Ensuring Customer Data Quality On the Web

One of the biggest dilemmas businesses face today is preventing inaccurate and incomplete customer data from finding its way into their databases.

With the rapid emergence of Web-site ordering systems, customers now decide what products or services they want to buy, enter in their personal data and purchase information and place the order.

Unfortunately, customers make data-entry mistakes about 40 percent of the time — either through keying errors, misunderstanding questions or simply not entering various data, such as an apartment number. Among the problems that Web-site-ordering data errors cause are wrong delivery of goods, dissatisfied customers, missed upsell and cross-sell opportunities, and process and program failures.

According to Allen Kane, U.S. Postal Service chief marketing officer and senior vice president, the USPS must move into e-commerce, and package delivery will be critical.

“The key is the return service, because the company that solves the return issue will own the Internet,” Kane said. During the 1998 holiday season, the USPS handled 25 percent of packages that were ordered over the Internet. Of those, 25 percent to 30 percent were returned, he said.

Given the recent increase in package returns among Internet companies, many organizations are realizing that the potential power of online commerce does not just lie in being an Internet player. The key to successful e-commerce lies in package delivery and customer service.

As a result, companies must take their business on the Web a step further and address customer data-quality issues. One important way to limit inaccuracies in a customer database is preventive data-quality. An example is validating customer information during Web-site ordering. The idea is to catch data entry mistakes before they end up in the database.

There are three ways to implement preventative data quality: filtering, conversing and data reuse.

Filtering. During filtering, the data goes through preventive data quality tools and procedures that are corrected based on the company's business rules.

The advantage of filtering is that the process is usually fast. The disadvantage is that it relies on general validation rules that must be applied to all situations. Many times customers present exceptional situations that cannot be anticipated by a filter design.

Filtering is appropriate for requests for information applications, one-time ordering systems or registration systems.

Conversing. Conversing runs like a filter, except that it asks the customer to verify or agree with changes that the data quality tools make. Conversational implementations are more difficult to create than filter implementations. They also slow the process. The advantage of conversing is that the customer acts as the quality administrator and reviews changes. It's recommended for high-value transactions, such as opening an account with an Internet-based trading company as well as for fraud prevention, to verify personal identification or to keep previously stored data up-to-date.

Data Reuse. Data reuse takes data supplied by the customer and accesses other existing customer-related pre-validated data during the transaction. A good example of this method is e-wallet systems, which enable online merchants to access securely stored customer information from an e-wallet provider's system during the transaction. With some e-wallet systems, such as those offered by Microsoft and CyberCash, customers can simply enter a single password during the transaction, which will then access the system and provide all of the necessary customer information to the Internet company.

The advantages to data reuse are that the data is acquired quickly from other sources, it has already been validated and the process is simplified. The biggest advantage, however, is that reusing data reduces the number of keystrokes that customers must make to enter the necessary data, resulting in a significant reduction of Web-site ordering errors.

For many companies, Internet technology has opened huge avenues for distribution. But like so many other revolutionary technologies of the past, it will bring several business challenges with it as it evolves and grows in its use. Companies must look beyond merely being an Internet player. They must now take the next step to ensure their online business processes are profitable and provide the personal attention customers demand. Online customer data quality is an effective solution for companies to move to that next level as they seek to improve their marketing on the Internet.

Eric Malmborg is director of information strategies at Pitney Bowes Software Systems, Chicago. His e-mail address is [email protected]

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