Marketing Mix Optimization
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This document discusses marketing mix optimization and the key factors that drive sales. It outlines the 5 P's of marketing - product, place, price, promotion, and people - as well as external factors that affect sales. It then describes a marketing mix optimization process that involves understanding the market and data, collecting data, visualizing insights, and re-allocating budgets to find an optimal allocation that can increase sales. The process involves consolidating data, describing sales patterns, preparing models, and fully optimizing the marketing mix where every step is important.
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This document provides an overview of using SAS Enterprise Miner software to conduct predictive modeling using regression analysis. It discusses how to import data from Excel, create a project and data source in Enterprise Miner, run linear and logistic regression models to predict outcomes, and interpret the results, including measures of model fit and variable effects. Examples are provided on using linear regression to predict kilowatts from temperature, multiple regression to predict food expenditures from income and family size, and logistic regression to predict exam passing from study hours.
Rで実験計画法 後編
This slide was presented in Tokyo.R #22.It describes about design of experiment with R. DoE.base package is useful for software testing because it makes easier for us to make orthogonal array.
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- 4. YES! But there ain’t no data…
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