Improving customer relationships has been on the top of banks’ strategic to do list for years, but making the transition from a product-centric model to a customer centric model has seemed difficult for many banks to realize. One reason could be the difference between customers and products: Products have a small number of defined attributes that are easily defined. Customers are multifaceted with dynamically changing needs and profiles. Banks that try to manage relationships with customers with the same technology that they used to support their product-orientated business model are doomed to fail.
Most retail banks have committed to evolve from a product-centric to customer-centric approach, but it’s only relatively recently that banks have realized the extent to which in memory computing can accelerate this transition.
Today many banks and consulting companies are working on customer centric business models to increase customer satisfaction. In Deloitte’s research study Building Customer-Centric Business Models in Retail Banking Deloitte recommends building business analytic capabilities as part of the future retail-banking business model.
Why In Memory Underpins Customer Centric Retail Banking.
In my opinion business analytics is the most important enablers of customer-centricity. To become a customer-oriented retail bank a strong understanding of customer preferences and banking habits are necessary. Therefore, banks need a systematic approach for collect all customer-specific data, process data in real-time and use the gained information for managing customer relationships.
But traditional CRM systems are separated in analytical and operational systems. This is bound to lead to trouble during the transformation to customer-centricity because separated systems make it impossible for a bank to analyze all available customer-data in real time and to manage relationships effectively. For this reason I want to supplement the Deloitte statement about business analytics.
My recommendation is that any customer-centric retail banking business model must have built real-time business analytics with integrated capabilities for end-to-end business processes and this can only be done by using In-Memory technologies. With integrated architectures banks can make responsive, even proactive business decisions in real-time. This will transform how all customer facing lines of business will maintain customer relationships:
For example, integrated real-time capabilities can help banks to simulate patterns of customer churn and identify customers who are about to move away to other banks. With an In-Memory approach you can track and make pro-active business decisions when churn probability increases. So the real question for a bank is then: What can I do increase customer satisfaction to stop the customer from leaving?
Not All In Memory Approaches are Created Equal.
Almost every conversation that I have with retail bank IT people will contain the phrase “banks are different. We hold far more data than any other industry” and they’re right. (They rarely talk about utilization rates though). This leads me to refine my recommendation: a customer centric retail banking model can only be delivered using a high capacity and high speed In Memory system. In my opinion there’s currently only one solution that fits this specification its SAP’s HANA. (http://download.sap.com/download.epd?context=CCBE2C4417A4583A96A74DC0E2D820576429CADB38C81772D70FE496C29B362BCBBAB6D377ADC5D7063F40D27A0790298C2238F3F161645E)
With SAP HANA banks are able to store up to 100 TB (s. SAP HANA Performance, 2012, P.1) of customer data in the main memory. Combined with columnar-based data storages HANA delivers breakthrough analytic performance for analyzing massive amounts of customer data. Furthermore HANA makes it technically feasible to run operational and analytical data on the same database. These HANA capabilities enable banks to implement complete new customer-oriented use cases that had been impossible in the past to execute.
The journey towards customer-centricity has just begun and with HANA banks can gain a real competitive advantage by making real customer centricity possible. But before packing bags for the journey to customer-centricity with HANA a bank should ask itself four questions:
- What are the key areas in our customer value chain where In-Memory Technologies like SAP HANA can deliver benefits and value?
- What would a target IT architecture using in-memory computing look like?
- Which is the best transformation path to integrate in-memory computing?
- What are the benefits and risks of implementing SAP HANA, and when will the investment pay off?
Interested in more? Stay tuned…
Business Transformation Services / Financial Services EMEA