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In executing a business strategy, poor data quality results from two main sources. These include the use of flawed performance indicators and incoherent analysis of business operations and performance metrics.
In many businesses, this problem is exacerbated by shifts into business management systems that synchronise business and computer-based systems. This approach increases the risks posed by poor data quality on business outcomes.
Poor data quality presents challenges that adversely affect the provision of quality customer service. Access to high-quality data is necessary for two main reasons. First, high-quality data facilitates the operation and maintenance of analytical Customer Relationship Management (CRM) systems. Second, it improves the quality of customer service.
The success of a CRM system depends on the quality and reliability of customer data. Quality customer data is obtained by resolving key issues outlined below.
- What channels are available to businesses for generation of reliable and high-quality customer information that is easy to apply?
- How can various customer experiences be synchronized in enterprises that use multiple channels?
- How can businesses develop their analytical systems in order to use customer feedback to improve their efficiency and profitability?
- How can customer feedback be integrated into business systems in order to improve customer service?
In-depth analysis is vital in identifying the most effective operational sources that can be used to generate customer data for use in developing better systems. Such analysis involves resolution of several questions.
- Which are the most reliable sources of customer data?
- In what format is the data stored?
- Is the data available in any other form?
- Can duplicate data improve the value of the enterprise?
- Which data pool is the most consistent?
Businesses use diverse databases. Therefore, it is imperative for system operators to possess special skills and extensive understanding of technology for effective operation of varied database management systems. The most preferred data architecture applied in many enterprises is the model integrated into the CRM suite vendor.
Despite its wide application, the model presents several challenges that affect the efficiency of chosen management systems. The model replaces the existing database with a new one that has a different data model. In addition, it clouds the new system with innumerable data pools.
The model is beneficial because it facilitates the integration of the new system with existing systems for rapid data transfer. Another challenge that comes along with this model is the risk of data duplication.
In many cases, the new system duplicates certain data elements that are contained in the existing system. Data duplication complicates the decision-making process.
In order to make the CRM data architecture the default system in an enterprise, it is necessary to map the applications of a new system into existing databases in order to synchronise their functions. This approach is expensive because it requires many resources.
Therefore, it is mainly applied by large enterprises. An alternative approach is to integrate existing databases with newly developed operational data stores and CRM applications. Companies discover and master their CRM capabilities by finding and executing projects from clients through brainstorming sessions.
These sessions aim to understand customer insights, build data pools, integrate new technologies into business operations, and increase employee performance through training. Identification of projects that align with an enterprise’s CRM capabilities reveals the technical and financial requirements of chosen projects.
After project identification, the company focuses on finding the right people, business processes and technologies to execute the projects. Comprehension of customer profitability is based on the proper integration of products, sales staff, data channels, and customer insights into the management system.
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