Customer Relationship Management (CRM) is a relatively new term for what many with historical experience in the direct marketing industry have referred to for many years as database marketing, or "data based" marketing. Using what you know about the historical purchases, behavior, lifestyle changes, and other transactional items related to an existing customer or customer segment to drive business intelligence with the goal to generate new sales from both existing and new customers based upon what you have learned from those you already have.

Whether one refers to their ongoing customer and prospective customer marketing communication programs as CRM, ABC, or XYZ, the issue of data quality remains as the greatest challenge to face those charged with the ongoing successful operation of such data based intelligence. With the varying departments within an organization responsible for data collection, customer interaction, support, and future sales it comes as no surprise that the overall success of such a system depends upon ensuring the quality of each customer or potential customer interaction and data collection opportunity.

Good data quality practices are one of the most important factors determining the success of any CRM or data based marketing initiative. Without confidence in data quality reliability, senior management and sales representatives alike are hindered from making business decisions and meeting sales opportunities to drive increased sales, decreased costs, and overall improvement to shareholder value.

Successful data quality practices should include:

  • Establishing consistent data quality standards across the various sources from which data enters the enterprise and subsequently the CRM database
  • Eliminating errors in existing records to include duplicate or erroneous information in order to create a single customer view
  • Involvement of personnel from all company departments from senior management to those responsible for customer and potential customer interaction so that the needs to be met from the data warehouse can be evaluated

Problems with consistent data quality standards can occur in many areas, ranging from invalid mailing addresses to improperly formatted data, such as a professional title, company, and delivery information that omits a crucial data item which in turn creates inefficiencies, cost increases, and decreases in the life time value of the customer. Effective data quality procedures ensure that data is consistently applied to the various applications and data integration processes that are responsible for aggregating said information into a single customer view.

With solid data quality, the process of identifying and linking historical and future transactions that identify that two or more individuals belong to the same physical location, family, company, or branch location can be realized in order to successfully build valid aggregate views for both consumer households and business/corporate linked sites. This data linkage in turn enables actionable marketing strategies to be developed that might not otherwise be available based upon the capacity for targeted sales segmentation opportunities driven by the knowledge that two or more individuals belong to the same physical location, company or family name, and/or purchase pattern(s).

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