CoChat
Sign Up
Cited by 194Artificial Intelligence ApplicationsAI in Service InteractionsImpact of AI and Big Data on Business and Society

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Letting the Computers Take Over: Using AI to Solve Marketing Problems

Open access
Overgoor, Gijs, Chica, Manuel, Rand, William, Weishampel, Anthony·2019·California Management Review
DOI 10.1177/0008125619859318
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to improve marketing, they lack a process by which to execute a Marketing AI project. This article discusses the use of AI to provide support for marketing decisions. Based on the established Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, it creates a process for managers to use when executing a Marketing AI project and discusses issues that might arise. It explores how this framework was used to develop three cutting-edge Marketing AI applications.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/0008125619859318

Cites

0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 53

  1. Adnan Darwiche, "Human-Level Intelligence or Human-Level Intelligence or Animal-Like Abilities?" Communications of the ACM, 61/10 (October 2018): 56-67.
  2. Seshadri Tirunillai and Gerard J. Tellis, "Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation," Journal of Marketing Research, 51/4 (August 2014): 463-479.
  3. Aurélie Lemmens and Christophe Croux, "Bagging and Boosting Classification Trees to Predict Churn," Journal of Marketing Research, 43/2 (May 2006): 276-286.
  4. Colin Shearer, "The CRISP-DM Model: The New Blueprint for Data Mining," Journal of Data Warehousing, 5/4 (Fall 2000): 13-22.
  5. Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Boston, MA: Pearson, 2018). 10. Ibid.
  6. Ajay Agrawal, Joshua Gans, and Avi Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence (Boston, MA: Harvard Business Review Press, 2018).
  7. Tom M. Mitchell, Machine Learning (New York, NY: McGraw-Hill, 1997).
  8. Kevin P. Murphy, Machine Learning: A Probabilistic Perspective (Cambridge, MA: MIT Press, 2012).
  9. Marno Verbeek, A Guide to Modern Econometrics (Chichester: John Wiley, 2008).
  10. Hal R. Varian, "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, 28/2 (Spring 2014): 3-28.
  11. Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Inbal Yahav, and Kenneth C. Lichtendahl Jr., Data Mining for Business Analytics: Concepts, Techniques, and Applications in R (Hoboken, NJ: John Wiley, 2017).
  12. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning (Cambridge, MA: MIT Press, 2016).
  13. Nikolay Burlutskiy, Miltos Petridis, Andrew Fish, Alexey Chernov, and Nour Ali, "An Investigation on Online versus Batch Learning in Predicting User Behaviour," in Research and Development in Intelligent Systems XXXIII. Conference Paper at International Conference on Innovative Techniques and Applications of Artificial Intelligence, ed. M. Bramer and M. Petridis (New York, NY: Springer, 2016), pp. 135-149.
  14. Nello Cristianini and John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge, UK: Cambridge University Press, 2000).
  15. William Rand and Roland T. Rust, "Agent-Based Modeling in Marketing: Guidelines for Rigor," International Journal of Research in Marketing, 28/3 (September 2011): 181-193;
  16. Uri Wilensky and William Rand, Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo (Cambridge, MA: MIT Press, 2015).
  17. Ajay K. Agrawal, Joshua S. Gans, and Avi Goldfarb, "Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence," NBER Working Paper No. 24243, National Bureau of Economic Research, Cambridge, MA, 2018.
  18. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay, "Scikit-Learn: Machine Learning in Python," Journal of Machine Learning Research, 12 (2011): 2825-2830.
  19. Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, and Zachary M. Jones, "mlr: Machine Learning in R," Journal of Machine Learning Research, 17/170 (2016): 1-5.
