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 in order to satisfy customers. With that in mind, the productive system is considered from its beginning at the production level, to the end of the cycle at the consumer. Customer analytics plays an important role in the prediction of customer behavior.
RetailAlthough until recently over 90% of retailers had limited visibility on their customers, with increasing investments in loyalty programs, customer tracking solutions and market research, this industry started increasing use of customer analytics in decisions ranging from product, promotion, price and distribution management. The most obvious use of customer analytics in retail today is the development of personalized communications and offers and/or different marketing programs by segment. Additional reasons set forth by Bain & Co. include: prioritizing product development efforts, designing distribution strategies and determining product pricing. Demographic, lifestyle, preference, loyalty data, behavior, shopper value and predictive behavior data points are key to the success of customer analytics.
Retail managementCompanies can use data about customers to restructure retail management. This restructuring using data often occurs in dynamic scheduling and worker evaluations. Through dynamic scheduling, companies optimize staffing through predictive scheduling software based on predictive customer traffic. Worker schedules can be adjusted in response to updated forecasts at short notice. Customer analytics allows retail companies to evaluate workers by comparing daily sales to daily traffic in a store. The use of customer analytics data affecting the management of retail workers in a phenomenon known as refractive surveillance.
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Train stations have increasingly become crowded, necessitating stringent requirements in the design of stations and commuter navigation through these stations. In this study, we explored the use of mobile eye tracking in combination with observation and a ...
2023
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We present the La Mobiliere insurance customers dataset: a 12-year-long longitudinal collection of data on policies of customers of the Swiss insurance company La Mobiliere. To preserve the privacy of La Mobiliere customers, we propose the data aggregated ...
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