Retail Banking Growth Engine
Efficient exploitation of client data to boost sales
Swiss retail Banks are facing intense competition in a close to saturated market. Marketing costs poured into new client acquisition, compined with competition retaliation, often do not bring adequate results. The focus of established institutions is now shifting towards increasing revenue from existing client portfolios – nevertheless the main challenge remains: how to do this successfully in today’s complex multichannel data-driven world while developing customer relationships and loyalty?
Highly innovative solutions such as the Deloitte Retail Banking Growth Engine (RBGE) can help. RBGE is a unique modular suite of advanced customer analytics models developed by Deloitte to help retail banks gain deeper insights into their data and address growth challenges throughout the clients’ entire banking life cycle, from improved individualised servicing through to active relationship development supported by targeted cross- and up-sell of the best suitable products complemented by churn prevention of valued customers.
Which added value does Retail Banking Growth Engine generate for the bank?
- Think about all the information and signals a bank receives from its customers – demographics, channel and product preferences, interactions and transaction behaviours, etc.
- Customer analytics is among the most powerful enablers a bank can use for translating those signals into useful actionable insight about their individual customer needs.
- Just as important, analytics can help deliver customer insights directly to bank sales people who need them most, or to CRM, in a format that makes it easy to understand and act on them.
Applying the RBGE models can deliver increases in bank up-sell by as much as 15-25%, cross-sell up to 45%, and marketing campaign hit rates boosts of up to 30%. An important additional effect which RBGE brings is the improved customer experience due to better segmentation and offer targeting and last but not least savings or better utilisation of the marketing budget.
A comprehensive, yet flexible and truly modular solution suitable for most client situations
RBGE consists of more than nineteen modules which can be flexibly combined in a way that is best suited for an individual bank’s needs. The core offering of RBGE consists of the development of a highly comprehensive database combining both the banks internal client and transactional data enhanced by external data facilitated by Deloitte. Above this database all predictive analytical models are built. These include not only propensity to buy and segmentation models, but also additional models calculating customer lifetime value, churn rates, customer wallet or optimal product offer selection.
- RBGE combines several components complementing each other
- Modular solution provides flexibility and allows for further expansion
- Robustness and accuracy of RBGE is boosted by a combination of several analytical approaches joined together
- RBGE calculates the likelihood to purchase a product from a Bank’s product portfolio for each customer in the database. This allows to deliver highly targeted direct campaigns, identify opportunities for cross-selling and up-selling, as well as calculate identified potential in current clients portfolio for budgeting and decision making
- Main components consist of propensity to buy models jointly delivered with an integral database of client data. Additional components add further benefits or improve the propensity model accuracy
Modelling outputs can be used in many practical ways in daily customer interaction
Results of the models calculated for each individual client in the portfolio are then embedded in the bank’s CRM and/or front-end systems to be used during each client interaction and for selecting the most appropriate client offering for the specific situation, channel and time. Alternatively, the output can be used to run dedicated sales campaigns. Our approach allows aggregation of the results to serve a wide range of sales growth initiatives while significantly improving database quality and providing further revenue potential estimation for fact-based management decisions at all levels.