Advanced Customer Analytics: Targeting, Valuing,

Advanced Customer Analytics: Targeting, Valuing, Segmenting and Loyalty Techniques Providing a clear guide to the specific analytical challenges faced by the retail sector, Advanced Customer Analytics covers the nature and scale of data obtained in transactions, relative proximity to the consumer, and the need to monitor customer behavior across multiple channels The book advocates a category management approach, taking into account the need to understand the consumer mindset through elasticity modeling and discount strategies, as well as targeted marketing and loyalty design It also covers regression and factor analysis using ordinary regression to estimate demand and value marcomm using poisson regression to explain number of transactions or number of purchases using survival analysis to determine when customers will buy general segmentation algorithms using survival and tobit analysis to determine customer lifetime value using structural equation modeling to determine customer loyalty using perceptual mapping to find out how customers view brands Author Mike Grigsby addresses the complexities of customer analytics and offers conceptual support to steer retail marketers towards making the right choices for data analysis [EPUB] ✵ Muerte en Hamburgo (Jan Fabel, Author Craig Russell – 9facts.co.uk Advanced Customer Analytics covers the nature and scale of data obtained in transactions ❮Download❯ ➵ Jazz Age Stories Author F. Scott Fitzgerald – 9facts.co.uk relative proximity to the consumer [Read] ➮ Much Obliged, Jeeves Author P.G. Wodehouse – 9facts.co.uk and the need to monitor customer behavior across multiple channels The book advocates a category management approach [KINDLE] ❅ The Wrong Blood By Manuel de Lope – 9facts.co.uk taking into account the need to understand the consumer mindset through elasticity modeling and discount strategies ❰PDF❯ ✓ The Customer-Funded Business Author John W. Mullins – 9facts.co.uk as well as targeted marketing and loyalty design It also covers regression and factor analysis using ordinary regression to estimate demand and value marcomm using poisson regression to explain number of transactions or number of purchases using survival analysis to determine when customers will buy general segmentation algorithms using survival and tobit analysis to determine customer lifetime value using structural equation modeling to determine customer loyalty using perceptual mapping to find out how customers view brands Author Mike Grigsby addresses the complexities of customer analytics and offers conceptual support to steer retail marketers towards making the right choices for data analysis

Leave a Reply

Your email address will not be published. Required fields are marked *