Rethinking the Role of CRM Systems

by David M. Raab

Curtis Marketwise FIRST

June, 2008

Hoping to teach their over-optimistic child about life’s grim realities, 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.

]]> http://archive.raabassociatesinc.com/2008/06/347/feed/ 0 http://archive.raabassociatesinc.com/2008/04/new-technologies-for-inbound-marketing-2/ http://archive.raabassociatesinc.com/2008/04/new-technologies-for-inbound-marketing-2/#comments Tue, 01 Apr 2008 11:49:32 +0000 http://archive.raabassociatesinc.com/?p=53 New Technologies for Inbound Marketing
by David M. Raab
Curtis Marketwise FIRST
April 2008

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We’ve all heard the saying, “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.

The first step is to recognize that mobile phones are not just very small personal computers. Yes, they can receive emails and videos and browse the Web. Some can even run spreadsheets, word processors, and other software. But mobile phones are different in at least four fundamental ways:

- location aware: cell phones know where they are based on GPS and wireless positioning systems. Assuming permission has been granted, marketers can use this information to better serve customers through targeted offers, tailored search results, and new insights into customer needs. There’s more going on here than offering a coupon to someone walking past the window: imagine traffic alerts triggered by a delay along the customer’s normal route to work (the delay itself inferred from cell phone transmissions), or a taxi service that automatically calls the car closest to your current location. Bankers might want something more prosaic like helping customers find the nearest ATM, but bear in mind that every type of contact is a potential advertising or branding opportunity, so there’s more going on here than just delivering bank services.

- always on: many people won’t shut off their cell phone unless explicitly instructed to (and sometimes not even then) This means low balance notices, fraud alarms, answers to customer service questions, and time-sensitive offers can reach customers more quickly. Again, the most compelling applications may come outside of banking, but anything that gains a customer’s attention can be built into a bank’s communications plans.

- integrated billing: payments for purchases made through the cell phone can be collected via the phone bill. This removes a major point of commercial friction at a fraction of the processing cost of credit cards or paper checks (remember those?) Of course, this could hurt banks if it cuts into fee income—but it’s going to happen anyway, so be prepared and make it work to your advantage.

- personal: cell phones are not (yet) physically implanted in people, but in many cases they may as well be. Stored phone numbers, recent call history, downloaded ring tones, saved photos and videos all tie each phone closely to its owner. This makes the phone better suited to truly personalized offers and services than any other communications device. Of course, people do occasionally leave phones at home, lose them or lend them to others, so you can’t assume the owner is always the user, or even present. But for marketing purposes, being right most of the time is good enough.

If phones have so many advantages, why aren’t they used more widely for marketing and customer relationship management? The short answer is the world—and the U.S. in particular– isn’t quite ready yet. Old, limited-capability handsets are still common. Calling costs remain high, particularly for non-voice services like text, Internet, video and music transfers. Advertising inventory is scarce due to low mobile Web usage and because so few sites have been mobile-enabled. Tools to set up mobile campaigns are immature and often usable only by the agencies that developed them. Few marketers have enough experience with mobile promotions to reliably predict what will work.

But all these barriers are falling. It’s impossible to predict when mobile marketing will become “mainstream”, whatever that might mean, but it will be sooner rather than later. What we do know today is that the unique capabilities of mobile phones imply new and unique customer interactions. With mobile phones present during nearly every customer activity, the ability of phones to participate in those activities is limited only by the ability of marketers to find ways for phones to add value. This value could come from making the activity easier, faster, cheaper, or more fun. Many value-adding services will make use of financial information already held by a bank. Still more will provide opportunities to deliver highly targeted messages to promote a bank’s brand or offerings. Bank marketers need to recognize the growing role that mobile phones play in their customers’ lives and look closely at how they can participate in the new world of opportunities the phones will provide.

For executives, this means choosing among a near-infinite set of possible changes: in training programs, incentive plans, products and services, branch design and signage, marketing campaigns, business practices, and more. No organization can afford, or absorb, more than a few of these. How do you decide which to adopt? And how do you know after the fact whether you made the right choice?

Both questions have the same answer: you need a customer-level measurement system that lets you determine the anticipated value of any proposed change, and then measures its actual results when it’s complete. Traditional measurement systems won’t work because they measure profitability by product, branch or account—anything but the customer—and report only past results. Marketing campaign analysis, which shows only single promotions, is even less useful. A service improvement program—or, indeed, of any business change—must be judged by its impact on total customer behavior over a long period of time. In short, it requires measuring changes in customer lifetime value.

Organizations often shy away from lifetime value because it seems so unreliable. After all, how can anyone know what customers will be doing years from now, or what products will be offered or what business conditions will apply? Making investments on the basis of such speculative projections seems risky, if not downright foolish. And since lifetime value figures include profits from previous periods, isn’t it silly to use them to assess changes which can only affect results in the future?

