Open Access Open Access  Restricted Access Subscription Access

A Predictive Analytical Study on Factors Enhancing Customer Acquisition and Retention


Affiliations
1 Scholar, New Delhi Institute of Management, India
2 Associate Professor, Department of Business Analytics, New Delhi Institute of Management, India
3 Assistant Professor, Department of Business Analytics, New Delhi Institute of Management, India
 

CRM (Customer Relationship Management) Systems have long been used for strengthening relationships with customers thereby ensuring retention and enhancing business. Data stored in the CRM software can be analyzed to provide deep insights into the customer behavior thus influencing future products and services. Predictive Analytics are a branch of Business Analytics that helps in analyzing the current data, with the help of statistical tools, data mining algorithms, modelling tools, AI or machine learning, to make effective predictions for the future. This paper studies the impact of predictive analytics applied onto the CRM data of the sample Organization (name concealed owing to secrecy issues), which is among the front runners in the Instrumentation Industry in India and has been providing best quality Instruments and allied services through leading edge global technology. This paper examines the significant factors which help in winning a deal by using logistic regression in the reference Organization. Data are obtained from the Customer Relationship Management software provided by the company. The results presented in this paper confirm that the CRM data can be used to predict the probability of winning a deal. It also helps to find factors which are impacting 'Win' or 'Loss' of the opportunity/deal so that businesses can take precautionary measures to avoid potential loss of opportunity. Such analysis is helpful in the creation of new sales tactics, improvement of winning proportions and thereby enhancing sales.

Keywords

CRM, Predictive Analytics, AI, Logistic Regression.
User
Notifications
Font Size

  • Anand, S. S., Bell, D. A., & Hughes, J. G. (1996). EDM: a general framework for data mining based on evidence theory. Data and Knowledge Engineering. 18, 189–223.
  • Ascarza, Eva, Neslin, Scott A., Netzer, Oded, Anderson, Zachery, Fader, Peter S., Gupta, Sunil, (2017), “In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions,” Customer Needs and Solutions, available at https://doi.org/10.1007/s40547-017-0080-0.
  • Kamakura Wagner, Mela Carl, Ansari Asim, Bodapati Anand, Fader Pete, Iyengar Raghuram, Naik Prasad, Neslin Scott, Sun Baohong, Verhoef Peter, Wedel Michel, Wilcox Ron. (2006). Choice models and customer relationship management. Marketing Lett. 16(4):279–291
  • Mirzaei,Tala and Iyer, Lakshmi (2014 ). Application of predictive analytics in customer relationship management: A literature review and classification,Proceedings of the Southern Association for Information Systems Conference, Macon, GA, USA March 21st–22nd.
  • Mueller, B. (2010). Dynamics of International Advertising: Theoretical and Practical Perspectives. Peter Lang second edition 2010.
  • Piatetsky-Shapiro, G. 1995. Knowledge Discovery in Personal Data vs. Privacy - a Minisymposium. IEEE Expert: Intelligent Systems and Their Applications. 10(2): 46-47.
  • Sahar F. Sabbeh (2018). Machine-Learning Techniques for Customer Retention: A Comparative Study. (IJACSA) International Journal of Advanced Computer Science and Applications, 9(2), 273-281.
  • Sheetal Kumari, Renu Balyan, Ashish Bhardwaj (2019). Driving Customer Acquisition and Retention with Predictive Analytics, http://bpo.rsystems.com/whitepapers/RSI-BPO-White-Paper-Driving-Customer-Acquisition-and-Retention-with-Predictive-Analytics.pdf
  • Sinkovics, R.R & Ghauri, P.N. (2009). New Challenges to International Marketing. Emerald Group Publishing.
  • Spinello, Richard A & Bernard Hames Collection (1997). Case studies in information and computer ethics. Prentice Hall, Upper Saddle River, N.J
  • Usama Fayyada, Paul Stolorz, (1997). Data mining and KDD: Promise and challenges. Future Generation Computer Systems. 13 (2-3): 99-115.
  • Yusuff H., Mohamad N., Ngah U.K. & Yahaya A.S. (2012). Breast Cancer Analysis Using Logistic Regression. International Journal of Research and Reviews in Applied Sciences. 10(1): 14-22
  • http://r-statistics.co/Logistic-Regression-With-R.html
  • https://www.analyticsvidhya.com/blog/2015/11/beginners-guide-on-logistic-regression-in-r/
  • https://www.techadv.com/blog/3-predictive-analytics-crmshodhganga.inflibnet.ac.in/bitstream/10603/11075/6/06_chapter2.pdf

Abstract Views: 320

PDF Views: 235




  • A Predictive Analytical Study on Factors Enhancing Customer Acquisition and Retention

Abstract Views: 320  |  PDF Views: 235

Authors

Sahil Dudeja
Scholar, New Delhi Institute of Management, India
Rinku Dixit
Associate Professor, Department of Business Analytics, New Delhi Institute of Management, India
Shailee Choudhary
Assistant Professor, Department of Business Analytics, New Delhi Institute of Management, India

Abstract


CRM (Customer Relationship Management) Systems have long been used for strengthening relationships with customers thereby ensuring retention and enhancing business. Data stored in the CRM software can be analyzed to provide deep insights into the customer behavior thus influencing future products and services. Predictive Analytics are a branch of Business Analytics that helps in analyzing the current data, with the help of statistical tools, data mining algorithms, modelling tools, AI or machine learning, to make effective predictions for the future. This paper studies the impact of predictive analytics applied onto the CRM data of the sample Organization (name concealed owing to secrecy issues), which is among the front runners in the Instrumentation Industry in India and has been providing best quality Instruments and allied services through leading edge global technology. This paper examines the significant factors which help in winning a deal by using logistic regression in the reference Organization. Data are obtained from the Customer Relationship Management software provided by the company. The results presented in this paper confirm that the CRM data can be used to predict the probability of winning a deal. It also helps to find factors which are impacting 'Win' or 'Loss' of the opportunity/deal so that businesses can take precautionary measures to avoid potential loss of opportunity. Such analysis is helpful in the creation of new sales tactics, improvement of winning proportions and thereby enhancing sales.

Keywords


CRM, Predictive Analytics, AI, Logistic Regression.

References





DOI: https://doi.org/10.20968/rpm%2F2019%2Fv17%2Fi1%2F145647