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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. Marketing provides services to satisfy customers.
Consumer behaviour analysis is the "use of behaviour principles, usually gained experimentally, to interpret human economic consumption." As a discipline, consumer behaviour stands at the intersection of economic psychology and marketing science.
Behavioral analysis focuses on understanding how consumers act and why, enabling predictions about how they are likely to act in the future. It enables marketers to make the right offers to consumer segments at the right time. Behavioral analytics can be useful for authentication as for security purposes. [1]
Customer or consumer profiles are the essences of the data that is collected alongside core data (name, address, company) and processed through customer analytics methods, essentially a type of profiling. The three basic methods of customer profiling are the psychographic approach, the consumer typology approach, and the consumer ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Marketing research is the systematic gathering, recording, and analysis of qualitative and quantitative data about issues relating to marketing products and services. The goal is to identify and assess how changing elements of the marketing mix impacts customer behavior.
The exploratory factor analysis begins without a theory or with a very tentative theory. It is a dimension reduction technique. It is useful in psychometrics, multivariate analysis of data and data analytics. Typically a k-dimensional correlation matrix or covariance matrix of variables is reduced to k X r factor pattern matrix where r < k.
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.