Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Dixit, Rinku
- Technology and Innovation Management
Abstract Views :260 |
PDF Views:113
Authors
Affiliations
1 New Delhi Institute of Management, New Delhi, IN
1 New Delhi Institute of Management, New Delhi, IN
Source
Review of Professional Management- A Journal of New Delhi Institute of Management, Vol 15, No 2 (2017), Pagination: 77-78Abstract
The book “Technology and Innovation Management” has been written by the author having three decades of experience in the area. The coverage of this book, has been aptly chosen as presently this is the key emerging discipline and is able to provide insight into the subject. The book is divided into eleven chapters and addresses all areas from conceptual foundation to examples of technology led innovation projects. The book is written in a simple and easy to read manner and is designed to equip its readers to implement a concept quickly and effectively before any technology/innovative idea becomes old or irrelevant.- A Predictive Analytical Study on Factors Enhancing Customer Acquisition and Retention
Abstract Views :316 |
PDF Views:232
Authors
Affiliations
1 Scholar, New Delhi Institute of Management, IN
2 Associate Professor, Department of Business Analytics, New Delhi Institute of Management, IN
3 Assistant Professor, Department of Business Analytics, New Delhi Institute of Management, IN
1 Scholar, New Delhi Institute of Management, IN
2 Associate Professor, Department of Business Analytics, New Delhi Institute of Management, IN
3 Assistant Professor, Department of Business Analytics, New Delhi Institute of Management, IN
Source
Review of Professional Management- A Journal of New Delhi Institute of Management, Vol 17, No 1 (2019), Pagination: 38-45Abstract
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
- 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