2008 Jun 03

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 draab@raabassociates.com.

2008 Apr 01
New Technologies for Inbound Marketing
by David M. Raab
Curtis Marketwise FIRST
April 2008

Bank marketers are increasingly recognizing the opportunities of customer-initiated contacts such as telephone calls and Web site visits. Outbound messages risk being ignored, viagra 60mg ill-targeted or intrusive, while inbound messages start with the customer’s attention, can be tailored directly to the situation, and are clearly triggered by the customer’s own actions. As a result, they are much more likely than an outbound message to yield a productive result.

Given these advantages, why isn’t inbound marketing more common? The problem is simple: like tongue-tied adolescents as a school dance, banks don’t know what to say when an opportunity presents itself. So they stare down at their metaphorical feet, listen silently to the hold music, and eventually wander off without having tried to make a connection.

But help is on the way. New technologies can teach banks to understand what customers want and how to offer it to them. They can even deliver the right message at the exact moment it is needed. Think of them as can’t-miss pickup lines for financial institutions.

The first challenge in successful inbound marketing is listening. Having a human involved helps—but call center and branch agents focus on solving the customer’s immediate problem, not assessing the situation for marketing opportunities. Nor do most agents have the skills, training or personality to do a good job of marketing. So whether an agent is involved or it’s a fully automated interaction on a Web site or ATM machine, technology should handle most of the marketing-related listening.

This listening has at least three components. One is literally understanding the customer’s words. The process might begin with spoken words that are converted to text through speech analysis software, or it may originate as text in an email message, Web query or agent call notes. Either way, text analysis software will then parse the message to identify key attributes such as products mentioned, terms demanding attention (e.g., “attorney”), and emotional content. Current technology can extract these sorts of items, but it hasn’t reached the stage where it can reliably understand the exact meaning of how they are being used. That is, the software might recognize that a conversation involves free checking accounts, but not assess whether the customer already has an account or is considering opening a new one, let alone precisely which features would be most important.

This brings up the second component of listening: tracking specific activities in company systems. Technology can monitor the events during an interaction—accounts opened or closed; deposit, transfer, and withdrawal transactions; balance inquiries; data from forms; search terms entered; Web pages viewed; and so on. These are much less ambiguous than streams of text.

Current transactions can be further enriched by the third component of listening, which is placing the current interaction in context. This brings in customer data such as existing accounts and balances, past transactions, service history, and background information in company systems or from external sources like a credit bureau. It can also include non-customer data such as the current workload in the call center, current promotions, and profitability of specific products.

Taken together, these three forms of “listening” provide a rich view of a current interaction: a much richer view, in fact, than a human agent could assemble on her own. It can be hard work to assemble all this data, and the initial implementation of an inbound marketing system is unlikely to be complete. But even partial data can be adequate input to the next task: making sense of what’s happening and deciding what to do about it.

This step usually involves a combination of business rules and statistical models. The models predict specific behaviors, such as probability of accepting a particular product offer or of closing an account. The business rules make decisions, or recommendations if a human is involved: offer this product, waive that fee, present these selling points. The rules themselves often incorporate model scores, which helps keep the rules simple: the rule might indicate it’s time to make a product offer, but let the model select the specific product based on likelihood of acceptance, profitability, expected impact on retention, and other factors. Rules and models can also provide non-marketing guidance, such as flagging a transaction for fraud review or identifying a credit risk.

Once the system has decided what to recommend, this must be fed back to the system conducting the interaction itself—the call center, branch workstation, Web site, ATM, or another. Modern “customer-facing” systems are designed to make this possible without major modifications. Older systems can be harder to work with, but technologies exist to allow superficial integration even if the inner workings of the customer-facing system remain hidden.

The final step in the inbound marketing process is learning from the results. The system records the decisions it has made, what was actually presented to the customer (which may not be the same thing if a human discretion is involved), and how the customer responded. Some systems automatically analyze this information and adjust future recommendations to be more effective. In other systems, the information is analyzed separately and then reviewed by business people who decide whether changes are needed. Automated and non-automated approaches each have their advantages, but in practice, even an automated system must be watched closely by human beings to ensure it doesn’t spin out of control.

What benefits can marketers expect from these sorts of systems? Published results can be hard to find, but here are a few. Key Bank increased revenue per call by 23% after installing a call center recommendation system from eglue www.e-glue.com. Barclay’s Bank doubled the number of inquiries on portions of its Web site using Omniture’s Touch Clarity www.omniture.com to tailor content based on observed customer activity. Holland’s Spaarbeleg retail bank added $30 million in sales on one million calls to its service center with SPSS www.spss.com PredictiveCallCenter.

In other words, this isn’t science fiction. Inbound marketing is a proven approach with very substantial benefits. It’s one you should give a good close look at your own institution.

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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 draab@raabassociates.com.

