Data Monetization Through Cross Industry Collaboration in Retail Banking
This study investigates how data sharing between retail banks and e-commerce platforms, facilitated by data monetization, can improve customer experience in banking. Recognizing that most banking customers also utilize e-commerce services, the research explores how collaboration can benefit both par...
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| Format: | Article |
| Language: | English |
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Ram Arti Publishers
2025-06-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
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| Online Access: | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/37-IJMEMS-24-0529-10-3-729-753-2025.pdf |
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| author | Sandeep Dey Prasun Das Indranil Mukherjee |
| author_facet | Sandeep Dey Prasun Das Indranil Mukherjee |
| author_sort | Sandeep Dey |
| collection | DOAJ |
| description | This study investigates how data sharing between retail banks and e-commerce platforms, facilitated by data monetization, can improve customer experience in banking. Recognizing that most banking customers also utilize e-commerce services, the research explores how collaboration can benefit both parties. By analyzing customer data from both industries, the study develops propensity models to achieve market penetration and enhanced customer satisfaction. These models help identify high-potential customer segments for targeted product and service offerings. Conversely, e-commerce platforms can leverage banking data to target credit card promotions to customers with a history of high spending or large credit limits. This collaboration allows both industries to personalize their offerings and recommendations, ultimately leading to a more positive customer experience. The study proposes a novel framework for customer experience improvement through this collaboration. This framework utilizes three key pillars: portfolio segmentation, lead generation through e-commerce attribute propensity modeling, and banking attribute propensity modeling. By segmenting customers based on shared characteristics and predicting behavior based on specific data sets, the framework allows both industries to identify valuable leads and personalize their offerings, fostering customer acquisition and satisfaction. This research, focusing on a B2C collaboration approach, contributes valuable insights to a less-explored area within customer experience research. |
| format | Article |
| id | doaj-art-8dae05a378094e2abcd9f198625b41c2 |
| institution | DOAJ |
| issn | 2455-7749 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj-art-8dae05a378094e2abcd9f198625b41c22025-08-20T02:59:46ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492025-06-01103729753https://doi.org/10.33889/IJMEMS.2024.10.3.037Data Monetization Through Cross Industry Collaboration in Retail BankingSandeep Dey0Prasun Das1Indranil Mukherjee2School of Management Sciences, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India.Statistical Quality Control & Operations Research, Indian Statistical Institute, Kolkata, West Bengal, India.School of Management Sciences, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India.This study investigates how data sharing between retail banks and e-commerce platforms, facilitated by data monetization, can improve customer experience in banking. Recognizing that most banking customers also utilize e-commerce services, the research explores how collaboration can benefit both parties. By analyzing customer data from both industries, the study develops propensity models to achieve market penetration and enhanced customer satisfaction. These models help identify high-potential customer segments for targeted product and service offerings. Conversely, e-commerce platforms can leverage banking data to target credit card promotions to customers with a history of high spending or large credit limits. This collaboration allows both industries to personalize their offerings and recommendations, ultimately leading to a more positive customer experience. The study proposes a novel framework for customer experience improvement through this collaboration. This framework utilizes three key pillars: portfolio segmentation, lead generation through e-commerce attribute propensity modeling, and banking attribute propensity modeling. By segmenting customers based on shared characteristics and predicting behavior based on specific data sets, the framework allows both industries to identify valuable leads and personalize their offerings, fostering customer acquisition and satisfaction. This research, focusing on a B2C collaboration approach, contributes valuable insights to a less-explored area within customer experience research.https://www.ijmems.in/cms/storage/app/public/uploads/volumes/37-IJMEMS-24-0529-10-3-729-753-2025.pdfcross industrycustomer satisfactionretail bankecommercecustomer experience |
| spellingShingle | Sandeep Dey Prasun Das Indranil Mukherjee Data Monetization Through Cross Industry Collaboration in Retail Banking International Journal of Mathematical, Engineering and Management Sciences cross industry customer satisfaction retail bank ecommerce customer experience |
| title | Data Monetization Through Cross Industry Collaboration in Retail Banking |
| title_full | Data Monetization Through Cross Industry Collaboration in Retail Banking |
| title_fullStr | Data Monetization Through Cross Industry Collaboration in Retail Banking |
| title_full_unstemmed | Data Monetization Through Cross Industry Collaboration in Retail Banking |
| title_short | Data Monetization Through Cross Industry Collaboration in Retail Banking |
| title_sort | data monetization through cross industry collaboration in retail banking |
| topic | cross industry customer satisfaction retail bank ecommerce customer experience |
| url | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/37-IJMEMS-24-0529-10-3-729-753-2025.pdf |
| work_keys_str_mv | AT sandeepdey datamonetizationthroughcrossindustrycollaborationinretailbanking AT prasundas datamonetizationthroughcrossindustrycollaborationinretailbanking AT indranilmukherjee datamonetizationthroughcrossindustrycollaborationinretailbanking |