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Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications

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  • Villarroel Ordenes, Francisco
  • Silipo, Rosaria

Abstract

Machine learning (ML) promises great value for marketing related applications. However, the proliferation of data types, methods, tools, and programing languages hampers knowledge integration amongst marketing analytics teams, making collaboration difficult. Visual-based programing might help by facilitating the orchestration of ML projects in a more intuitive visual fashion. This article introduces the KNIME Analytics Platform, a leading visual-based programing software, and its potential for implementing ML projects in Marketing. We contribute to the ML literature in marketing by creating a live repository of projects, hosted on the KNIME hub, where users can learn, share and reuse workflows (visual code). We developed a starting set of five annotated projects in the areas of customer churn, sentiment analysis, automated image analysis, search engine optimization, and customer experience. For two of these projects, we offer a step-by-step guide to facilitate learning by marketing practitioners, academics, and machine learning enthusiasts.

Suggested Citation

  • Villarroel Ordenes, Francisco & Silipo, Rosaria, 2021. "Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications," Journal of Business Research, Elsevier, vol. 137(C), pages 393-410.
    • Handle: RePEc:eee:jbrese:v:137:y:2021:i:c:p:393-410
    DOI: 10.1016/j.jbusres.2021.08.036
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    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    3. Joachim Büschken & Greg M. Allenby, 2020. "Improving Text Analysis Using Sentence Conjunctions and Punctuation," Marketing Science, INFORMS, vol. 39(4), pages 727-742, July.
    4. Shivaji Alaparthi & Manit Mishra, 2021. "BERT: a sentiment analysis odyssey," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 118-126, June.
    5. Ian Sutherland & Kiattipoom Kiatkawsin, 2020. "Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    6. Davide Proserpio & John R. Hauser & Xiao Liu & Tomomichi Amano & Alex Burnap & Tong Guo & Dokyun (DK) Lee & Randall Lewis & Kanishka Misra & Eric Schwarz & Artem Timoshenko & Lilei Xu & Hema Yoganaras, 2020. "Soul and machine (learning)," Marketing Letters, Springer, vol. 31(4), pages 393-404, December.
    7. Holmlund, Maria & Van Vaerenbergh, Yves & Ciuchita, Robert & Ravald, Annika & Sarantopoulos, Panagiotis & Ordenes, Francisco Villarroel & Zaki, Mohamed, 2020. "Customer experience management in the age of big data analytics: A strategic framework," Journal of Business Research, Elsevier, vol. 116(C), pages 356-365.
    8. Yolande Piris & Anne-Cécile Gay, 2021. "Customer satisfaction and natural language processing," Post-Print hal-03110702, HAL.
    9. Ascarza, & Neslin, & Netzer, & Lemmens, Aurélie & Anderson, Zachery & Fader, Peter S. & Gupta, S. & Hardie, B.G.S. & Libai, Barak & Neal, David & Provost, Foster, 2018. "In pursuit of enhanced customer retention management : Review, key issues, and future directions," Other publications TiSEM 28a90d28-6daf-42f1-bd8e-e, Tilburg University, School of Economics and Management.
    10. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    11. Nanne, Annemarie J. & Antheunis, Marjolijn L. & van der Lee, Chris G. & Postma, Eric O. & Wubben, Sander & van Noort, Guda, 2020. "The Use of Computer Vision to Analyze Brand-Related User Generated Image Content," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 156-167.
    12. Piris, Yolande & Gay, Anne-Cécile, 2021. "Customer satisfaction and natural language processing," Journal of Business Research, Elsevier, vol. 124(C), pages 264-271.
    13. Ciechanowski, Leon & Jemielniak, Dariusz & Gloor, Peter A., 2020. "TUTORIAL: AI research without coding: The art of fighting without fighting: Data science for qualitative researchers," Journal of Business Research, Elsevier, vol. 117(C), pages 322-330.
    14. Li, Xi & Shi, Mengze & Wang, Xin (Shane), 2019. "Video mining: Measuring visual information using automatic methods," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 216-231.
    15. Liu Liu & Daria Dzyabura & Natalie Mizik, 2020. "Visual Listening In: Extracting Brand Image Portrayed on Social Media," Marketing Science, INFORMS, vol. 39(4), pages 669-686, July.
    16. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
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