Data Centricity Leads to Customer Centricity

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Building a customer-centric marketing or CRM initiative on inaccurate or poor-quality data is like building a house on sand. The foundation will keep shifting until the house finally comes down. Getting the right data into your program should be the No. 1 priority in becoming customer-centric. Sound data stewardship requires that the data be current, consistent and accurate whenever it is used or accessed. A potential side benefit to this data-centric approach is improved business intelligence throughout the enterprise.

Becoming data centric benefits all three continuums - technology, processes and people - required for a successful program implementation.

Technology. Your marketing data warehouse is built, but is it synchronized with the data in customer service or finance or sales? In other words, does your warehouse have the most current information or complete view of your customers? We've all heard of companies that can't bill their customers because of missing or inaccurate address information. Data quality is an area that many companies just now are addressing. With the proliferation of data from a multitude of sources, this issue will get a lot more attention in the future.

Processes. Without accurate, repeatable updating processes, you risk leaving essential customer information "on the cutting room floor," thus providing an incomplete picture of your customer relationships, which leads to poor customer service.

Some data problems are really process problems in disguise. For instance, there are entire departments dedicated to fixing pricing errors generated by the system. But the system is not providing incorrect prices; it's providing the most current prices loaded. This is a process problem, not a data problem. The sales force should be given incentives, either negatively or positively, to have the current prices in the system for their customers before orders are generated.

People. Recent studies indicate that 75 percent of bad-quality data is caused by employee data entry errors. This big obstacle can be overcome only by better training, not just on their job function, but on their role in the enterprise's success.

Here's an example of how it works when it all comes together. A credit card company wanted to differentiate treatment of customers based on profitability. The data from its financial system (technology) was used to allocate direct revenues and expenses to each customer and allocate indirect expenses based on balances, number of transactions, etc., to create a profitability score. The total customer profitability tied reasonably well to the company's total profitability.

This three-month average score was simply an alpha code that displayed on the customer service agent's screen (process). The CSA didn't know how profitable a customer was, just that "A" was the best and "M" was the worst. If a customer called to complain about a returned check charge on her statement, the CSA was empowered (people) to waive the charge if she was a good customer with no history of returned checks, or state firmly that she needed to pay the charge if the customer were unprofitable.

The impact of data stewardship is vast. Without it, companies will continue to receive marginal results from customer-centric marketing programs. By identifying and resolving your data issues, real or perceived, you solve numerous business issues and improve your organization's business intelligence.

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