IBM’s latest study, From Complexity to Client Centricity, details how banks can adopt a more client-centric approach while still raising revenue. For example, banks should move toward needs-based and behavior-based segmentation by understanding client usage such as when, where, how and on what devices they use the bank’s services, or make other transactions. Such a move can potentially improve risk management, pricing, channel performance and client satisfaction.
The digital revolution has forever changed the balance of power between the individual and the institution. If CMOs are to understand and provide value to empowered customers and citizens, they will have to concentrate on getting to know individuals as well as markets.
They will also have to invest in new technologies and advanced analytics to get a better grasp of how individual customers behave. – IBM CMO Study
IBM Research – India is putting that capability into its Edge Analytics software. A lightweight, non-intrusive technology that connects a bank’s customers with contextual information in real time, it cross-references where the customer is with what he or she is doing to provide useful insights and location-based recommendations, such as offering a discount at a specific store.
Edge Analytics, built on top of IBM’s iLOG and SPSS software, could also alert the bank about customers who use a competitor’s credit cards (causing what bankers call “capital leakage”) and offer incentives to use their credit card, instead. Or, the technology can identify customers with home or auto loans with other banks (more “capital leakage”), and offer them refinancing options.
Benefits of being on the edge
Edge Analytics is embedded within participating banks’ customer channels, such as online banking, ATM, SMS, and mobile banking. When the bank’s customers access their account on these channels, Edge Analytics uses transactional and other contextual data feeds to better understand customer transaction behavior and preferences, for example to detect a customer’s transaction location in real time, or infer the transaction category and spend pattern, preferred shopping location, preferred restaurant or cuisine.
These insights help the bank understand their customer transaction behavior and priorities better to make location-based recommendations or relevant offers in real time, when customers buy a book online or shop at the mall, for example.
Consider the case of a customer purchasing an airline ticket. After the transaction, she receives a message of “Thank you for using your credit card with LufthansaIVR Airi.” Edge Analytics mines such transaction details and applies other relevant data and rules the transaction as international travel. It will trigger an offer for travel insurance from the bank, offer currency exchange options from the bank, or simply inform the customer that she can use the bank’s credit card abroad.
The bank can also provide incentives for these offers, such as certain discounts if purchased on bank’s credit card. For the bank, it would mean a significant improvement in customer experience and satisfaction and also improve revenues and profitability per customer.