Skip to main content
Springer Nature Link
Log in

Artificial Intelligence in Marketing Strategic Decisions via Product Portfolio Optimization

  • Conference paper
  • First Online:
Decision Sciences (DSA ISC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14778))

Included in the following conference series:

Abstract

This article presents theoretical and numerical insights from employing artificial intelligence algorithms to strategic marketing decisions, focusing specifically on the optimization of product portfolio allocation. Given its critical importance in revenue generation, customer satisfaction and market positioning, it is crucial that marketing directors make appropriate and data-driven decisions on the implications of different strategies regarding the allocation of inventory and advertising spending, among others. Through computational analysis, utilizing the Non-dominated Sorting Genetic Algorithm II, we explore how varying allocation strategies impact key performance metrics. Beyond merely providing optimal portfolio allocation, the algorithm allows for quantifying trade-offs among objectives, shedding light on their implications for competitive dynamics, revenue generation, and inventory costs. By solving multi-objective optimization problems inherent in product portfolio management, this article offers comprehensive insights into the role of AI-driven approaches in enhancing strategic decisions that shape competitiveness and customer value in today’s marketing landscape.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arora, S., Sahney, S., Parida, R.R.: Drivers of showrooming behaviour: insights from integrated perspectives. Int. J. Retail Distrib. Manag. 50(3), 398–413 (2022)

    Article  MATH  Google Scholar 

  2. Bella, K.M.J.: A study on marketing analytics and artificial intelligence XV Issue 55, 53–57 (2024)

    Google Scholar 

  3. Borges, A.F., Laurindo, F.J., Spínola, M.M., Gonçalves, R.F., Mattos, C.A.: The strategic use of artificial intelligence in the digital era: systematic literature review and future research directions. Int. J. Inf. Manag. 57, 102225 (2021)

    Article  Google Scholar 

  4. Bullah, S., van Zyl, T.L.: A learnheuristic approach to a constrained multi-objective portfolio optimisation problem. arXiv preprint arXiv:2304.06675 (2023)

  5. Cannavacciuolo, L., Iandoli, L., Ponsiglione, C., Zollo, G.: Knowledge elicitation and mapping in the design of a decision support system for the evaluation of suppliers’ competencies. Vine - J. Inf. Knowl. Manag. Syst. 45(4), 530–550 (2015)

    Google Scholar 

  6. Davenport, T., Guha, A., Grewal, D., Bressgott, T.: How artificial intelligence will change the future of marketing. J. Acad. Mark. Sci. 48, 24–42 (2020)

    Article  MATH  Google Scholar 

  7. Demirkan, H., Delen, D.: Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decis. Support Syst. 55(1), 412–421 (2013)

    Article  MATH  Google Scholar 

  8. He, Q., He, Z., Duan, S., Zhong, Y.: Multi-objective interval portfolio optimization modeling and solving for margin trading. Swarm Evol. Comput. 75, 101141 (2022)

    Article  MATH  Google Scholar 

  9. Henderson, H.: Creating alternative futures. Media Resources Center, Iowa State University (1978)

    Google Scholar 

  10. Juan, A.A., Keenan, P., Martí, R., McGarraghy, S., Panadero, J., Carroll, P., Oliva, D.: A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics. Ann. Oper. Res. 320(2), 831–861 (2023)

    Article  MathSciNet  MATH  Google Scholar 

  11. Juan, A.A., Kelton, W.D., Currie, C.S., Faulin, J.: Simheuristics applications: dealing with uncertainty in logistics, transportation, and other supply chain areas. In: 2018 Winter Simulation Conference (WSC), pp. 3048–3059. IEEE (2018)

    Google Scholar 

  12. Kalayci, C.B., Polat, O., Akbay, M.A.: An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm Evol. Comput. 54, 100662 (2020)

    Article  MATH  Google Scholar 

  13. Khotinskaya, G., Chernikova, L., Likhacheva, O., Setchenkova, L., Slepneva, T.: Digital transformation as a driver for business ecosystem development. In: E3S Web of Conferences, vol. 403, p. 08017. EDP Sciences (2023)

    Google Scholar 

  14. Kolm, P.N., Tütüncü, R., Fabozzi, F.J.: 60 years of portfolio optimization: practical challenges and current trends. Eur. J. Oper. Res. 234(2), 356–371 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. Kumar, D.T.S.: Data mining based marketing decision support system using hybrid machine learning algorithm. J. Artif. Intell. Capsule Netw. 2(3), 185–193 (2020)

    Article  MATH  Google Scholar 

  16. Li, S.: The development of a hybrid intelligent system for developing marketing strategy. Decis. Support Syst. 27(4), 395–409 (2000)

    Article  MATH  Google Scholar 

  17. Loke, Z.X., Goh, S.L., Kendall, G., Abdullah, S., Sabar, N.R.: Portfolio optimisation problem: a taxonomic review of solution methodologies. IEEE Access (2023)

    Google Scholar 

  18. Ma, J., Kim, H.M.: Continuous preference trend mining for optimal product design with multiple profit cycles. J. Mech. Des. 136(6), 061002 (2014)

    Article  Google Scholar 

  19. Markowitz, H.M., Markowitz, H.M.: Portfolio Selection: Efficient Diversification of Investments. J. Wiley, Hoboken (1967)

    Google Scholar 

  20. Mehlstäubl, J., Braun, F., Denk, M., Kraul, R., Paetzold, K.: Using machine learning for product portfolio management: a methodical approach to predict values of product attributes for multi-variant product portfolios. Proc. Des. Soc. 2, 1659–1668 (2022)

