To stay competitive, retailers must understand not only current consumer behavior, but must also be able to predict future consumer behavior. This can help retailers reduce churn, improve sales, and improve customer loyalty. SQL Server 2012 offers predictive analysis through data mining, empowering users with actionable insight across the organization. SQL Server 2012 and Microsoft Office Excel offer data mining capabilities that can help retailers make informed decisions. Here is a story of how EB Games is winning repeat business through analysis of customer data with SQL Server:

Here are some tutorials that walk you through creating mining models for retail business scenarios:

·        Retail Data Mining Tutorial with SQL 2012: In this tutorial, you will complete a scenario for a targeted mailing campaign in which you create models for analyzing and predicting customer purchasing behavior and for targeting potential buyers. The tutorial demonstrates how to use three of the most important data mining algorithms, how to analyze your findings using the mining model viewers, create predictions and accuracy charts, using the data mining tools that are included in Microsoft SQL Server Analysis Services. The fictitious company, Adventure Works Cycles, is used for all examples.

·        Retail Tabular Modeling Tutorial: This tutorial is based on Adventure Works Cycles, a fictitious company. Adventure Works Cycles is a large, multinational manufacturing company that produces and distributes metal and composite bicycles to commercial markets in North America, Europe, and Asia. To better support the data analysis needs of sales and marketing teams and of senior management, you are tasked with creating a tabular model for users to analyze internet sales data in the AdventureWorksDW2012 sample database. The purpose of the lessons is to guide you through authoring a basic tabular model running in In-Memory mode by using many of the features included in SQL Server Data Tools.

·        Retail Multidimensional Modeling Tutorial: This tutorial describes how to use SQL Server Data Tools to develop and deploy an Analysis Services project, using the fictitious company Adventure Works Cycles for all examples. The tutorial also covers how to define calculations, Key Performance Indicators (KPIs), actions, perspectives, translations, and security roles within a cube.

·        Forecasting & Market Basket Analysis Tutorial: This tutorial introduces several new scenarios, including common business requirements such as forecasting and market basket analysis. You will learn how to create a time series model, an association model, and a sequence clustering model. Finally, you will learn how to use neural network to explore correlations in data and to use logistic regression for predictions.

·        Predict whether a customer will purchase a product using Data Mining Models: In this tutorial, you will learn how create, train, and explore mining models by using the Data Mining Extensions (DMX) query language. You will then use these mining models to create predictions that determine whether a customer will purchase a bicycle.

·        Using Data Mining Models for Market Basket Analysis: In this tutorial, you will learn how to create, train, and explore mining models by using the Data Mining Extensions (DMX) query language. You will then use these mining models to create predictions that describe which products tend to be purchased at the same time.

·        Sales Forecasting using Data Mining Models: In this tutorial, you will learn how to create a time series mining structure, create three custom time series mining models, and then make predictions by using those models. The Microsoft Time Series algorithm creates models that can be used for prediction of time-related data. Data Mining Extensions (DMX) is a query language provided by Analysis Services that you can use to create mining models and prediction queries.

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