Many companies have focused on customer-centric marketing or CRM initiatives over the last several years. Many of these initiatives have failed for a variety of reasons which have been rehashed ad nauseum. I would like to suggest another reason why many of these initiatives failed and suggest how to improve your chances going forward. It’s not about the technology or the processes or the people – it’s about the data!

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 number one 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 entire enterprise.

Becoming data-centric will positively impact all three continuums – technology, processes and people, required for a successful program implementation. Let’s talk about each area in more detail.

Technology – Your marketing data warehouse is built, but is it synchronized with the data in customer service, or finance, or even sales. In other words, does YOUR data 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 info. Data quality is an area that many companies are just now starting to address. With the proliferation of data generated from a multitude of sources, it’s an issue that will be getting a lot more attention in the future.

Processes – Without accurate and 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.

Beware, some data problems are really process problems in disguise. For instance, there are entire departments dedicated to fixing pricing errors generated by the system. In actuality, the system is not providing incorrect prices; it’s providing the most current prices loaded. This is definitely not a data problem, but a process problem. The saleforce should be incented, either negatively or positively, to have the current prices in the system for their customers before orders are generated.

People – Recent studies have indicated that over 75% of the bad quality data is caused by employee data entry errors. This is a big obstacle that can only be overcome, by better training, not just on their job function, but on the impact of their role in the overall enterprise’s success.

Here’s an example of how it works when it all comes together. A credit card company wanted to differentiate their treatment of customers based on profitability. The data from their 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 which displayed on the customer service agent’s screen (process). They CSA didn’t know how profitable a customer was, they just knew that ‘A’ was the best and ‘M’ was the worst.

If a customer called in complaining about a returned check charge on their statement, the CSA was empowered (people) to waive the charge if they were a good customer without a history of returned checks or firmly state they needed to pay the charge if the customer was unprofitable.

Based on these examples, you should understand that the impact of data stewardship is vast. Without it, companies will continue to receive marginal results from their customer-centric marketing programs. By identifying and resolving your data issues, real or perceived, you will solve a number of business issues and at the same time, improve your organization’s business intelligence.