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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.
These analytics help improve customer service by finding small problems which can be solved, perhaps by marketing to different parts of a consumer audience differently. [20] For example, through the analysis of a customer base's buying behavior, a company might see that this customer base has not been buying a lot of products recently.
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. [1] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science ...
Customer intelligence (CI) as part of business intelligence is the process of gathering information regarding customers, and their details and activities, to build deeper and more effective customer relationships and improve decision-making by vendors.
The definition of an operational analytics processing engine (OPAP) [8] can be expressed in the form of the following six propositions: Complex queries: Support for queries like inner & outer joins, aggregations, sorting, relevance, etc. Low data latency: An update to any data record is visible in query results in under than a few seconds.
Campaign and customer analysis help craft the right offers and learn from information gathered from past campaigns. Campaign management oversees all communication with customers across multiple channels, tracks their responses, and reports results. Data warehousing pulls customer information together from different systems and channels.