    Article  Google Scholar 

  21. Mehlstäubl, J., Braun, F., Paetzold, K.: Data mining in product portfolio and variety management–literature review on use cases and research potentials. In: 2021 IEEE Technology & Engineering Management Conference-Europe (TEMSCON-EUR), pp. 1–6. IEEE (2021)

    Google Scholar 

  22. Mustak, M., Salminen, J., Plé, L., Wirtz, J.: Artificial intelligence in marketing: topic modeling, scientometric analysis, and research agenda. J. Bus. Res. 124, 389–404 (2021)

    Article  Google Scholar 

  23. Pai, G.V., Michel, T.: Metaheuristic multi-objective optimization of constrained futures portfolios for effective risk management. Swarm Evol. Comput. 19, 1–14 (2014)

    Article  MATH  Google Scholar 

  24. Panadero, J., Juan, A.A., Freixes, A., Grifoll, M., Serrat, C., Dehghanimohamamdabadi, M.: An agile simheuristic for the stochastic team task assignment and orienteering problem: applications to unmanned aerial vehicles. In: 2020 Winter Simulation Conference (WSC), pp. 1324–1335. IEEE (2020)

    Google Scholar 

  25. Pouya, A.R., Solimanpur, M., Rezaee, M.J.: Solving multi-objective portfolio optimization problem using invasive weed optimization. Swarm Evol. Comput. 28, 42–57 (2016)

    Article  MATH  Google Scholar 

  26. Qu, B., Zhou, Q., Xiao, J., Liang, J., Suganthan, P.N., et al.: Large-scale portfolio optimization using multiobjective evolutionary algorithms and preselection methods. Math. Probl. Eng. 2017 (2017)

    Google Scholar 

  27. Rana, J., Gaur, L., Singh, G., Awan, U., Rasheed, M.I.: Reinforcing customer journey through artificial intelligence: a review and research agenda. Int. J. Emerg. Mark. 17(7), 1738–1758 (2022)

    Article  MATH  Google Scholar 

  28. Rodriguez-Garcia, P., Li, Y., Lopez-Lopez, D., Juan, A.A.: Strategic decision making in smart home ecosystems: a review on the use of artificial intelligence and internet of things. Internet Things 100772 (2023)

    Google Scholar 

  29. Schmidt, C.P., Gould, F.J., Schmidt, C.: Study Guide [to] Introductory Management Science, Gary. D. Eppen, Floyd J. Gould, Charles P. Schmidt. Prentice Hall, Hoboken (1991)

    Google Scholar 

  30. Schrage, M., Kiron, D.: Improving strategic execution with machine learning. MIT Sloan Management Review (2018). pp. 0_1–7

    Google Scholar 

  31. Schrage, M., Kiron, D.: Understanding the real keys to effective kpi’s. MIT initiative on the digital economy research biref, pp. 1–3 (2018)

    Google Scholar 

  32. Shahid, M.Z., Li, G.: Impacto of artificial intelligence in marketing: a perspective of marketing professionals in Pakistan. Glob. J. Manag. Bus. Res. 19(2), 27–33 (2019)

    MATH  Google Scholar 

  33. Tang, J., Liu, G., Pan, Q.: A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends. IEEE/CAA J. Autom. Sin. 8(10), 1627–1643 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  34. Tucker, C.S., Kim, H.M.: Data-driven decision tree classification for product portfolio design optimization, p. 041004 (2009)

    Google Scholar 

  35. Ünal, H.T., Başçiftçi, F.: Using evolutionary algorithms for the scheduling of aircrew on airborne early warning and control system. Defence Sci. J. 70(3) (2020)

    Google Scholar 

  36. Verma, S., Sharma, R., Deb, S., Maitra, D.: Artificial intelligence in marketing: systematic review and future research direction. Int. J. Inf. Manag. Data Insights 1(1), 100002 (2021)

    Google Scholar 

  37. Vlačić, B., Corbo, L., e Silva, S.C., Dabić, M.: The evolving role of artificial intelligence in marketing: a review and research agenda. J. Bus. Res. 128, 187–203 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Universitat Oberta de Catalunya, 08018, Barcelona, Spain

    Patricia Rodriguez-Garcia

  2. Department of Marketing, ESADE Business School, 08172, Sant Cugat, Spain

    David Lopez-Lopez

  3. Department of Computer Science, Universitat Oberta de Catalunya, 08018, Barcelona, Spain

    Jon Ander Martin Solano

Authors
  1. Patricia Rodriguez-Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Lopez-Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jon Ander Martin Solano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patricia Rodriguez-Garcia .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Valencia, Valencia, Spain

    Angel A. Juan

  2. Public University of Navarre, Pamplona, Spain

    Javier Faulin

  3. ESADE Business School, Sant Cugat, Spain

    David Lopez-Lopez

Ethics declarations

Disclosure of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rodriguez-Garcia, P., Lopez-Lopez, D., Martin Solano, J.A. (2025). Artificial Intelligence in Marketing Strategic Decisions via Product Portfolio Optimization. In: Juan, A.A., Faulin, J., Lopez-Lopez, D. (eds) Decision Sciences. DSA ISC 2024. Lecture Notes in Computer Science, vol 14778. Springer, Cham. https://doi.org/10.1007/978-3-031-78238-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-78238-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78237-4

  • Online ISBN: 978-3-031-78238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics