Crm Rbc Financial Case Analysis

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  • Topic: Royal Bank of Canada, Customer service, RBC Dominion Securities
  • Pages : 8 (1564 words )
  • Download(s) : 826
  • Published : July 23, 2010
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The Royal Bank of Canada using CRM and customer profitability tools to gain a competitive advantage in Canada's increasingly crowded financial services market.

Key Issues:

RBC financial, a full service bank in Canada is facing fierce competition from leading financial institutes and new niche-market entrants after deregulation, the bank is also struggling with its 7th ranking out of 8 among financial institutions in the bank’s internal value for money study.

Mr. Mclaughlin, the VP of CRM and information management have several questions in his mind –

• Having the real customer profitability numbers and using CRM tool, RBC know clearly about the customer preferences and needs, issue is what should RBC do with this information?

• How can they turn unprofitable customers into profitable ones?

• Is there a way to enhance the Bank’ value in the eyes of the banking public?

• How can they put the while picture together and make decisions that work for both the Bank and their customers?

Reorganization around CRM

As given in exhibit2, segmentation done based on customers life stages and complexity of their financial needs –


Key Grp: Low current value but many within these sub segments have potential to provide higher level of profit

Growth: Still growing their assets, and have high credit and financial advisory needs. Strategy – to retain, grow and consolidate the relationship.

Prime Grp: More mature customers in accumulation and preservation phases with significant potential for RBCFG offerings.

Q1. What are the key elements of CRM at RBC financial Group?

Initially RBC mktg team deployed a tool, the model worked as follows -

Customer Profitability and Potential Measurement

Aggregate Information rather Actual Data

For Customer Profitability measurement, the tool used personal banking data to measure the profitability that used aggregate information rather than actual data. Then using this data(of approx 8 million customer base) and distributed the profit over deciles(1/10th each).

100/20 rule

Derived 100/20 rules, means 20% customers accounted for 100% profit,

The model divided the customer base into three large baskets -

a. Basket “A” = > customers made the most profit

b. Basket “B” => customers made some profit

c. Basket “C” => customers broke even or lost money


o Helped in aligning the sales force around customer profitability and planted the seeds for the new customer centric organization.

o Not refined enough for advanced channel optimization or relationship pricing.

o It was also found that in some instances customers were treated without consideration of potential business they can contribute.

Requirement => Most robust profitability measurement; thus a better model => bought a software from NCR – Value Analyzer - providing following benefits -

• High Processing Power => can take large size actual data

• Calculated Profitability faster => due to its high processing power and provides

• More accurate spread of information

• Created a Better Understanding of Customers

• Help Determine Customer tolerance


Customer profitability calculations not enough as customer can be both profitable and having the potential to be profitable, bank need both kind of customers.

Finally, calculated Future Profitability and Lifetime Value and segmentation is doe based on that

Future Profitability and Lifetime Value

1) Calculate Present Value of Profit : Assuming profitability percentile of client remains constant throughout expected lifetime, calculate the present value of those profits

2) Factor in Variables: (such as: Age, Tenure with Bank, , Number of Products Held, Probability...
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