Impact and Role of Artificial Intelligence in Sales and Marketing

Arun Kumar Neeli*
MBA Marketing, Palamuru University, Telangana, India.
Periodicity:June - August'2020
DOI : https://doi.org/10.26634/jmgt.15.1.17067

Abstract

In the recent years there have been rapid advances in the fields of information technology, processing power, data handling methods, robotics, and artificial intelligence. These advances impact businesses all over the world and play as a key role in their growth and development. Artificial Intelligence as a concept is changing the way companies work. Due to the enormous potential and applicability it is being implemented in many fields like information technology, retail industry, space science, automobile Industry, entertainment, medical, transportation, medical, social sciences, business management, etc. In this paper we will explore What is AI and how the advances of AI are impacting the growth and evolution of sales and marketing field. Additionally this paper discusses how different techniques and methods of AI are changing the activities and functions of sales and marketing. Finally, we will conclude with analysis on the present role of AI in lives of sales and marketing professionals.

Keywords

Artificial Intelligence, Sales and Marketing, Applications, Impact, Machine Learning, Trends.

How to Cite this Article?

Kumar, N. A. (2020). Impact and Role of Artificial Intelligence in Sales and Marketing. i-manager's Journal on Management, 15(1), 1-6. https://doi.org/10.26634/jmgt.15.1.17067

References

[1]. AI Index. (n.d.). Lexico. Retrieved from https://english. oxforddictionaries.com/artificial%20intelligence
[2]. Autor, D. H., Dorn, D., & Hanson, G. H. (2015). Untangling trade and technology: Evidence from local labour markets. The Economic Journal, 125 (584), 621-646.
[3]. Balakrishnan, P. S., Cooper, M. C., Jacob, V. S., & Lewis, P. A. (1996). Comparative performance of the FSCL neural net and K-means algorithm for market segmentation. European Journal of Operational Research, 93(2), 346- 357. https://doi.org/10.1016/0377-2217(96)00046-X
[4]. Casabayó, M., Agell, N., & Sánchez-Hernández, G. (2015). Improved market segmentation by fuzzifying crisp clusters: A case study of the energy market in Spain. Expert Systems with Applications, 42 (3), 1637-1643. https://doi. org/10.1016/j.eswa.2014.09.044
[5]. Cespedes, F. V. (1994). Industrial marketing: Managing new requirements. Mit Sloan Management Review, 35(3), 45.
[6]. Chen, H., & Zimbra, D. (2010). AI and opinion mining. IEEE Intelligent Systems, 25(3), 74-80.
[7]. Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameterselection techniques. Expert Systems with Applications, 34(1), 313-327.
[8]. Courville, A., Goodfellow, I., Bengio, Y., & Bengio, Y. (2016). Deep Learning (Vol. 1). Cambridge: MIT Press.
[9]. Eggel, T., & Reinhold, M. (2014). Second generation recommendation engines in a SME B2B context: A case study. In 13th Internation Science-to-Bussiness Marketting Conference, Winterthur.
[10]. Florez-Lopez, R., & Ramon-Jeronimo, J. M. (2009). Marketing segmentation through machine learning models: An approach based on customer relationship management and customer profitability accounting. Social Science Computer Review, 27(1), 96-117. https://doi.org/10.1177%2F0894439308321592
[11]. Ghose, T. K., & Tran, T. T. (2010, May). A dynamic pricing approach in e-commerce based on multiple purchase attributes. In Canadian Conference on Artificial Intelligence (pp. 111-122). Berlin, Heidelberg: Springer.
[12]. Huang, B., Kechadi, M. T., & Buckley, B. (2012). Customer churn prediction in telecommunications. Expert Systems with Applications, 39(1), 1414-1425. https://doi. org/10.1016/j.eswa.2011.08.024
[13]. Huang, J. J., Tzeng, G. H., & Ong, C. S. (2007). Marketing segmentation using support vector clustering. Expert Systems with Applications, 32(2), 313-317. https:// doi.org/10.1016/j.eswa.2005.11.028
[14]. Hülsmann, M., Borscheid, D., Friedrich, C. M., & Reith, D. (2012). General sales forecast models for automobile markets and their analysis. Transactions on Machine Learning and Data Mining, 5(2), 65-86
[15]. Järvinen, J., & Taiminen, H. (2016). Harnessing marketing automation for B2B content marketing. Industrial Marketing Management, 54, 164-175.
[16]. Martínez, A., Schmuck, C., Pereverzyev Jr, S., Pirker, C., & Haltmeier, M. (2020). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research, 281(3), 588-596.
[17]. Morin, C. (2011). Neuromarketing: The new science of consumer behavior. Society, 48(2), 131-135. https://doi. org/10.1007/s12115-010-9408-1
[18]. Ono, C., Kurokawa, M., Motomura, Y., & Asoh, H. (2007, July). A context-aware movie preference model using a Bayesian network for recommendation and promotion. In International Conference on User Modeling (pp. 247-257). Springer, Berlin, Heidelberg.
[19]. Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons. https://doi.org/10.1016/j.bushor.2020. 01.003
[20]. Perakakis, E., Mastorakis, G., & Kopanakis, I. (2019). Social media monitoring: An Innovative Intelligent Approach. Designs, 3(2), 24-36. https://doi.org/10.3390/ designs3020024
[21]. Rafiei, M. H., & Adeli, H. (2016). A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2).
[22]. Raju, C. V. L., Narahari, Y., & Ravikumar, K. (2003, June). Reinforcement learning applications in dynamic pricing of retail markets. In EEE International Conference on E-Commerce, 2003 (CEC 2003) (pp. 339-346). IEEE. https:// doi.org/10.1109/COEC.2003.1210269
[23]. Ren, S., Chan, H. L., & Siqin, T. (2019). Demand forecasting in retail operations for fashionable products: methods, practices, and real case study. Annals of Operations Research, 1-17. https://doi.org/10.1007/ s10479-019-03148-8
[24]. Rosienkiewicz, M. (2019, September). Accuracy assessment of artificial intelligence-based hybrid models for spare parts demand forecasting in mining industry. In International Conference on Information Systems Architecture and Technology (pp. 176-187). Springer, Cham.
[25]. In: Dey N., Mahalle P., Shafi P., Kimabahune V., Hassanien A. (eds) Internet of Things, Smart Computing and Technology: A Roadmap Ahead. Studies in Systems, Decision and Control, vol 266 (pp.57-80). Springer, Cham. https://doi.org/10.1007/978-3-030-39047-1_3 1:32.
[26]. Sharma, S. K., & Sharma, V. (2012). Comparative analysis of machine learning techniques in sale forecasting. International Journal of Computer Applications, 53(6).
[27]. Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146.
[28]. Wang, Q. F., Xu, M., & Hussain, A. (2019). Large-scale ensemble model for customer churn prediction in search ads. Cognitive Computation, 11(2), 262-270
[29]. Yeo, J., Kim, S., Koh, E., Hwang, S. W., & Lipka, N. (2016, April). Browsing2purchase: Online customer model for sales forecasting in an e-commerce site. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 133-134). https://doi.org/10.1145/ 2872518. 2889394
[30]. Zhang, J., & Cheng, C. (2008). Day-ahead electricity price forecasting using artificial intelligence. In 2008 IEEE Canada Electric Power Conference, Vancouver, BC (pp. 1- 5).
[31]. Žliobaite, I., Bakker, J., & Pechenizkiy, M. (2009, December). Towards context aware food sales prediction. In 2009 IEEE International Conference on Data Mining Workshops (pp. 94-99). IEEE. https://doi.org/10.1109/ ICDMW.2009.60
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.