Tracking customer needs

“Treat me as an individual, not a number. Demonstrate that you know me. Anticipate my needs. Do all this regardless of where and when I interact with you.”

It hardly comes as a surprise today to learn that’s what customers really want, but when the Royal Bank of Canada gathered this information in 1977 with a gap analysis to identify which areas of its CRM strategy required greater focus, most companies had yet to bring their focus away from their own product base and to the end consumer.

Five years ago, it was hard to tell the financial institutions apart, says Richard McLaughlin, the Royal Bank’s enthusiastic ambassador of customer relationship management (CRM). Officially titled vice-president, CRM and Information Management, he adopted the label Chief CRM Officer “to communicate how seriously we take our commitment to CRM,” he explains. Since joining the Bank in 1977, he has been responsible for the strategic leadership and program implementation of the bank’s CRM capability. That capability has earned productivity and other awards, as well as prompted McLaughlin to carry their CRM message to business audiences around the world.

“We are proud of our accomplishments and are not afraid to share them,” he explains.” We also feel an obligation to tell a positive story about CRM in light of the negative press that has been told highlighting the failures.”

That story starts in 1978 when the bank began collecting client data and established a centralized view of clients. The data was used to make decisions at a client/branch level. While forward-thinking, it had the downside of creating inconsistent approaches to clients. As well, time that front line staff could have used for contact with clients was shortened by their responsibility to analyse data.

The story picks up speed in 1992 with their first profitability engine at the client level which the bank used to divide client data into three distinct profitability segments. McLaughlin describes that progress as “interesting but not actionable.” It did not provide any opportunity for pro-activity, performance measurement or predictive modeling. While the capability provided front line staff with customer segment codes, it required subjective interpretation. A corporate-wide consistent, strategic approach to customers was still lacking.

In 1995, Royal Bank installed an NCR Teradata data warehouse which proved to be adequately robust to capture millions of client transactions on a daily basis in a centralized system. McLaughlin explains that they chose Teradata because it had the lowest total cost of ownership, a significant consideration when storing six to seven terabytes of data. The data warehouse provided centralized decision support which in turn addressed the high costs of branches getting support information at their level. Centralizing created a huge efficiency, says McLaughlin.

In addition to being able to capture and quickly analyse the millions of daily client transactions, implementing the Teradata data warehouse reduced decision support costs 75 per cent at the branch level. Branch account managers could spend the majority of their time focusing on customers, rather than studying analytics. Time spent on direct marketing was reduced by 70 per cent while the response to that direct marketing improved. The bank was also able to query the same data multiple ways, leaping from a maximum of 60 queries daily in 1995/6 to tens of thousands of queries today.

Leveraging that data warehouse capability, in 1997 the bank began creating a sample profitability prototype. Later that year, it served as an early beta site for NCR to refine and further develop account-level profitability measure software which NCR would market to other financial institutions as the Value Analyzer. The bank adopted this tool the following year. The bank also replaced its in-house CRM analysis engine with Strata Enterprise rules-based decision engine from American Management System in 1998. Both products are still used today. The bank’s work with NCR on Value Analyzer continues as NCR develops new releases.

Identifying important customers

The bank’s CRM efforts rely heavily on the ability to create and apply behavioural-based analytical models to identify the most important customers today and predict who they will be in five years.

Notice that the search is for the most important, not just the most profitable. It isn’t enough just to identify who are the most profitable customers, as not all profitable clients have potential, Ga

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