Key FeaturesStudy new techniques for marketing analyticsExplore uses of machine learning to power your marketing analysesWork through each stage of data analytics with the help of multiple examples and exercises
Book Description
Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.
The book starts by teaching you how to use Python libraries, such as pandas and MatDescriptionlib, to read data from Python, manipulate it, and create Descriptions, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.
By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.
What you will learnAnalyze and visualize data in Python using pandas and MatDescriptionlibStudy clustering techniques, such as hierarchical and k-means clusteringCreate customer segments based on manipulated dataPredict customer lifetime value using linear regressionUse classification algorithms to understand customer choiceOptimize classification algorithms to extract maximal information
Who this book is for
Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.
Table of ContentsData Preparation and CleaningData Exploration and VisualizationUnsupervised Learning: Customer SegmentationChoosing the Best Segmentation ApproachPredicting Customer Revenue Using Linear RegressionOther Regression Techniques and Tools for EvaluationSupervised Learning: Predicting Customer ChurnFine-Tuning Classification AlgorithmsModeling Customer Choice