These are legitimate questions, but they reflect a misunderstanding of how lifetime value is used to measure program results. When lifetime value is used for purposes such as finding the allowable acquisition cost of a new customer, the result is a single estimated value. This is often heavily discounted to allow for the uncertainty of future year results. But a calculation to measure program results yields values for at least two scenarios: one with the program in place, and one if the program had never been run. This latter value may be based on historical results or, more scientifically, by setting aside a control group and running an actual test.

Results can still be discounted for uncertainty, but the really important measure is the difference between the two calculated values, not their absolute level. After all, it’s the change in value due to the new program that really establishes what that program is worth. Any other changes in future conditions, such as deviations from expected interest rate spreads, will affect both scenarios similarly, so the difference between the two values should remain about the same. If different assumptions do yield substantial changes in the difference between the scenario results, it’s worth exploring why this happens and calling out that particular assumption as an explicit risk to consider in assessing the proposed change.

The objection to including past profits highlights an important issue: you have a portfolio of customers who are at varying stages in their lifecycles. Clearly the lifetime value calculations used to evaluate a change should only include projections of future behavior. But the impact of the change can vary greatly for customers at different life stages. To pick an obvious example, a change in new customer welcome procedures will have no impact on existing customers.

This simply means that the value of the change must be calculated for your actual inventory of existing customers, rather than some mythical “average” customer. Taking this a step further, you need to analyze how different groups of customers react to any change. You may well find that some groups are not affected, or even affected negatively. If so, you can seek ways to limit execution to customers for whom the change makes sense.

Gathering lifetime value inputs requires tracking all the activities that impact customer value—service costs as well as account transactions—and linking these to form a complete picture of each customer’s results. These statistics must be recorded at regular intervals to track any changes in behavior, and projection models must be built to estimate their future impact. The models must also allow what-if simulation to assess proposed changes before they are even tested.

None of this is easy, but the result is worth the effort: a reliable way to translate your commitment to superior service into programs that improve the results of your business. Anything less and you’re flying blind, which is the greatest risk of all.

Traditionalists. These organizations are the slowest to adopt new technologies and business approaches. Many are still moving from a basic marketing database (MCIF) environment, used primarily for outbound direct mail, to their first attempt at a CRM system that includes live customer interactions at branches and call centers. The key trends for this group are integration of data from multiple back-office and customer contact systems; making the integrated data visible to company representations at customer-facing systems; and providing basic sales functionality such as lead distribution, contact management, and sales funnel analysis. More advanced members of this group are starting to add some statistical intelligence to their marketing efforts. This can include simple predictive models to help target the best prospects for particular products and activity-based triggers to identify customers who have done something unusual enough to warrant personal attention.

Traditionalists are the most likely to use the increasingly-popular hosted CRM systems, such as Salesforce.com, which reside at an outside service agency. Such products are easy to deploy but somewhat limited in their ability to integrate with existing company systems. Particular issues are coordinating real time activities across systems and accommodating complex data structures. The hosted products appeal most strongly to buyers whose limited in-house technical resources make them willing to compromise on flexibility in return for get quick access to much-needed basic functions.

Leaders. These users are striving to make better use of the CRM systems they have already put in place, sometimes many years ago. They too are still working on integration, but from the more sophisticated perspectives of trying to coordinate activities across different channels (direct mail, email, Web, branch, ATM, call center, etc.) and different product lines. These involve organizational as well as technical issues, so the leaders find themselves worrying more about business process than pure technology.

The leaders are also making increased use of advanced analytics to help improve their customer interactions. Analytic tools include predictive models to pick the next best offer for each customer (which is more complicated than finding the most likely responders for a single offer), business intelligence systems to identify business opportunities and trends in customer behavior, and role-based dashboards to distribute customer information to everyone from senior managers to front-line agents.

Leaders are also the most likely to take advantage of the latest functions being added to high-end customer management systems. These include marketing strategy, planning and budgeting for top-down management; project management, workflow, collaboration and content management to improve operating efficiencies and relationships with service providers; and direct control by marketers of list selections, multi-step campaigns, email contents and Web offers. Direct control is important to leaders because minimizing the involvement of technical staff in marketing execution lets them react faster to customer needs and market conditions.

Futurists. These are the handful of visionaries whose firms have successfully deployed cutting-edge CRM technologies and are looking beyond CRM mechanics to true customer optimization. They are reorganizing their businesses around customer segments (although typically retaining product managers as well) and letting the segment managers try to identify and deliver the most suitable experience for each group. They are developing customer value models that analyze profitability based on specific activities and are using sophisticated statistical methods to understand how different marketing and operational experiences influence future customer behavior. They are exploring new interaction types such as social networks, blogs, RSS feeds, location-based messaging, and online communities to see how they can better meet customer needs and firmly cement relationships between the customer and their institution. At the same time, they are developing new techniques such as automated testing and data visualization to effectively manage the exploding number of interaction possibilities.

All organizations do share one trend: the pressure for compliance. Ever-tighter regulations for customer surveillance, privacy protection and financial reporting require more precise data integration, activity monitoring, and record keeping. The burden falls most heavily on the least sophisticated organizations, because their systems need the most improvement and they have the fewest resources available for the work. But more advanced systems require additional compliance techniques, so some effort is required at every level.