  20. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten, "The WEKA Data Mining Software: An Update," ACM SIGKDD Explorations Newsletter, 11/1 (2009): 10-18;
  21. Janez Demšar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar, Mitar Milutinovič, Martin Možina, Matija Polajnar, Marko Toplak, Anže Starič, Miha Štajdohar, Lan Umek, Lan Žagar, Jure Žbontar, Marinka Žitnik, and Blaž Zupan, "Orange: Data Mining Toolbox in Python," Journal of Machine Learning Research, 14 (August 2013): 2349-2353.
  22. J. Alcalá-Fdez, L. Sánchez, S. García, M. Del Jesus, S. Ventura, J. Garrell, J. Otero, C. Romero, J. Bacardit, V. Rivas, J. C. Fernández, and F. Herrera, "KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems," Soft Computing, 13/3 (2009): 307-318.
  23. Umair Shafique and Haseeb Qaiser, "A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA)," International Journal of Innovation and Scientific Research, 12/1 (November 2014): 217-222;
  24. Godfrey Onwubolu, "An Inductive Data Mining System Framework," in Proceedings of the International Workshop on Inductive Modeling (IWIM '09) (Krynica, Poland: Citeseer, 2009), pp. 108-113.
  25. J. A. Harding, M. Shahbaz, Srinivas, and A. Kusiak, "Data Mining in Manufacturing: A Review," Journal of Manufacturing Science and Engineering, 128/4 (2006): 969-976.
  26. B. Carneiro da Rocha and R. Timóteo de Sousa Júnior, "Identifying Bank Frauds Using CRISP-DM and Decision Trees," International Journal of Computer Science and Information Technology, 2/5 (October 2010): 162-169.
  27. Sérgio Moro, Raul Laureano, and Paulo Cortez, "Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology," in Proceedings of European Simulation and Modeling Conference-ESM 2011, EUROSISETI, 2011, pp. 117-121, https://www. semanticscholar.org/paper/Using-data-mining-for-bank-direct-marketing%3A-an-of-Moro- Laureano/1999417377ec21ecf7f7f55af62975065f785fb2;
  28. Wendy Gersten, Rüdiger Wirth, and Dirk Arndt, "Predictive Modeling in Automotive Direct Marketing: Tools, Experiences and Open Issues," in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY: ACM, 2000), pp. 398-406, https://dl.acm.org/cita- tion.cfm?id=347174.
  29. Michael S. Garver, "Using Data Mining for Customer Satisfaction Research," Marketing Research, 14/1 (2002): 8.
  30. Alon Halevy, Peter Norvig, and Fernando Pereira, "The Unreasonable Effectiveness of Data," IEEE Intelligent Systems, 24/2 (2009): 8-12.
  31. Ronen Feldman and James Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data (Cambridge, UK: Cambridge University Press, 2007).
  32. Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, Data Mining: Practical Machine Learning Tools and Techniques (Cambridge, MA: Morgan Kaufmann, 2016).
  33. Jerome Friedman, Trevor Hastie, and Robert Tibshirani, The Elements of Statistical Learning (New York, NY: Springer-Verlag, 2001).
  34. Claudia Beleites, Ute Neugebauer, Thomas Bocklitz, Christoph Krafft, and Jürgen Popp, "Sample Size Planning for Classification Models," Analytica Chimica Acta, 760 (2013): 25-33.
  35. Michael Trusov, Randolph E. Bucklin, and Koen Pauwels, "Effects of Word-of-Mouth ver- sus Traditional Marketing: Findings from an Internet Social Networking Site," Journal of Marketing, 73/5 (September 2009): 90-102.
  36. Barak Libai, Eitan Muller, and Renana Peres, "Decomposing the Value of Word-of-Mouth Seeding Programs: Acceleration versus Expansion," Journal of Marketing Research, 50/2 (April 2013): 161-176.
  37. Iman Golkar Amnieh and Marjan Kaedi, "Using Estimated Personality of Social Network Members for Finding Influential Nodes in Viral Marketing," Cybernetics and Systems, 46/5 (2015): 355-378.
  38. Philipp Schmitt, Bernd Skiera, and Christophe Van den Bulte, "Referral Programs and Customer Value," Journal of Marketing, 75/1 (2011): 46-59.
  39. Gary L. Lilien, "Bridging the Academic-Practitioner Divide in Marketing Decision Models," Journal of Marketing, 75/4 (2011): 196-210.
