Closing the CRM Loop
Tuesday, March 22, 2005
CDI Creates a Single Customer View Across an Organization
by David Newman
According to recent research by Meta Group, many CRM initiatives do not succeed because of inadequate data management and integration capabilities. The inability to create a single version of the truth about a customer compromises CRM project objectives, which span collaborative, operational, and analytical CRM business functions.
The Promise of CRM
Many organizations fail to achieve the expected ROI from CRM investments because of an inability to produce and share a single version of the truth about their customers. Often underestimated in enterprise-wide initiatives such as CRM are data integration, architecture, and ownership issues stemming from decades of silo-based processes, stovepiped applications, conflicting terms, patchwork interfaces, and incongruent business rules.
The goal of a panoramic, cross-functional view of a customer remains elusive as organizations struggle to integrate new channels and points of interaction (e.g., call centers, Web sites, e-mail systems, interactive kiosks) with marketing, sales, and service functions. The failure to create a consolidated view of the customer relationship affects basic project objectives, such as a real-time, closed-loop process bridging analytical, operational, and collaborative CRM functions. Accordingly, leading organizations improve their CRM implementation success through data management and infrastructure solutions known as “customer data integration” (CDI).
Solving the Data Integration Problem
Integration requires standardization and interoperability to achieve efficient processing and consistent cross-functional communication. Barriers to success include department-centric applications, often containing different versions of the same master data (e.g., cross-functional information common across all applications), because individual units maintain separate customer lists for local business processing.
During 2005/06, Meta Group predicts that these “dueling customer master files” will negatively affect CRM implementations, delaying the goal of a consistent and accurate 360-degree view of the customer relationship. Moreover, traditional integration methods (e.g., point-to-point interfaces) add cost and complexity as new business requirements (e.g., reorganization, new marketing campaigns, mergers, acquisitions) necessitate system changes, interface modifications, and data quality diagnostics to ensure accurate and rapid cross-functional transaction processing from upstream systems to downstream applications.
Accordingly, leading organizations solve customer data quality, redundancy, and integration issues using enterprise architecture techniques and approaches. Through 2006, these organizations will adopt CDI solutions to resolve data primacy and accuracy issues. Early adopters have successfully applied CDI solutions to overcome data integration challenges in their CRM implementations.
CDI delivers the required infrastructure to consolidate and augment client transactions into a single customer view, creating a panoramic view of a customer to leverage across CRM functions. For instance, CDI solutions produce merged and authoritative data to support analytical CRM applications (e.g., database marketing, predictive modeling, data mining, lifetime value, segmentation, campaign management, loyalty programs), which then generate customer insights for sharing across the CRM life-cycle process. CDI also supports collaborative and operational CRM objectives by monitoring events for context-based response.
Although some organizations may have investments in data warehousing, these downstream applications alone cannot satisfy closed-loop CRM objectives. Data warehouses are designed to generate retrospective analysis (e.g., “what happened” and “why”). Closed-loop CRM requires prospective and predictive analysis in real-time (e.g., “what is happening” and “make it happen”). By combining CDI solutions with downstream data warehousing, organizations can integrate analytics to produce a closed-loop CRM business intelligence environment.
ISTCL can help your organization to improve CRM implementation success by applying customer data integration infrastructure solutions to support closed-loop processing across analytical, operational, and collaborative CRM domains.
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