Skip to main content

2025 | OriginalPaper | Buchkapitel

Machine Learning for Management of Data: The Role of Machine Learning in Marketing Mix Modelling and Decision-Making

verfasst von : Meghna Chaudhary, M. Afshar Alam, Sherin Zafar

Erschienen in: Innovative Computing and Communications

Verlag: Springer Nature Singapore

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Marketers may use the power of advanced investigation and arrangement of data incorporating algorithms for learning into the modelling of the marketing mix (MMM). This combination increases the predictive power and accuracy of MMM models, enabling marketers to get a deeper understanding of the complex relationships that exist between marketing efforts and financial outcomes. Furthermore, machine learning (ML) provides dynamic, real-time analysis of marketing strategies, facilitating prompt decision-making and marketing resource allocation optimisation. In essence, machine learning (ML) offers the prospect of a more effective quantitative method to marketing investment selections because of precise measurement and optimisation. In the field of Internet marketing, automated learning is a cutting-edge strategy since it captures, assesses, as well as uses opinions and comments about businesses to determine the feelings associated with the brand. Marketers may use this data to tailor their marketing interactions so that they speak specifically to each prospective customer and increase sales of their items. Machine learning techniques help to enhance customer visits by categorising the different click-through reactions to businesses that interact with them. Understanding digital clients better is made possible by deep learning, which divides the massive daily data cache into several sectors and uses pattern analysis to create insights from it. Based on judgmental sampling, the study chose 1250 digital users in India to examine how machine learning affects various machine learning capabilities that deal with consumer behaviour, decision-making and emotions in online marketing. These days, machine learning and artificial intelligence are the two main digital technologies that are changing people’s lives. Machine learning has transformed the way value is produced in digital marketing. Customers have a lot of alternatives when it comes to digital platforms these days, and system intelligence may assist advertisers in offering ideal object to customers in a competitive market. One popular approach is learning by machines which has an impact on daily tasks. The literature that is now accessible indicates that additional investigation into the use of machine learning to advertise electronically is necessary. The developments in machine learning have opened up new opportunities for digital marketing firms. Potential for clients in the services industry, especially in digital marketing, is presented by this study. Amazon and Facebook are two examples of how machine learning is being used, and both examples might enhance digital marketing. Starting with individual connections, we go to unified teams of participants connections, book publication and ultimately creating an area for collaboration among employers and employees.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Agarwal, A., Dudik, M., & Wu, Z. S. (2019). Fair regression: Quantitative definitions and reduction-based algorithms. In Proceedings of the International Conference on Machine Learning (pp. 120–129). Agarwal, A., Dudik, M., & Wu, Z. S. (2019). Fair regression: Quantitative definitions and reduction-based algorithms. In Proceedings of the International Conference on Machine Learning (pp. 120–129).
2.
Zurück zum Zitat Aghaei, S., Azizi, M. J., & Vayanos, P. (2019). Learning optimal and fair decision trees for non-discriminative decision-making. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 1418–1426.CrossRef Aghaei, S., Azizi, M. J., & Vayanos, P. (2019). Learning optimal and fair decision trees for non-discriminative decision-making. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 1418–1426.CrossRef
3.
Zurück zum Zitat Alipourfard, N., Fennell, P. G., & Lerman, K. (2018). Can you trust the trend? Discovering Simpson’s paradoxes in social data. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (pp. 19–27). ACM. Alipourfard, N., Fennell, P. G., & Lerman, K. (2018). Can you trust the trend? Discovering Simpson’s paradoxes in social data. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (pp. 19–27). ACM.
4.
Zurück zum Zitat Alipourfard, N., Fennell, P. G., & Lerman, K. (2018). Using Simpson’s paradox to discover interesting patterns in behavioral data. In Proceedings of the 12th International AAAI Conference on Web and Social Media. Alipourfard, N., Fennell, P. G., & Lerman, K. (2018). Using Simpson’s paradox to discover interesting patterns in behavioral data. In Proceedings of the 12th International AAAI Conference on Web and Social Media.
5.
Zurück zum Zitat Amini, A., Soleimany, A. P., Schwarting, W., Bhatia, S. N., & Rus, D. (2019). Uncovering and mitigating algorithmic bias through learned latent structure. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES’19). Association for Computing Machinery, New York, NY, USA (pp. 289–295). https://doi.org/10.1145/3306618.3314243 Amini, A., Soleimany, A. P., Schwarting, W., Bhatia, S. N., & Rus, D. (2019). Uncovering and mitigating algorithmic bias through learned latent structure. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES’19). Association for Computing Machinery, New York, NY, USA (pp. 289–295). https://​doi.​org/​10.​1145/​3306618.​3314243
8.
Zurück zum Zitat Barbosa, S., Cosley, D., Sharma, A., & Cesar Jr, R. M. (2016). Averaging gone wrong: Using time-aware analyses to better understand behavior. In Proceedings of the 25th International Conference on World Wide Web (pp. 829–841). Barbosa, S., Cosley, D., Sharma, A., & Cesar Jr, R. M. (2016). Averaging gone wrong: Using time-aware analyses to better understand behavior. In Proceedings of the 25th International Conference on World Wide Web (pp. 829–841).
9.
Zurück zum Zitat Hurley, S., Moutinho, L., & Stephens, N. M. (2022). Solving marketing optimization problems using genetic algorithms. European Journal of Marketing, 29(4), 39.CrossRef Hurley, S., Moutinho, L., & Stephens, N. M. (2022). Solving marketing optimization problems using genetic algorithms. European Journal of Marketing, 29(4), 39.CrossRef
10.
Zurück zum Zitat Duchessi, P. (2021). A research perspective: Artificial intelligence, management and organizations. Intelligent Systems in Accounting, Finance and Management, 2, 151–159.CrossRef Duchessi, P. (2021). A research perspective: Artificial intelligence, management and organizations. Intelligent Systems in Accounting, Finance and Management, 2, 151–159.CrossRef
11.
Zurück zum Zitat O’Kane, B. (2022). Using machine learning to drive stronger marketing outcomes. O’Kane, B. (2022). Using machine learning to drive stronger marketing outcomes.
12.
Zurück zum Zitat Abad-Grau, M. M., Tajtáková, M., & Arias-Aranda, D. (2022) Machine learning methods for the market segmentation of the performing arts audiences. International Journal of Business Environment, 2(3). Abad-Grau, M. M., Tajtáková, M., & Arias-Aranda, D. (2022) Machine learning methods for the market segmentation of the performing arts audiences. International Journal of Business Environment, 2(3).
13.
Zurück zum Zitat Kohli, A. K., & Haenlein, M. (2021). Factors affecting the study of important marketing issues: Additional thoughts and clarifications. International Journal of Research in Marketing, 38(1), 29–31.CrossRef Kohli, A. K., & Haenlein, M. (2021). Factors affecting the study of important marketing issues: Additional thoughts and clarifications. International Journal of Research in Marketing, 38(1), 29–31.CrossRef
14.
Zurück zum Zitat Reutterer, T., Platzer, M., & Schröder, N. (2021). Leveraging purchase regularity for predicting customer behavior the easy way. International Journal of Research in Marketing, 38(1), 194–215.CrossRef Reutterer, T., Platzer, M., & Schröder, N. (2021). Leveraging purchase regularity for predicting customer behavior the easy way. International Journal of Research in Marketing, 38(1), 194–215.CrossRef
15.
Zurück zum Zitat Bi, S., Perkins, A., & Sprott, D. (2021). The effect of start/end temporal landmarks on consumers’ visual attention and judgments. International Journal of Research in Marketing, 38(1), 136–154.CrossRef Bi, S., Perkins, A., & Sprott, D. (2021). The effect of start/end temporal landmarks on consumers’ visual attention and judgments. International Journal of Research in Marketing, 38(1), 136–154.CrossRef
16.
Zurück zum Zitat Cassillas, J., & Lopez, F. J. M. (2021). Marketing intelligent system using soft computing techniques: Managerial and research application. Springer. Cassillas, J., & Lopez, F. J. M. (2021). Marketing intelligent system using soft computing techniques: Managerial and research application. Springer.
17.
Zurück zum Zitat Lynn, M. (2021). Segmenting and targeting your market: Strategies and limitations. Cornell University. Lynn, M. (2021). Segmenting and targeting your market: Strategies and limitations. Cornell University.
18.
Zurück zum Zitat Vinerean, S., Cetina, I., Dumitrescu, L., & Tichindelean, M. (2019). The effects of social media marketing on online consumer behavior. International Journal of Business and Management, 8(14), 66. Vinerean, S., Cetina, I., Dumitrescu, L., & Tichindelean, M. (2019). The effects of social media marketing on online consumer behavior. International Journal of Business and Management, 8(14), 66.
Metadaten
Titel
Machine Learning for Management of Data: The Role of Machine Learning in Marketing Mix Modelling and Decision-Making
verfasst von
Meghna Chaudhary
M. Afshar Alam
Sherin Zafar
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-4152-6_9