Data Quality in Customer Relationship Management (CRM) Failure

CRM Failure: Data Quality
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Data Quality in CRM Failure
Data quality refers to the high-quality storage of data from the consumers via a series of transactions, and these provide information about their individual and group characteristics. It is significant in analyzing consumer data and serves as the basis for the success of CRM interventions. Unresolved issues in data operation lead to insufficient information, which affects the analysis of consumer behavior. In turn, it prevents the customer relations manager from providing strategies that satisfy the consumers via efficient and effective decision-making (Petrović, 2020).
The types of data include descriptive, behavioral, and contextual. Descriptive data emphasize the consumer, which can be an individual or collective. These involve the characteristics, lifestyle, and demographics of the consumer. This is important in maintaining a competitive advantage by improving CRM. Behavioral data pertain to the two-way communication between the consumer and provider. These define the relationship and link the strength and weaknesses of one to the other. These help CRM by customizing an enterprise. Last, contextual data can be used to foster a strong relationship with the conusmers. However, these do not have a framework, making it diverse. Problems with any of these data can lead to CRM failure (Boban et al., 2011).
CRM failure secondary to data quality ensues due to the challenging collection and storage of customer information. The data are heterogeneous and can be encoded in infinite formats. Therefore, many ways can affect CRM. These include unmatching input and storage with various defects in data, decentralized data storage, and insufficient assimilation of data sources and (Petrović, 2020).
First, inconsistencies in the entries in the input and storage promote redundancy and a disorderly system. Redundancy rep...