Almost every financial services company in the developed world is using a data base for marketing. For the most part, these companies look at data base marketing as something confined to the marketing department.
We need to consider that the benefits of a marketing solution can be extended far beyond that.
By infusing a marketing data base with customer profitability measures, sales and service transactions, customer preferences, risk profiles, and other external information, we transform it into "customer intelligence."
A customer intelligence system can generate significant returns in functions as diverse as financial, risk, and credit management and in information systems operations. Three basic principles should guide the adoption of these solutions:
The organization should expect high returns on the investment.
These returns will come principally from revenue enhancements rather than cost savings.
Many revenue enhancements will be generated outside the marketing department.
The most important key to such large and diversified returns is the incorporation of customer profitability measurements in the data base. You must be able to tell which of your relationships create value, which destroy value, and what drives value creation.
Without this vital information, data base marketing is simply a more expensive way to conduct the inefficient mass-marketing techniques of the past. You may be able to track sales more accurately, but you will not be able to tell whether you are making money in the value exchange.
Almost every independent study shows that financial services companies are probably losing money on most of their new accounts and new customers.
The largest initial returns can be expected in financial management. Most organizations recognize that they have a wide range of profitability in their customer base. Customer intelligence, combined with sharp financial analysts armed with powerful query tools, will let managers uncover profitability skews and their causes.
Action can be taken to minimize the skews and protect the most profitable accounts and customers. It has been shown that organizations can easily improve profits by 25% to 50% by focusing on profitability skews and taking corrective action.
Let's look at some other examples from the product management function.
Consider the traditional savings account. Most banks price this product at virtually no cost, assuming that net interest margin from the balances will offset the cost of the small number of transactions the average customer makes.
Enterprising customers have discovered that these accounts are the cheapest way to maintain a banking relationship. They use the savings account as a transaction account, making repeated deposits and withdrawals in the branch and at the automated teller machine, calling the telephone service center frequently to check on balances, requiring monthly statements, maintaining minimal balances, and paying the bank little or nothing for these privileges.
The problem is in the pricing structure. The enterprise has priced a product for the average low-volume user and hopes to make up the difference in volume. However, it will lose money on customers who make significantly more transactions than average.
Without customer profitability data, companies have no effective way to gauge how price elasticity varies from customer to customer and how it can affect consumer behavior. As a result, they must rely on average-pricing strategies that invariably create unprofitable accounts and relationships.
Customer profitability information lets the company identify unprofitable users and reprice products appropriately. Revised pricing structures can move high-volume users to lower-cost channels or to more fairly priced products like lifeline checking accounts.
The savings account example is an application of a profitability-enabled data base to product management, a function that blends the disciplines of marketing and financial management.
Fee waivers are another example. Product managers create fee schedules to keep transactions and products profitable, but front-line employees typically waive these fees at the first sign of customer discontent. A customer intelligence solution would let the customer service representative make more discerning waiver decisions.
If a customer's profile indicates the relationship is worth it, then the representative could grant the waiver and possibly use the customer information to make a recommendation for an additional product sale.
Though any one decision may seem trivial, fee waivers collectively add up to a major total of lost revenues at most financial companies. These firms must recognize that not all customers have the same value and that it is not always necessary to grease the squeaky wheel.
The same data base that guides a marketing campaign can also guide the customer service or relationship officer in day-to-day decisions. Studies suggest that improved fee-waiver practices can increase some organizations' fee revenue by 25% to 50%.
Financial managers can also use customer intelligence information to guide delivery-channel investments, to provide standardized performance measurement metrics across the organization, or to demonstrate compliance with the Community Reinvestment Act and other regulatory requirements.
In risk management, the promise is that risk managers can use transaction patterns and profitability data to predict such important behaviors as late payments, defaults, and responses to collection attempts. This information can improve credit underwriting and permit more flexible pricing based on total relationships.
Certain patterns may be early warnings that a customer is at increased risk of a default, letting the company take corrective action before a loss occurs. Actions could range from restructuring a credit to referring the debtor to credit counseling.
Activities that reduce loan losses are particularly valuable because loan-loss reductions have an immediate, dollar-for-dollar effect on current-period profits.
The same type of analysis could be applied to collections.
It should be possible to use customer profitability and behavior data to separate those customers who will respond to collection efforts from those who will not. Or collections analysts may be able to determine what sorts of offers work best with certain customer segments.
Companies can then target their limited resources on collection efforts to customers who show higher probabilities of responding. Improved targeting increases recoveries and at the same time reduces overall costs.
A properly carried out customer intelligence solution supports customer relationship management on both the retail and corporate levels. But the most promising application may be the ability to match customer value to level of service.
Some banks, such as First Union in the United States and Westpac of Australia, already assign automatic priority in call center queues to their most valued customers.
Others, such as Capital One, use skill-based routing to direct customers to the appropriate sales and service representatives, a strategy that improves both cross-selling and customer retention.
Information on customer value can be used to offer value pricing packages selectively, to formulate best practices, to identify retention triggers, and to design more effective incentive plans and performance measures for relationship officers.
Finally, the information systems area would benefit from a customer intelligence solution by consolidating the proverbial information silos.
Many balk at the concept of a consolidated data base because of the costs, inflexibility, and limited accessibility that characterized the centralized data bases of the mainframe era. Access to the data repository using simple Internet computing technology such as Web browsers makes the consolidated marketing data base a cost-effective and critical option in today's competitive environment.
Pundits continually remind us to "think globally and act locally." The marketing and customer intelligence solution may be a case in which this admonition can be put into practice.
Companies should plan for marketing solutions to meet immediate departmental needs and at the same time consider extending their systems to benefit other functions across the enterprise.
One caveat: Getting to customer intelligence requires unprecedented levels of cooperation throughout an organization. Bureaucratic and political turf issues must be put aside.