Rethinking the Role of CRM Systems
by David M. Raab
Curtis Marketwise FIRST
June, malady 2008
Hoping to teach their over-optimistic child about life’s grim realities, prostate his parents lock him overnight in a room filled with manure. But the next morning, they find him digging enthusiastically through the muck, happy as ever. “What are you doing?” the parents ask in exasperation. “With all this mess,” the bright-eyed child calls back, “there has to be a pony in here somewhere.”
Make a few changes, and the same story applies to the masses of data that bankers now collect about their customers. Not so long ago, even the most basic customer information was hard to come by: there wasn’t much to begin with—often just account balances from line of business systems—and even that was difficult for marketers to access. But today, data from line of business systems, background information from external sources, and behavioral details from Web site and call center visits are all easily assembled in a data warehouse or marketing database. The trick is no longer building the pile of data, but sifting through it to find the banking equivalent of that pony.
One version of this sifting process involves sophisticated analytical software to determine which data patterns to look for, and then to find those patterns as they occur. This is a challenging task, since there are many patterns to consider, the details vary from bank to bank, and the patterns themselves can shift over time. But products including SAS Interaction Managment, Unica Affinium Detect , Fair Isaac OfferPoint, Harte-Hanks Allink Agent, Eventricity and ASA Customer Opportunity Advisor all provide the necessary capabilities. Most draw on the vendors’ experience with previous clients to provide a starter set of useful patterns and rules as well. While the technical details vary, each system creates lists of customers whose behaviors match patterns that have been identified as significant. Those lists are handed over to a Customer Relationship Management (CRM) system for action.
Another type of sifting happens when the CRM system itself monitors customer activities for specified conditions. This usually looks for simple conditions, such as visits to the mortgage calculator on a Web site, rather than multiple events over time. The result is still a list of customers to contact, either via phone call, email or direct mail.
Yet another version of this sifting happens during actual interactions, when the CRM system uses its record of previous behaviors to help select customer treatments. This may involve nothing more than displaying a summarized list of past behaviors to a banker talking to the customer in person or by telephone. Or, more powerfully, it may recommend specific treatments based on an analysis of those behaviors and other customer data. For automated systems such as Web sites, ATM machines, or telephone voice response units, it may select the actual treatments themselves.
What all these approaches have in common is that the final customer contact is managed in the CRM system. This means that the CRM system, not the behavior detection technique, is the critical link between assembling masses of customer data and getting business value from that data. Banks can and do apply multiple techniques to identifying opportunities within this data, but it’s up to the CRM system to combine these inputs and ultimately determine what the customer actually sees. The CRM system can now be thought of as a “treatment delivery system”.
This is a relatively new role for CRM systems. Their original function was to themselves act as central repositories for customer information and account history. They presented this information to bankers in call centers and branches so they could answer customer questions and make decisions based on relatively complete knowledge. If they provided any guidance regarding specific treatments, it was usually based on campaigns assigned to customer segments or business rules embedded in telemarketing scripts.
This new role imposes new requirements on the CRM software. It must be more open to inputs from external sources, whether accepting leads identified by the behavior detection systems, capturing non-transactional behaviors from a Web site, or accepting recommendations from a predictive modeling system. It must arbitrate among recommendations provided from the different systems, ensuring that customers are treated consistently over time and across channels, and that the most valuable treatments are chosen among the options presented. It must support an ever-growing array of delivery media, seamlessly merging new channels like mobile Web, video and text messages with traditional call center, branch automation and direct mail. Finally, it must report back to the various recommendation systems, telling them what treatments the customer was actual given and how the customer responded. This feedback is crucial for helping the recommendation systems to improve their own performance.
Older CRM systems may not meet these conditions. Many were designed with rigid data models tailored to the specific needs of call centers and sales automation. Even adding support for Web sites and email can be difficult, while real-time integration with recommendation systems can be nearly impossible. Often the systems were extensively customized during their initial deployment to fit the bank’s technical environment and business processes, making additional changes costly and difficult.
Newer CRM systems are generally more flexible, so this is one area where late adopters have an advantage. One word of caution: the latest rage in CRM software, “on demand’ systems that are run by a third party, should be evaluated very carefully in this area. Although their developers have worked hard to make them more flexible, they may still be too limited for a multi-channel, bank-wide deployment.
In some cases, it may be possible to supplement rather than replace an existing CRM system. For example, vendors including eglue and Infor read data from the CRM system and other sources as an interaction takes place, and then deliver recommendations that can be viewed in a separate window. This allows them to control treatments across multiple channels with minimal changes to the channel systems themselves.
Realistically, many banks today will not be able to afford a comprehensive treatment delivery system. Even so, they should still be able to deploy simpler technologies that monitor some types of customer behavior to identify significant business opportunities. Feeding these as leads into an existing CRM or sales automation system can provide substantial value with minimal technical effort. You already have those huge piles of data—so you might as well start digging.
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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at firstname.lastname@example.org.