I am often asked what trends I see for data warehousing and CRM. While there are quite a number of them, in this first column of 2001, I list a few that will undoubtedly receive serious attention this year the ones related to preparing and expanding data warehouses to support CRM. If your company is following a strategy of competitive advantage through customer relationships, you will want to be sure your CRM-ready data warehouse is enabled to support all activities.
A CRM-ready data warehouse is an architecture for data delivery in support of the strategy of customer intimacy the most effective way of competing in business today. It enables the management of improved overall customer satisfaction and the ability to segment customers and treat them as individuals rather than as part of a collective group. We've all heard that it costs 10 times as much to acquire a new customer as it does to keep an existing customer, but it is not good enough to stop there. It's going to cost 100 times more to reacquire a customer similar to your best customer, and it will be cheaper to let some existing customers go than to keep them. The bridge to making this work is customer segmentation supported by a CRM-Ready data warehouse.
Incorporation of external data. If you can conceive of a need for external data, chances are the data is available or derivable from available data. Some data warehouses are comprised of mostly external data as consortiums that clear data continue to gain prominence in some industries. The high-value component of much of this "reverse-appended" data is its ability to generate effective cross-sell and up-sell possibilities as well as lists of prospects with characteristics similar to your best customers.
Realization of the "architecture" nature of the data warehouse. The CRM-ready data warehouse is not a singular database. It is many databases playing roles within an integrated architecture that is multipurpose, flexible and cross-functional in nature.
Unpredictable, varied and growing access patterns. There is no "one size fits all" when it comes to access tools. Most best practice programs have three to six different access tools in the hands of a varied user community. The tools span various access categories such as relational OLAP, hybrid OLAP, multidimensional OLAP, push, desktop OLAP, reporting, data mining, data visualization and portals. Access patterns have risen largely due to the need for custom, not mass, approaches to the marketplace. Survey.com predicts that the average number of users of a data warehouse will be 2,718 by 2002. Much of this usage will be from suppliers, partners, customers and employees not just the traditional knowledge workers.
E-intelligence analytics. Web-houses that take you beyond site traffic statistics and drive strategic directives of customer profitability, product profitability, customer satisfaction and return on investment support the highest value-add of e-business to a company.
Packaged solutions. Some packaged solutions are dangerous if not supported by business process; but, nonetheless, incorporating some measure of packaged approaches is worth consideration for new efforts. Time to market is critical for CRM efforts. Information technology (IT) must become, practically speaking, an internal systems integrator, integrating heterogeneous system and software components from software development houses. IT must primarily be responsive to business need. In addition to technology skills in their core area, skills required by all include customer service, requirements gathering, information collection and planning. Understanding of and responsiveness to business needs are paramount in today's environment. Integration of best-of-breed purchased components contributes to that goal.
Analytical calculations for derived dynamic data. Customer lifetime value, promotion response, customer spend percentile, customer profitability and customer category spending are just a few of the dynamic metrics that can be proactively and dynamically added to a data warehouse. The interesting part is the ability to track customer patterns over time.
Data quality issues abound requiring custom solutions. Since the beginning of data warehousing, data quality has been the number one risk to data warehousing efforts. This remains true today. Many overestimate the quality of data in their operational systems. Often these systems data must be "cleaned up" prior to feeding the warehouse with their data. Clean-up processes often require specific company knowledge to ensure the data is properly represented.
Financial payback for CRM data warehouse efforts. Whether going for complete ROI or stopping short by targeting intermediate factors such as customer satisfaction, number of customers or promotion response, it is important to have tangible goals for a CRM-ready data warehouse program.
Our challenge this year is to rise above the tsunami of shortcut and siloed approaches to data warehousing and establish a long-term program architecture comprising all of these trends. The challenge is to recognize and drive personalization opportunities everywhere possible and ensure that the infrastructure and support services are satisfying the ever-increasing demands for our CRM-ready data warehouses.
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