International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Parameterization of the Company's Business Model for Machine Learning-Based Marketing Stress Testing

  • Received : 2022.02.05
  • Published : 2022.02.28
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Abstract

Marketing stress testing is a new method of identifying the company's strengths and weaknesses in a turbulent environment. Technically, this is a complex procedure, so it involves artificial intelligence and machine learning. The main problem is currently the development of methodological approaches to the development of the company's digital model, which will provide a framework for machine learning. The aim of the study was to identify and develop an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. This aim provided the company's activities to be considered as a set of elements (business processes, products) and factors that affect them (marketing environment). The article proposes an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. The proposed approach includes four main elements that are subject to parameterization: elements of the company's internal environment, factors of the marketing environment, the company' core competency and factors impacting the company. Matrices for evaluating the results of the work of expert groups to determine the degree of influence of the marketing environment factors were developed. It is proposed to distinguish between mega-level, macro-level, meso-level and micro-level factors depending on the degree of impact on the company. The methodological limitation of the study is that it involves the modelling method as the only one possible at this stage of the study. The implementation limitation is that the proposed approach can only be used if the company plans to use machine learning for marketing stress testing.

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References

  1. Zozul'ov O.V, Menkova K.I. Understanding of marketing stress-testing of business model of enterprises, as a factor of a company's steady development. SWorldJournal, 43, 60-67 (2019). https://doi.org/10.30888/2410-6615.2020-06-03-005
  2. Subrahmanyam OV., Nishit M. Anomaly detection in market risk analysis and stress testing. 2020. https://www.cfainstitute.org/-/media/regional/arx/post-pdf/2020/06/04/anomaly-detection-in-market-risk-analysis-and-stress-testing.ashx (Accessed December 5, 2021)
  3. Jacobs M. The validation of machine-learning models for the stress testing of credit risk. 2018. https://www.researchgate.net/publication/330191526_The_validation_of_machinelearning_models_for_the_stress_testing_of_credit_risk (Accessed December 10, 2021)
  4. Petropoulos A., Siakoulis V., Vlachogiannakis N., Stavroulakis E. Deep-Stress: A deep learning approach for dynamic balance sheet stress testing. 2019. https://www.eba.europa.eu/sites/default/documents/files/document_library/Calendar/ConferenceWorkshop/2019/8th%20annual%20workshop%20documents/2%20DeepStressA%20deep%20learning%20approach%20for%20dynamic%20balance%20sheet%20stress%20testing.pdf (Accessed December 12, 2021)
  5. Aziz S., Dowling M. Machine Learning and AI for Risk Management. 2018. https://link.springer.com/chapter/10.1007/978-3-030-02330-0_3 (Accessed December 15, 2021)
  6. PWC. Model Risk Management of AI and Machine Learning Systems. 2020. https://www.pwc.co.uk/data-analytics/documents/model-risk-management-of-ai-machine-learning-systems.pdf (Accessed December 7, 2021)
  7. Haaker T., Bouwman H., Janssen W., de Reuver M. Business model stress testing: A practical approach to test the robustness of a business model. 2017. https://www.sciencedirect.com/science/article/pii/S001632871630341X (Accessed December 9, 2021)
  8. Licari J.M. Market Risk Stress Testing Models. 2015. https://www.moodysanalytics.com/-/media/presentation/2015/2015-02-12-market-risk-stress-testing-models.pdf (Accessed December 6, 2021)
  9. Aragones J.R., Blanco C., Dowd K. Incorporating Stress Tests into Market Risk Modeling. 2001. http://efinance.org.cn/cn/FEshuo/FEIncorporating%20Stress%20Tests%20into%20Market%20Risk%20Modeling.pdf (Accessed December 10, 2021)
  10. Alexander C., Sheedy E. Developing a Stress Testing Framework Based on Market Risk Models. 2008. https://www.researchgate.net/publication/222425217_Developing_a_Stress_Testing_Framework_Based_on_Market_Risk_Models (Accessed December 12, 2021)
  11. Engle R.F. Stress Testing with Market Data. 2020. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3666145 (Accessed December 12, 2021)
  12. Menkova K.I., Zozul'ov O.V. Company's business model stress testing as a base of economical sustainability in the next normality. 2018. https://www.sworldjournal.com/index.php/swj/article/view/swj06-03-005/670 (Accessed December 16, 2021)
  13. CEBS. Technical aspects of stress testing under the supervisory review process - CP 12. 2006. https://www.eba.europa.eu/sites/default/documents/files/documents/10180/16094/0fbfc032-152c-4936-b8c7-8bd965e7c98c/GL03stresstesting.pdf?retry=1 (Accessed December 16, 2021)
  14. Booth D. Marketing analytics in the age of machine learning. 2019. https://hstalks.com/article/5014/marketing-analytics-in-the-age-of-machine-learning/ (Accessed December 12, 2021)
  15. Brei A.V. Machine Learning in Marketing: Overview, Learning Strategies, Applications, and Future Developments. 2020. https://www.researchgate.net/publication/344000369_Machine_Learning_in_Marketing_Overview_Learning_Strategies_Applications_and_Future_Developments (Accessed December 12, 2021)
  16. Sundsoy P., Bjelland J., Iqbal A.M., Pentland A., de Montjoye Y.A. Big Data-Driven Marketing: How Machine Learning Outperforms Marketers' Gut-Feeling. 2014. https://www.semanticscholar.org/paper/Big-Data-Driven-Marketing%3A-How-Machine-Learning-Sundsoy-Bjelland/62e5bd319c55f025296dbcbd7f2cacbe0ee12798 (Accessed December 8, 2021)
  17. Mari A. The Rise Of Machine Learning In Marketing: Goal, Process, And Benefit Of Ai-Driven Marketing. 2019. https://groupmp15170118135410.blob.core.windows.net/cmscontent/2019/05/The-Rise-of-Machine-Learning-in-Marketing_Research-Report_Alex-Mari_2019.pdf (Accessed December 8, 2021)
  18. Cannella J. Artificial Intelligence In Marketing. 2018. https://assets.websitefiles.com/5d3a327acd3c3c5e5999b0b6/5f10bf5288c2791376593af5_Cannella_J_Spring_2018.pdf (Accessed December 9, 2021)
  19. Vertical Leap. A marketer's guide to machine learning and AI. 2018. https://www.vertical-leap.uk/downloads/the-marketing-managers-guide-to-machine-learning.pdf (Accessed December 6, 2021)
  20. Ullal M.S., Hawaldar I.T., Soni R., Nadeem M. The Role of Machine Learning in Digital Marketing. 2021. https://journals.sagepub.com/doi/pdf/10.1177/21582440211050394 (Accessed December 12, 2021)
  21. Zozul'ov O.V, Menkova K.I. Concept of marketing stress-testing business model as a factor of steady development of enterprise. Topical issues of economics, accounting, finance and law in modern conditions. 2019. https://ela.kpi.ua/bitstream/123456789/36963/1/Zozulov_Menkova_Marketing-XXI_2020.pdf (Accessed December 10, 2021)
  22. Menkova K.I , Zozul'ov O.V. Approaches to forming enterprises' business-models. Marketing and Digital Technologies, 3(4), 32-39 (2019). https://doi.org/10.15276/mdt.3.4.2019.3