:. Random decision forests (PDF). In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, August 14–16, pp. 278–282 (1995)

    Google Scholar 

  27. Ho, T.K.: The Random Subspace Method for Constructing Decision Forests (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998). doi:10.1109/34.709601

    Article  Google Scholar 

  28. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    Google Scholar 

  29. Chang, C.-C., Lin, C.-J.:. LIBSVM: a library for support vector machines (2001). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  30. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)

    MATH  Google Scholar 

  31. Friedman, J.: Greedy Function Approximation: A Gradient Boosting Machine. Reitz Lecture 29(5), 1189–1232 (1999)

    Google Scholar 

  32. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Statist. 28(2), 337–407 (2000) .doi:10.1214/aos/1016218223

  33. Chen, D., Plemmons, R.J.: Nonnegativity constraints in numerical analysis. In: Symposium on the Birth of Numerical Analysis (2009). CiteSeerX: 10.1.1.157.9203

    Google Scholar 

  34. https://cran.r-project.org/web/packages/nnls/nnls.pdf

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Authors and Affiliations

  1. VMware Inc., Bangalore, India

    Anit Bhandari, Kiran Rama, Nishant Niranjan, Parag Chitalia & Stig Berg

  2. Indian Institute of Management, Bangalore, India

    Nandini Seth

Authors
  1. Anit Bhandari
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  2. Kiran Rama
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  3. Nandini Seth
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  4. Nishant Niranjan
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  5. Parag Chitalia
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  6. Stig Berg
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Corresponding author

Correspondence to Anit Bhandari .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Sachsen, Germany

    Petra Perner

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Bhandari, A., Rama, K., Seth, N., Niranjan, N., Chitalia, P., Berg, S. (2017). Towards an Efficient Method of Modeling “Next Best Action” for Digital Buyer’s Journey in B2B. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2017. Lecture Notes in Computer Science(), vol 10357. Springer, Cham. https://doi.org/10.1007/978-3-319-62701-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-62701-4_4

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  • Online ISBN: 978-3-319-62701-4

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