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.

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