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Customer analytics is a process by which data from customer behavior is used to help make key business decisions via market segmentation and predictive analytics. This information is used by businesses for direct marketing , site selection , and customer relationship management .
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
The role of analytical CRM systems is to analyze customer data collected through multiple sources and present it so that business managers can make more informed decisions. [24] Analytical CRM systems use techniques such as data mining, correlation, and pattern recognition to analyze customer data.
Customer data management (CDM) is the ways in which businesses keep track of their customer information and survey their customer base in order to obtain feedback. CDM includes a range of software or cloud computing applications designed to give large organizations rapid and efficient access to customer data .
The delta model can be illustrated using the strategic triangle (see fig.1). There are three points: system lock-in, best customer solutions and best product. [8] System lock- in enables market dominance and can achieve complementor share, it focuses on the entire system economics and instead of product-centered economics, which makes it very sustainable. [9]
There are 2 main categories of data analysis tools, data mining tools and data profiling tools. Also, most commercial data analysis tools are used by organizations for extracting, transforming and loading ETL for data warehouses in a manner that ensures no element is left out during the process (Turban et al., 2008).
Affinity analysis falls under the umbrella term of data mining which uncovers meaningful correlations between different entities according to their co-occurrence in a data set. In almost all systems and processes, the application of affinity analysis can extract significant knowledge about the unexpected trends [ citation needed ] .
Recency = 10 – the number of months that have passed since the customer last purchased [2] Frequency = the maximum of "the number of purchases by the customer in the last 12 months (with a limit of 10)" and 1; Monetary = the highest value of all purchases by the customer expressed in relation to some benchmark value