Florida-Based Bank Optimises Customer Relationship Value Through Innovative Data Approach
This Florida-based organisation has grown from a small community back to one of the state’s largest financial institutions. Over time, the bank built its reputation by maintaining the personalised service of a community bank while investing in the talent, technology and financial products customers expect from a large bank – from facilitating inbound investments and empowering small businesses to financing transformative real estate projects, all factors in shaping the state’s booming economy.
One of Florida’s largest banks
The bank was not effectively assigning its relationship managers to optimise service, balance resource workloads and maximise revenue
Developed a graph data structure and algorithms in the Microsoft Azure environment
Reduced daily data processing time by nearly 96%; automated more than 90% of client relationship management assignments
Maintaining customer relationships
The bank’s senior leadership knew that one of the most crucial elements of success was effectively servicing customers and building long-term customer relationships and they also recognised technology would further enhance those customer relationships. Specifically, they were looking to improve how data was used to help their relationship managers (RMs) become more responsive to customers. Current technology isolated much of the data, hampering efforts to effectively assign relationship managers (RMs) to optimise service, balance resource workloads and maximise revenue. The bank needed to group customer household and organisational relationships, identify high-value relationships and effectively assign RMs to develop and grow those accounts. This required an innovative data-grouping solution to handle account complexities without negatively impacting customers.
The bank’s leaders had a vision of how client data should be presented to RMs and how that data should be orchestrated to create actionable insights. However, the bank did not have a scalable technology solution to build that vision and would need to take a deep dive into the resources available and the related data sets.
Protiviti teamed up with the bank staff for a comprehensive look at the data environment. Client data was stored in a two-dimensional space, with most data constrained to rows and columns. That made it difficult to build data associations and prevented RMs from garnering insights to better serve their clients.
That challenging data environment presented roadblocks to achieving the bank’s vision, since the data needed to be recategorised while business processes, resource management and data governance policies had to be adapted to support the desired capabilities. The bank’s leadership team was also laser focused on maintaining compliance and meeting regulatory needs and worked with Protiviti to ensure that compliance and other banking regulations were fully met.
The project team used Microsoft Azure to build a graph data structure and associated algorithms to organise and analyse nested banking customer relationships.
The team recognised that change management, resource management and operations were foundational to drive the project forward. They also needed to conceptualise how each group of relationships would be assigned to RMs and whether clients would have multiple RMs or other resources interacting with them. The goal of implementing a data driven solution involved using existing technology in a new way and using recent technologies as needed.
The solution framework identified optimal resource assignments, which drove the need for change management to support adoption of new business processes while maintaining outstanding relationships between RMs and clients.
To ensure both consensus and integrity of group relationships, the team:
- Established consistent logic rules for account linkages
- Defined new policies and processes to assign relationship managers
- Constantly refined the criteria based on new insights gleaned from the advanced analytics
Adopting this approach removed variable decision-making and transformed decision making into data driven processes, based on outcomes from the data algorithm results.
A vision realised
The bank’s leadership team’s clear vision and well-defined objectives accelerated the data transformation process, providing RMs with actionable information that improved client relationships.
Data is now updated daily and associated with all other banking data, enabling the bank to gauge the size of each banking relationship and optimise how the resources are assigned.
The innovations implemented created results that exemplified the company’s vision, including:
- Reducing the daily data processing time needed to evaluate, group data and remap the data relationship tree by nearly 96%, from 24 hours to approximately one hour
- Automating more than 90% of client relationship management assignments
- Simplifying the relationship creation and maintenance process
- Enabling the organisation to measure the size of each banking relationship in its multi-billion-dollar portfolio.
- Utilising a data-driven approach to assign customer relationship managers and establish account sales objectives. This includes:
- Quantifying the sales relationship by aggregating the balance per entire relationship
- Assigning relationship managers based on the size of the relationship and the RM’s seniority
- Establishing appropriate cross-sell goal and measurement based on the size of sales relationship per relationship management resources
- Improving collaboration and communication across the organisation after the graphing algorithm was implemented by making the customer/sales relationship objective data driven.
- Developing consistent logic and rules for account relationship linkages.
- Improving customer satisfaction and revenue generation
This banking client now leads the way in financial services innovation, thanks to its data-driven design, mapping and staffing of customer relationships and account management using a graph data structure, with resulting improvements in the organisation’s revenue and overall customer relationships and experiences.
These innovations reduced daily data processing time by nearly 96%, from 24 hours to approximately one hour