Abstract
In a service based economy, companies strive to continue deriving revenue by creating and nurturing long term relationship with clients. A case in point is Retail Banking, where customer value is of utmost importance. In today's hyper-competitive environment, banks are aggressively leveraging their customer base to engage in revenue driving activities such as cross selling and up-selling. To be successful, it is imperative for banks to embrace the power of analytics to gain insights and appropriately evaluate risks and opportunities-enabling more efficient decision making in the quest to enhance share of the wallet. This paper examines the various applications of analytics in Retail Banking and provides pointers for analytic implementations. The paper is divided into 10 parts. Part 1 is Introduction, Part 2 is about Business Analytics, Part 3 is about Data Mining, Part 4 deals with Literature Review, Part 5 is about Theoretical Framework on Analytics in Retail Banking, Part 6 is Methodology and Discussion on results. Part 7 is Inference and Conclusion.
Keywords
Data Mining, Cluster Analysis, Retail Banking, Credit Scoring
Recommended Citation
Singh, Prakash
(2015)
"Leveraging Business Analytics for Marketing Decisions in Retail Banking,"
Management Dynamics: Vol. 15:
No.
2, Article 4: 29-44
DOI: https://doi.org/10.57198/2583-4932.1082
Available at:
https://managementdynamics.researchcommons.org/journal/vol15/iss2/4
