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Does Your CRM System Contain Quality Data or Just Data?
Organizations can ensure that their CRM systems and the people using them can fulfill their potential.
Posted Feb 1, 2006
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Properly implemented and maintained CRM systems enable operational efficiencies and deliver superior analytical decision-making and customer service capabilities. People tend to focus on the functionality that a computer system can deliver, but a CRM system is really only as good as the data it contains. If users don't trust the answers that they get from the data, then they will stop asking the questions that will improve the organization's customer service and marketing efforts. When this happens, the CRM tool essentially becomes useless. As many industries have already learned, an automated CRM system must be built on a solid foundation of standards and quality control practices or it will not deliver the promised benefits. Despite this understanding, improving the quality of internal data is a challenge for many organizations. The most common database challenges include data accuracy, data completeness, compliance with company policy or law, standardization of data across sales and marketing teams, and timeliness of data updates. For many, the root of these data quality issues lies in two areas:
  • Organizations can't manage what they aren't able to or don't measure
  • Data quality is a continual process, not a technology The Principles of Data Quality To improve data quality, organizations must start by establishing the metrics necessary to measure the quality of the data currently housed in their database. These metrics will, by definition, be different in every organization--for instance, an accurate email address may be a requirement for an online store, but not so important for storefronts. Once the metrics that will be used are determined, organizations can then begin measuring the quality of their data and, subsequently, develop the business goals and strategies that will enable them to improve data quality going forward. For example, an organization might establish the goal that 95 percent of the individuals in the database must have a valid telephone number. Having determined the metric and the goal, it can then put processes in place to improve the quality of the data, and then measure the data against the metric once more.
    This brings us to the second principle: Data quality is a continuous business process and discipline, not an event or a technology. While technology is certainly a part of the solution and having the right technologies in place is critical to the final outcome, it is not the sole means by which the quality of data can be improved. Real improvement in data quality requires business leadership, a stated goal, a strategy to achieve the goal, and tactics for daily repetition to move the metrics towards the stated goal. All users must buy into and assist in improving the data in the system, as there will always be a way to beat the system (How many times have you seen 999 999 9999 entered into a phone number field?). Users must be trained and motivated to enter the best available data into the system. In the above example, the business process requires that a phone number be entered for every individual. The CRM user interface ensure users enter a telephone number before the record can be saved, and it even checks that the number is in the format of a phone number (999 999 9999) and that the area code matches the zip code of the address (which is often unreliable for cell phones and VoIP phones). However, the rule requires entry of a valid phone number, not a number that simply follows a 3, 3, 4 digit pattern. Having determined that the organization requires a valid phone number on 95 percent of records, the team may decide that the best way to improve this metric is to validate telephone numbers against a third-party database. An online and/or daily process can be implemented that validates numbers against a third-party database. The phone number metric can then be rechecked on a regular basis to ensure that the measure has climbed above, and stayed above, 95 percent. A Data Quality Strategy As previously mentioned, data quality is a recurrent process that, combined with the use of technology, enables an organization to improve the quality of its data and ultimately customer service. The stages of data quality are: 1. Measurement 2. Standardization 3. Internal quality 4. Augmentation 5. Monitor and measure again By integrating all points of customer interaction, a CRM system provides the organization with real-time views of customer information. However, if the customer data is inaccurate or incomplete, it impacts how the organization is able to interact with its customers, including its sales and marketing efforts. For example, if the marketing team targets a mailing to Thomas Smith, and in response he places an order by phone or online as Tom Smith, the organization may not understand that these are the same person. This means that the marketing team does not realize that its direct mail piece was effective in getting new business, and the organization has failed to understand the complete relationship it has with Thomas Smith. This is how the five stages of data quality would work with this data: 1. Measurement: the number of correct names in the database is measured. 2. Standardization--common nicknames are corrected, and terminology applied. In this case it recognizes Tom as a nickname for Thomas. 3. In the internal quality process the newly standardized names are merged together into one record, and their relationship histories are also merged. 4. The data is then augmented from publicly available databases, such as the American Medical Association's database of all physicians in the United States. 5. The measurements are run every day, and reviewed regularly against the goal. Surprisingly, many CRM providers see the problem of data quality as their customers' dilemma, and do not provide a full suite of tools and services to help their customers improve their asset. To maintain their competitive advantage, organizations need solutions to measure, standardize, improve, augment, and monitor the informational assets contained in their databases. Companies that are serious about data quality are turning to CRM providers offering data quality services designed to help customers get the most out of their sales and marketing investments, meet their business objectives and achieve bottom line results. Data Quality Services Organizations seeking data quality services should turn to CRM providers that offer a combination of software and services, can take them through the stages of data quality listed above, and provide data validation through third-party data sources and standardization and quality improvement. By standardizing and cleansing data on a continual basis, organizations are better able to compare and match data, which can lead to improved customer service efforts. Now, addresses can be instantly verified to ensure orders are correctly shipped, and callers can be identified and routed to the appropriate customer service representative faster. These practices can also reduce costs and even fraud. The biggest benefits to organizations come from having quality data that is unique to their organization. Since every organization is in some way unique, each must develop its own data quality metrics and goals. Organizations should work in concert with their CRM provider and allocate adequate resources to ensure their system remains in a validated state. By understanding and fully utilizing the principles and stages of data quality every day, organizations can stay focused on and further their core business through the use of a validated CRM system. Sam Barclay is vice president of business development at StayinFront. For more information, please visit www.stayinfront.com
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