It uses transactional data to produce a value metric

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It uses transactional data to produce a value metric for each account, allowing this measure to be used not only for individual customers but also at every level of the enterprise. Aggregation to product or channel level is easy to carry out but remains consistent across the Bank because individual identifiers are attached to each account or set of events, which have been used to generate the base value metric. The application was championed in the first instance as a way to start improving the segmentation process. Product managers quickly saw great value in the resource in financial terms. They now have consistent product-related profitability measures that Value Analyser produces, in addition to customer-based profitability. It is also seen as a boon to risk management, which uses historical models but had not been able to analyse the impact of their decisions on a portfolio of customer relationships. A report has now been written into the system, which shows the impact at a customer level, not just at a gross profit level. Says Burrows, “the system will become common data. It has opened unexpected doors.” Because of the CRM process already in place in RBC, it was recognized that the Bank needed to be able to look at client profitability at a transaction and account level, not using
averages. It also needed to understand the dynamics of its customers and see what the impact was on the customer profile of discretion at branch and delivery channel level. A three-dimensional profit metric is now used within the Bank. At an organizational level, it is able to deliver financial statements on the following: assets, liabilities and equity income and expenses contingencies inter-company transfers. These can be examined by total bank, non-bank subsidiaries, and shareholder. Product level metrics are taken on the same financial dimensions across each product line – loans, savings, accounts, insurance, etc. The customer-level segments the financials by personal account, with corporate and small business accounts under development. One of the difficulties initially encountered in determining profitability on a product such as customer loans was to disaggregate the financial data into meaningful values at a customer level. Previously, this information had been averaged out. This was where the processing power of Value Analyser proved its worth, because it allowed individual transactional and event records to be examined. The system has also made it possible to develop improved activity-based cost drivers and assign these to individuals and events. As a result, it was recognized that some previous assumptions were faulty. For example, many of the Bank’s younger customers operate a savings account with low balances and few transactions and no bank card. Inputs into CVM Five areas of cost have been used as inputs into the client value metric produced by Value Analyser: 1. Net interest revenue – income derived from interest paid on outstanding credit balances and loans.

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