Data-Driven approaches to improve customer experience in banking: Techniques and outcomes

Data-Driven approaches to improve customer experience in banking: Techniques and outcomes

August 2024 | Ibrahim Adedeji Adeniran, Angela Omozele Abhulimen, Anwuli Nkemchor Obiki-Osafiele, Olajide Soji Osundare, Edith Ebele Agu, Christianah Pelumi Efunniyi
The article "Data-Driven approaches to improve customer experience in banking: Techniques and outcomes" by Ibrahim Adedeji Adeniran et al. explores the significant transformation in the banking industry driven by data-driven approaches aimed at enhancing customer experience. The authors highlight the importance of leveraging customer data to gain insights into behavior, preferences, and needs, enabling banks to deliver personalized, efficient, and secure services. Key techniques discussed include customer segmentation, predictive analytics, personalization, sentiment analysis, fraud detection, and operational efficiency. These techniques not only enhance customer satisfaction and loyalty but also improve operational efficiency, risk management, and revenue growth. The article provides case studies of successful implementations by banks such as Wells Fargo and DBS Bank, showcasing how data-driven strategies have led to measurable outcomes and positive customer experiences. The integration of advanced data analytics, machine learning, and natural language processing is crucial for banks to stay competitive and meet evolving customer demands in a rapidly changing financial landscape.The article "Data-Driven approaches to improve customer experience in banking: Techniques and outcomes" by Ibrahim Adedeji Adeniran et al. explores the significant transformation in the banking industry driven by data-driven approaches aimed at enhancing customer experience. The authors highlight the importance of leveraging customer data to gain insights into behavior, preferences, and needs, enabling banks to deliver personalized, efficient, and secure services. Key techniques discussed include customer segmentation, predictive analytics, personalization, sentiment analysis, fraud detection, and operational efficiency. These techniques not only enhance customer satisfaction and loyalty but also improve operational efficiency, risk management, and revenue growth. The article provides case studies of successful implementations by banks such as Wells Fargo and DBS Bank, showcasing how data-driven strategies have led to measurable outcomes and positive customer experiences. The integration of advanced data analytics, machine learning, and natural language processing is crucial for banks to stay competitive and meet evolving customer demands in a rapidly changing financial landscape.
Reach us at info@study.space