2008 Feb 01

Building Customer Relationships
by David M. Raab
Curtis Marketwise FIRST
February 2008


We’ve all heard the saying, price “Military justice is to justice what military music is to music.”*. Something similar applies to customer relationships: they also serve a purpose different from relationships in general. Not to put too fine a point on it, the purpose of customer relationships is to make money.

Every businessperson knows this. But it’s still easy to get carried away with the romance of building a relationship management program. So let’s be clear: companies nurture customer relationships so they can sell more, reduce costs, or keep customers longer. If a program doesn’t serve at least one of those goals, it isn’t worth having.

But how, exactly, do relationship management programs do this? Other than creating warm feelings towards your bank—not a very reliable motivator, alas—relationship programs produce specific benefits. These include:

– reduced sales effort because customers trust the bank will offer products that suit their needs
– more information for targeting because customers are more willing to share their data with you
– first chance at providing new products because customers turn to the bank before checking elsewhere
– less price competition because customers will do business with the bank unless an alternative is substantially cheaper
– more referrals because customers are satisfied with their own treatment
– higher switching costs for customers because they get multiple services from the bank
– lower service costs because customers understand bank processes and self-service systems

Relationship building investments can be judged by their contribution to these benefits. Let’s see how a few common investment opportunities stack up.

– Customer Relationship Management (CRM) systems. These systems deliver customer data to sales and service personnel. But the data contributes little to customer relationships unless bankers are also given tools to use it wisely. This is why basic CRM systems are often supplemented with analytics that identify the best treatments for each customer. This helps bankers improve the relationship by making more relevant suggestions. CRM systems can also make the bank easier to deal with—and thus preferred over competitors—by letting bankers easily find customer data, thereby speeding and simplifying many interactions. In addition, the CRM system provides a convenient mechanism for capturing customer information in the first place.

– Self-service systems. These create opportunities for customers to bank when, where and how they want to. They include ATMs, Web sites, automated telephone systems, mobile devices, and whatever the technologists will dream up next. Self-service has many advantages: lower costs, greater customer convenience, barriers to switching because of the effort to learn someone else’s systems, and incentive to add new services that share data or functions with existing ones. The challenge to marketers is that customers often resist self-service systems at first, both because of the effort to learn them and because initial implementations often harder to use then traditional methods. Yet the benefits that banks gain from these systems can justify substantial investments in encouraging adoption—something not all bankers have fully realized.

– Customized services. These are services such as alerts for low balances, overdrafts, or market events. They are nearly always self-managed by customers, so they could be considered a type of self-service system. But the technologies involved are different enough that they should be treated separately. The relationship aspects are different too: these services are less about “high tech” efficiency than “high touch” personal treatment. This suggests a different approach to promoting these services, even though they face the same adoption hurdles (customer awareness and training) as self-service. Their primary relationship benefit is improved retention, since customers are reluctant to spend the time to convert to another bank’s system, and even more reluctant to convert to a bank that doesn’t offer the services at all. On the other hand, they don’t really save money, since the services would not otherwise be provided at all. And while they may generally improve a customer’s attitude towards the institution, they are largely tied to specific products, so they provide little direct incentive for customers to add new ones.

– Targeting. Banks can choose from a variety of tools to select offers for individual customers. Labels and technologies overlap, but it’s worth distinguishing three types of targeting systems based on how they are used:

– Event detection systems analyze customer transactions for patterns that indicate opportunities such as a funds to invest or risk factors such as loss of a job.

– Recommendation engines analyze interactions like Web site visits or telephone calls as they happen and suggest appropriate offers based on customer behavior. These engines may also factor in other information about the customer if it can be linked in.

– Predictive models use historical data to select offers for outbound promotions such as direct mail and email. They are also sometimes applied in real time within recommendation engines.

In each case, the goal of the system is to make better recommendations. This contributes directly to increased sales by making more effective use of business opportunities. It also improves the relationship indirectly by building trust that the company “understands” customer needs and acts to fill them. Of course, trust will not be built if the recommendations appear inappropriate or, even worse, contrary to the customer’s best interest.

– Branding. Brand marketing and relationship marketing are sometimes treated as opposites, but this is a false dichotomy. It is based on the idea that brand marketing is aimed at masses while relationship building targets individuals. This is often (but not always) true, but it doesn’t matter. A strong brand will encourage customers to do business with the company, to trust it, and to accept premium pricing. So even though the messages may be targeted differently, the business benefits are the same. This positions brand marketing as a valid competitor for relationship building funds.

Which tool is the best choice for relationship investments? That depends on the value provided in return. Measuring that value can be difficult, but we know it will be based on the relationship benefits presented above. Even an informal comparison of the benefits of the different relationship building tools will help you make a sound decision.

*usually attributed to Groucho Marx, sometimes to Georges Clemenceau. Take your pick.

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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 draab@raabassociates.com.