Data-driven marketing is in danger of becoming the latest good idea to be stripped of meaning and turned into an empty buzzword.

But when done correctly, it can rank among the most important tools for adding and retaining bank customers.

Banks tend to approach data-driven marketing as if it is the Normandy invasion-marshalling resources to build huge data warehouses and restructure organizations from top to bottom to "focus on the customer."

But as important as these initiatives are, they must not detract from a bank's ability to act quickly and decisively. Modest applications of data- driven marketing are the best first steps to success, and examples like the one that follows bear this out.

At a major retail bank, which will remain anonymous in this article, executives were certain that a newly created telemarketing center was vastly superior to branches for selling a range of banking products. Sales reports showed that telephone reps were achieving good sales rates.

Although they had not done cost analysis, conventional wisdom said the telephone channel was less expensive than a branch for most activities. Thus, the telemarketing center's managers intended to hire more reps and expand their programs.

When a manager from a leading data-driven marketing company joined the bank recently, she was skeptical of the approach.

She pointed out that by employing data-driven marketing approaches the bank could compare the performance of selling products through branches and through call centers.

Even if the telemarketing approach were profitable, the bank could boost performance even more by using sales results to better target future selling activity.

She faced many hurdles in persuading the telemarketing center to use a data-driven approach:

The telemarketing center's managers didn't embrace a data-driven marketing discipline.

Telephone workstations were not designed to capture who was called and the product sold.

Telephone reps were encouraged to use their judgment in deciding what product to sell and how to sell it.

Beyond its credit card business, the bank did not have experience in using data-driven testing and measurement.

Managers were unlikely to invest in building the systems and expertise without demonstrated benefits.

Fortunately, an alternative arose. The new manager recommended creating an initiative to improve telemarketing's sales productivity.

A senior executive became convinced that this would achieve significant returns and found special funding for the initiative. The executive felt that rapid prototyping would accelerate learning from data-driven marketing.

The team assembled to create this method of prototyping included phone- center supervisors, systems staff, the telephone workstation vendor, marketing experts, and several analysts.

The team established an aggressive goal: to complete a market test of cross-selling several products to customers within two months. It also planned to promptly analyze the collected data.

The immediate objective was to improve sales productivity. Once such improvements are identified, organizational changes must be made to let the entire bank take advantage of the benefits.

The team built tools that measured selling costs and the profitability of sold accounts. Systems staff developed programs to track who was called and the treatment they received.

Programs also were created to capture new account information for targeted customer populations. This information was placed in the data base of a desktop computer for analysis.

The results proved quite interesting. Just as conventional wisdom had predicted, telemarketing had improved sales by 10%.

However, the data showed that phone reps called more than 35 prospects to make each sale. This placed the cost of acquiring an account at $70, which was more than twice the cost of acquiring an account through the branch.

Further, average balances of accounts acquired through telemarketing were lower than accounts originated through in-bound telephone or branch sales.

All told, the bank was losing $18 on each account sold through the telemarketing channel.

Armed with this information, the prototyping team built models that showed which customers responded best to sales calls. The models showed that telemarketing center managers could increase cold-calling sales rates by 200% if they targeted their inquiries at high-value people rather than placing random calls.

Out of a pool of one million prospects, the team identified only 35,000 customers who were worth calling. The benefits from this rapid prototyping effort were enormous:

Phone center managers avoided spending $2 million on an effort that would not pay for itself.

They learned that not all products could be sold profitably through telemarketing.

Systems and operations staff received a practical definition of how to manage tests in the phone center and capture information required for measurement.

Looking at data-driven marketing winners demonstrates again that data and technology are a small part of the story for successful data-driven marketing. What really counts is perspective and creativity in using the tools at hand.

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