One of the hottest areas of growth, noticed lately is in the area of customer relationship management (CRM) applications and ultimately the need for CRM Databases to handle the large amount of data. Increasingly, organizations both large and small are building CRM applications. In some cases, these CRM Databases are being built using sales force automation (SFA) packages, while in others they are based on data warehouse technology.
Sophisticated organizations are blending both approaches: using SFA packages to manage the sales cycle, and then moving the data into decision-support CRM Databases for pre- and post-sales marketing and long-term customer management. They are also consolidating this sales-related data with other data, such as customer service, billing and receivables, and external demographic or market research data, to build a richer customer profile using CRM Databases.
If you build CRM Databases, it can yield major benefits for your company. There are a number of important issues that you will need to address when building CRM Databases.
The first is data quality in CRM Databases. Validation and verification is essential for CRM Databases; otherwise, you take the risk of creating multiple records for the same individual or identifying two people as the same individual. In either case, you can lose money and alienate your customer by, for example, sending multiple copies of a mailing to a single individual. This why you need to pay close attention to the problems associated with customer identifiers that are created by uncoordinated applications. For example, a health-care institution has to be sure that each patient is identified by a unique medical record number that is used by all applications throughout the institution, such as admitting, billing, laboratory, radiology, pharmacy, and so on. So the requirement of fool-proof CRM Databases with advanced capabilities.
Names and addresses require particular attention. Names, which are unique to an individual, should be parsed and normalized before they are entered into CRM Databases. Parsing is the process of decomposing the name into its elements, such as first name, last name, surname, prefix (Dr., Rev., Mr.), suffix (M.D., Ph.D., Jr., III, Esq.) and relationships (Trustee for …, and so on). Normalizing refers to reassembling the name into a consistent format in CRM Databases. Addresses are somewhat easier to normalize and validate than names, since they have a more consistent structure and can be verified using specialized CRM Databases. It’s relatively easy, for example, to validate a ZIP+4 field against a table, available from the postal service, of all ZIP codes in the U.S.
Another issue to consider in CRM Databases is the rich, relatively unstructured set of data you’ll want to collect on customers. Items such as support calls, demographic data and customer inquiries are all grist for the CRM Databases, but these tend to be unstructured, text-based data elements that can be difficult to query and analyze in a decision-support environment. You’ll need to carefully consider the kind of data your users will require, and how to structure the data in a way that will be meaningful and useful to them.
Once you’ve got the data in CRM Databases, the next thing you need to consider is how to maximize its value. First, and foremost, is to put the data into the hands of the people who can best use it. Every customer touch point, such as customer service or telemarketing reps, should have easy, quick access to this data.
This kind of data is essential for determining customer lifetime value in every CRM Databases. This process helps to identify the good customers who should be wooed and cultivated from those who consume products and services but are unprofitable and should be gently steered in the direction of your competition. Profitable customers can be provided with additional benefits, services and special premiums or offers that will entice them to remain good customers. Non-profitable customers can be managed differently, through additional fees or reduced services. These techniques can have the effect of either making these customers profitable or encouraging them to take their business elsewhere.