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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.
Data collection can be done by observing customer behavior through in-situ studies or by processing e.g. log files, by interviewing customers, potential customers, stakeholders, or a sample of the general population. The data can be quantitative in nature (counting sales, clicks, eye-tracking) or qualitative (surveys, questionnaires, interviews ...
Customer data or consumer data refers to all personal, behavioural, and demographic data that is collected by marketing companies and departments from their customer base. [1] To some extent, data collection from customers intrudes into customer privacy , the exact limits to the type and amount of data collected need to be regulated.
This sample of the firm's customers must be carefully designed and drawn if the results of the study are to be considered representative of the customer population as a whole. In most cases, the results of quantitative studies are based upon the responses of a relatively large number of interviews.
1.Internal Reports System: It records various data from different department of a company, which is regarded as a major source of information. 2.Marketing Intelligence System: It is a main source used by managers for gaining daily information of the external environment, hence assists the managers to react to the rapidly changing environment.
Consider a database of sales, perhaps from a store chain, classified by date, store and product. The image of the schema to the right is a star schema version of the sample schema provided in the snowflake schema article. Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product.
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A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.