Data Quality, ID Logic Find Your Customers
The CEO set out to send a letter to customers to explain the situation and provide an idea of when operations would return to normal. He passed the request to the vice president of customer relations, who in turn asked the IT department to generate a list of all customers for that particular center.
The IT staff pulled reports from its enterprise resource planning, customer relationship management, billing and supply chain management systems. What they found shows a distressing but pervasive problem at many organizations: Each list contained different, overlapping and confusing "views" of the customer base, and they could not create an accurate, inclusive list of customers that were affected by the loss of the distribution center.
Enterprise applications have promised a "single view of the customer" for years, but the proliferation of systems has led to more confusion -- at a data level -- about the customer base. Marketing and customer relations executives struggle to understand even the most basic questions: Who exactly are our customers? Which customers are we trying to target? Who are our best customers? Which customers represent our best opportunities?
Uncertainty about who customers are, much less about the value of a customer, can severely compromise efforts to strengthen relationships. And if customers don't feel valued, they will go to a competing vendor. Customers are hard to acquire, but even harder to keep.
Adding data quality and identification logic. Customer data integration is an emerging method to compile the most authentic customer information from all applications, databases and touch points into one centralized data source. By bringing the best data about customers to the surface, CDI strives to deliver the most consistent, accurate and reliable information, regardless of the originating application.
The benefit of CDI is that the data -- not the applications -- are the focus. Another way to think of it is that you are creating a reliable set of customer data to feed every internal application: the CRM application has access to the same details as the ERP system, and so on. By having each business unit view the same information about customers, companies can improve support and service across business functions.
But implementing CDI can be complex, especially for companies with dozens of sources of customer data. Early CDI solutions focused on building a master data set by pulling continuous data updates from various source applications. Organizations soon learned, however, that this method let bad data populate the customer master list -- hardly the expected and desired outcome.
Companies are turning to CDI solutions with two components: robust data quality capabilities coupled with sophisticated identity logic. These components let users improve the quality of data while also identifying and managing the same customer sets across sources and applications.
Data quality typically begins with an in-depth data profiling or discovery phase, during which organizations examine and catalog existing customer data sources. From there, companies build business rules to standardize and verify addresses and other attributes, reconcile conflicting information, validate names and addresses and add demographic data to augment the information.
The next phase is identity logic (aka identity management), a crucial component in any CDI effort. Identity logic determines whether customers listed in different sources are the same customer and intelligently integrates customer information from multiple applications and databases. With identity logic, companies can use specific data points to link customers across applications and sources and isolate the best data from those sources.
Let's say a company has records on James William Smith in different applications. The CRM system may list him as Jamie Smith while the call center refers to him as James W. Smith and the billing system lists him as J. William Smith. A CDI 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 lets the company aggregate all data about Mr. Smith and assign an accurate value to him as a customer.
CDI's impact. A telecommunications company recently began a CDI initiative to solve several challenges. Like most telecom companies, it had matured through a mix of organic growth, mergers and acquisitions. Over the years, it had accumulated dozens of systems containing customer information.
Using an approach centered on data quality and identity logic, the company set out to improve the accuracy and reliability of its customer data. Once it created a central master data reference for customers as described above, the company uncovered cross- and upsell opportunities across its business units because each group finally had a precise view of the customer.
For example, the company now could see which customers bought only one service (basic cable) and which had multiple services (cable and high-speed Internet). From the integrated data, it could make compelling offers (additional services, rebates, referral programs) to the latter group, helping expand -- and hopefully extend -- the relationship with higher-value customers.
But perhaps more importantly, the company did not need to scrap existing applications. Business units that once worked autonomously retained the same CRM or ERP system as before. The master reference file simply fed these existing intradepartmental applications.
Through consistent, accurate and reliable data, this company built a stronger foundation for sales, support and marketing. It developed healthier, more lasting relationships with customers and now markets more intelligently, efficiently and profitably to these customers.