Every day CRM users and administrators experience negative effects from a lack of data quality and the latest research identifies that a bad level of data is one of the biggest problems for customer management concepts. There are many reasons for this and it can be extremely difficult to achieve a completely faultless database. We therefore recommend making use of all potential systems that will help to prevent bad data quality and to improve your existing database.
 
Data Quality
Scientific results demonstrate that successful CRM management is positively linked to a good data quality. High quality data is characterized by the fact that all information stored in the CRM system is up-to-date, relevant, correct, complete and easily available. Every single user in a company has to take care of this. But this alone is generally insufficient.
 
Reasons for bad quality data
Even a well maintained customer database usually contains approx. 5% of duplicate addresses. They result from typing errors, different address formats, variations in name content or company relocations. If customer information is generated by the purchase of external addresses, there is often no de-duplication or file matching process taking place when the new information is merged with your current database. This results in new records being created for existing customers.
The reason that employees add incomplete or incorrect information could be because they have a lack of time, insufficient motivation or that they simply don’t understand the relevance of the data. In other cases, it is because the customer is not willing to give the required information, or even that some people abuse web forms by entering erroneous information.

Effects of lacking Data Quality
The result of a survey of Marketing Management (source: sas) points out the problem: 67 percent of the respondents said that, in their company, customer satisfaction suffers due to bad data quality which, for this reason, affects the entire CRM strategy. Besides complaints, the loss of image and customer desertion, a significant increase in costs occurs. If a mailing list with 6,000 contact addresses contains 5 percent (i.e. 300 addresses) duplicate records the company would have saved at least 150 letters including the postal charges.
Also, an additional problem is that the reliability of the analyzing tools gets lost with worsening data quality and badly influences the measurements of success in marketing, sales and service. Therefore, effective direct marketing, without the correct results of cross selling analyses and qualified marketing control, is not possible. This results in insufficient support of the operative business processes, e.g. identifying the addressing of incorrect target groups.
 
Data Quality Assurance Measurements
Maintaining top-quality customer contacts needs a mix of pro-active measurements such as future error avoidance, reactive identification and error correction. To identify the causes of bad data, the whole process and all its connected activities, from the creation of data to its use, need to be examined.

• Definition of data quality requirements within your organization
• Improvement of user satisfaction – regular user surveys and training
• Introduction of data entry protocol to ensure correct input into the CRM system
• Redefine workflow processes
• IT- supported data cleansing – eliminating duplicates, file matching to existing data, merging data from different data sources, e.g. external address lists
• Resources to support your employee, e.g. Card Scanner for CRM