The first part of the article appeared in the March, 1999 issue of DM Review, entitled "The Market Intelligent Enterprise."
The Market Intelligent Enterprise begins with a customer-centric vision, creates a strategy centered on that vision and builds processes and systems to support and implement that strategy throughout the organization. The organization develops new core competencies to integrate customer information throughout the value chain by managing internally and externally all touch points with prospects, customers, suppliers and business partners. New performance measures and customer satisfaction indices guide continuous improvement and strategy.
To support these customer-oriented strategies, we have seen many global organizations increase their strategic investment in sales productivity tools, advanced call center technology and relationship marketing solutions from vendors such as Siebel, Vantive, Clarify, Exchange Applications, Epiphany, Prime Response and Recognition Systems, to name just a few. While companies have gotten tremendous value from these investments, as they have with their customer data warehouses, many of these companies have implemented these applications in a standalone fashion. They have then developed interfaces to pass information from application to application as required, usually "in batch" on a daily, weekly or monthly basis.
The Market Intelligent Enterprise must also rethink the data and technical architecture that enable its processes. An integrated architecture supports its defined characteristics by providing an enterprise-wide customer data repository that gives a single view "of" the customer and "to" the customer. Information from sources within and outside the organization flows into the organization's data repository. Sources include customer and prospect information from transactions, the sales force, call centers, sales promotions, survey data, store-front interactions, the Internet, kiosks, demographic information, service bureaus, database marketing companies, other business partners and even motor vehicle registries.
Information gained about a customer during transactions, above and beyond pure operational data, also flows into the data warehouse, providing additional data for analysis and customer history that can be used to increase the effectiveness of transactions. This data can include the answers to survey-style questions asked of the customer, the results of cross-selling and up-selling attempts, the content of the interaction, etc. Finally, house-holding schemes are used to identify relationships between customers. That way, a moderately profitable customer can be recognized as related to a very profitable customer, allowing the organization to avoid indirectly alienating one of their best customers.
The enterprise will also make effective strategic and tactical use of analytical applications that turn customer data into knowledge. The integrated customer data architecture facilitates the use of data mining techniques such as clustering, induction, neural networks, linear and logistic regression, association and time sequencing. Used in tandem, data warehousing and data mining technologies support various knowledge discovery applications. These include credit scoring, cross-selling and up-selling, fraud detection and compliance, potential lifetime customer value, market demand forecasting, customer retention/vulnerability, product affinity analysis, price optimization, risk management and target market segmentation.
Enhancing the process even further, sales and marketing applications will operationalize the knowledge discovered through customer data warehouses and analytic applications. Marketing campaign management programs leverage the data warehouse and data mining results. These applications also assist with improving marketing cycle times and coordinating marketing programs enterprise-wide and across delivery channels.
Finally, customer touch-point applications must use the knowledge acquired during one-to-one customer transactions coordinated across channels. It all comes together at the front lines interacting with customers, either via the Internet, call center, sales force, store/branch, mail or ATM/kiosk. Customer touch-point technology can be deployed to support not only the operational needs, but also the strategy of the organization. The data warehousing/data mining combination not only ensures each touch-point has access to the same information, but it also provides a foundation for evolving advanced sales and marketing applications.
Many technology vendors are extending the functionality of their customer-focused applications to minimize the number of standalone applications an organization needs to integrate on the way to a seamless architecture. But even today, becoming a Market Intelligent Enterprise does not have to bring the existing sales and marketing machine to a halt. Companies should begin to integrate these existing applications through the customer data warehouse. Thus, the information contained in the warehouse will be the key enabler allowing companies to maximize the value of existing technology investments. At the same time, the customer data warehouse will be the critical component for achieving your company's customer relationship management strategy on your journey to become a Market Intelligent Enterprise.
Register or login for access to this item and much more
All Information Management content is archived after seven days.
Community members receive:
- All recent and archived articles
- Conference offers and updates
- A full menu of enewsletter options
- Web seminars, white papers, ebooks
Already have an account? Log In
Don't have an account? Register for Free Unlimited Access