Editor’s note: Scott Gidley is co-founder and chief technology officer of DataFlux.
_______________________________________________________________________________________In the 1980s and 1990s, one of the biggest growth areas for computer software was customer relationship management (CRM). Companies deployed applications for sales, support, marketing and other customer-facing functions to help them track information about the customer. The rationale was simple. Better information on customers would lead to better service — and more profitable customer relationships.
Well, if you’ve ever been frustrated by a customer service representative who doesn’t know your account history, which products you own or even where you live, you know the promise of CRM never came true. Customer service is as frustrating, if not more so, than it was before the emergence of CRM. It would seem that the result of CRM never materialized.
Why did investments in CRM show such little return? Because at the same time the organizations were implementing systems, they gave little concern to the quality of the data filling those systems. In addition, CRM systems were often implemented individually within the different groups throughout an organization, leading to informational “silos.” One division might know your account very well, while the next business unit may not have information on you at all.
Customer Data Integration: Putting It Together
Good customer data…reliable, consistent, and accurate information…is the foundation of strategic business decisions and the lifeblood of the competitive organization. But corporate growth, new technologies, and ever-growing volumes of data from multiple sources can significantly compromise a company’s ability to control the quality of that data. The result is data that leads to bad decisions, poor interactions and, worst of all, frustrated customers.
The solution isn’t simply installing a CRM application and forgetting about it. Customer data is a strategic asset that transcends the boundaries of applications, business units or job functions. To build better relationships with customers, organizations must rethink the way they manage customer data.
Customer data integration (CDI) is an emerging method to compile the most authentic customer information from all applications, databases and customer touch points into one centralized data source. By aggregating the best data information about customers into one dataset, CDI strives to deliver consistent, accurate and reliable information– regardless of the originating application.
The benefit of CDI is that the data itself — not the applications — is the focus. Another way to picture it is that you create a reliable set of customer data to feed every internal application; the CRM application would have access to the same details as a billing system; each business unit can view the same information about customers. This improves support and service across business functions.
CDI solutions typically have two components: comprehensive data quality capabilities coupled with sophisticated identity logic. With these components, users can improve the quality of data while also identifying and managing the same customer sets across sources and applications.
The data quality component typically begins with an in-depth data profiling or discovery phase, during which an organization examines and catalogues existing customer data sources. From there, companies build business rules to standardize and verify addresses and other attributes, reconcile conflicting information, validate name and address information and add demographic data to enhance the value of information.
The next phase is identity logic (a.k.a. identity management), a crucial component in any successful CDI effort. Identity logic determines if customers listed in different sources are the same customer — and intelligently integrates customer information from multiple applications and databases. With identity logic, companies can flag information for linking customers across applications and sources and isolate the best data from the various sources.
For example, let’s say a company has records on James William Smith in different applications. The CRM system may have him listed as Jamie Smith, while the call center system refers to him as James W. Smith and the billing system lists him as J. William Smith. A solution with strong identity logic would determine that these three records are the same individual, provided that other data points (address, social security number, etc.) were similar as well. This means that a company can aggregate all data about Mr. Smith and assign an accurate value to him as a customer.
CDI: How Do You Get There?
Although technology is a key component to any CDI discussion, the problem cannot be solved merely through software. CDI requires a combination of people (to establish standards for high-quality data), processes (to replicate those standards) and technology (to enforce those standards). By combining these three components, companies can create and maintain high-quality data — and create an environment focused on continual data improvement.
When working with customer data, the stakes are high. The rule of thumb for customer retention is that it takes 10 times the amount of money to acquire a customer as it does to keep an existing one. With integrated data, you have the foundation for longer-term, more profitable customers.