  40. Manuel Chica and William Rand, "Building Agent-Based Decision Support Systems for Word-of-Mouth Programs. A Freemium Application," Journal of Marketing Research, 54/5 (October 2017): 752-767. 56. Ibid.
  41. Charles M. Macal and Michael J. North, "Tutorial on Agent-Based Modeling and Simulation," Proceedings of the 37th Conference on Winter Simulation (New York, NY: ACM, 2005), pp. 2-15, https://dl.acm.org/citation.cfm?id=1162712.
  42. Mark Newman, Albert-László Barabási, and Duncan J. Watts, The Structure and Dynamics of Networks (Princeton, NJ: Princeton University Press, 2006).
  43. Fabien Viger and Matthieu Latapy, "Efficient and Simple Generation of Random Simple Connected Graphs with Prescribed Degree Sequence," Lecture Notes in Computer Science, Computing and Combinatorics, 3595 (2005): 440-449.
  44. Damon Centola and Michael Macy, "Complex Contagions and the Weakness of Long Ties," American Journal of Sociology, 113/3 (November 2007): 702-734.
  45. Manuel Chica, José Barranquero, Tomasz Kajdanowicz, Sergio Damas, and Óscar Cordón, "Multimodal Optimization: An Effective Framework for Model Calibration," Information Sciences, 375 (2017): 79-97. 64. Ibid.
  46. Ruth Rosenholtz, Yuanzhen Li, and Lisa Nakano, "Measuring Visual Clutter," Journal of Vision, 7/2 (August 2007): 1-17.
  47. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, "Deep Residual Learning for Image Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (New York, NY: IEEE, 2016), pp. 770-778, https://ieeexplore.ieee.org/document/7780459.
  48. Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba, "Places: A 10 Million Image Database for Scene Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, 40/6 (June 2018): 1452-1464.
  49. Andrew Rohm, Velitchka Kaltcheva, and George Milne, "A Mixed-Method Approach to Examining Brand-Consumer Interactions Driven by Social Media," Journal of Research in Interactive Marketing, 7/4 (2013): 295-311;
  50. Neil Woodcock, Nick Broomfield, Geoff Downer, and Michael Starkey, "The Evolving Data Architecture of Social Customer Relationship Management," Journal of Direct, Data and Digital Marketing Practice, 12/3 (January 2011): 249- 266;
  51. Liye Ma, Baohong Sun, and Sunder Kekre, "The Squeaky Wheel Gets The Grease-An Empirical Analysis of Customer Voice and Firm Intervention on Twitter," Marketing Science, 34/5 (2015): 627-645.
  52. Judith Chevalier and Dina Mayzlin, "The Effect of Word of Mouth on Sales: Online Book Reviews," Journal of Marking Research, 43/3 (August 2006): 345-354;
  53. Sabine Einwiller and Sarah Steilen, "Handling Complaints on Social Network Sites-An Analysis of Complaints and Complaint Responses on Facebook and Twitter Pages of Large US Companies," Public Relations Review, 41/2 (June 2015): 195-204.

Cited by

Sorted by citations

Items 101–193 of 193

Related

See all

Metadata

Authors
Overgoor, G., Chica, M., Rand, W., & Weishampel, A.
Affiliations
  • North Carolina State University (US)
  • Universidad de Granada (ES)
ORCID
Venue
California Management Review
Publisher
SAGE Publications
Published
2019-07-09
Volume
61
Issue
4
Pages
156-185
ISSN
  • 0008-1256 (ISSN-L)
  • 2162-8564
DOI
10.1177/0008125619859318
OpenAlex
W2957879630
Cited by
194
References
0
Citations by year
2026: 18 · 2025: 29 · 2024: 51 · 2023: 24 · 2022: 32 · 2021: 25 · 2020: 13 · 2019: 2
FWCI
8.31
Type
journal article
Language
en
Open access
Licence
Locations
  • California Management Review (publishedVersion)
  • Institutional Repository of the University of Granada (University of Granada) (submittedVersion, other-oa)
  • UvA-DARE (University of Amsterdam) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • NOVA (University of Newcastle Australia) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Universiteit van Amsterdam (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
artificial intelligence in marketing, marketing decision support, CRISP-DM, data mining, marketing analytics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review