Transforming Customer Experience and Performance: A Local Bank’s Data Management Strategy Success

This case study showcases how a community-based financial institution in the US leveraged a data management strategy that involved cloud-based data infrastructure, a data rules engine, and customer behavior analysis to gain an in-depth understanding of customer behavior and preferences for various banking services.

Looking for similar results for your business?

The Result


Higher Customer Satisfaction Score


Reduced Operational Costs


Better Customer Life Time Value


The client is a community-based financial institution offering a range of banking services, such as commercial and residential lending, to its customers in the US. The bank’s mission is to provide personalized, convenient, and reliable banking solutions that meet the diverse needs and expectations of its customers. Its vision is to become the preferred choice of banking partner for its customers and the local community.

The Challenge

The bank faced several challenges in fulfilling its mission and vision. Some of the challenges were:

  • They had a limited understanding of its customer behavior and preferences across different channels and segments. It made it difficult for the bank to tailor its products and services to suit the specific needs and expectations of its customers.
  • They had a fragmented and siloed data infrastructure that hindered its ability to store, process, analyze, and share its data effectively and efficiently. It resulted in data quality issues, data security risks, data integration challenges, and data analytics limitations.
  • The bank had a complex and manual business rules system that governed various aspects of its operations. It caused operational inefficiencies, errors, inconsistencies, and delays in executing its business processes and decisions.


To overcome these challenges, Innoppl’s data experts proposed a data management strategy that involved three key components: cloud-based data infrastructure, data rules engine, and customer behavior analysis.

The team provided a comprehensive solution:

Cloud-based data infrastructure: We migrated the bank’s data infrastructure to the cloud, using a hybrid cloud model that combined public and private cloud services. It enabled the bank to store, process, analyze, and share its large volumes of structured and unstructured data from various sources in a scalable, flexible, and cost-effective manner. We also implemented data governance, data quality, data security, and data analytics best practices to ensure effective data management across the organization.

Data rules engine: We used a data rules engine to automate complex business rules that governed various aspects of the bank’s operations. We designed its rules engine architecture, developed rules engine logic, deployed rules engine applications, tested rules engine functionality, maintained rules engine documentation, and updated rules engine performance. It enabled the bank to improve its operational efficiency, accuracy, consistency, and agility in executing its business processes and decisions.

Customer behavior analysis: We helped the bank use various tools and techniques to analyze their customer behavior across different channels and segments for different purposes. They collected customer behavior data from various sources, processed customer behavior data using various methods, visualized customer behavior insights using various tools, applied customer behavior recommendations using various actions, and measured customer behavior outcomes using various metrics. It enabled the bank to gain an in-depth understanding of customer behavior and preferences for various banking services and improve its customer satisfaction, retention, loyalty, and profitability.

What Our Client Says

The client is very pleased with the results of our data management strategy. It has helped them to understand their customers better and provide them with more personalized and convenient banking solutions. It has also improved their operational efficiency and agility, as well as their compliance and risk management. They feel Innoppl has been a valuable partner for their digital transformation journey.

Results Obtained

By leveraging a data management strategy, the bank achieved significant results in terms of its performance and competitiveness. Some of the results were:

  • They increased their customer satisfaction score by 15%, their net promoter score by 10%, and their customer lifetime value by 20%.
  • Reduced operational costs by 25%, error rate by 30%, and decision time by 40%.
  • Enhanced their product development, service improvement, marketing campaigns, risk management, and compliance capabilities.
  • Gained a competitive edge over their rivals and increased their market share and revenue.

Copyright © Innoppl Inc. All rights reserved.

AWS SISense Tableau Power BI Pyramid