Learn new and more sophisticated tools that reduce your marketing analytics efforts and give you more precise results.Key FeaturesStudy new techniques for approaching marketing analyticsExplore uses of machine learning to power your marketing analysesWalk through all stages of data analytics with multiple examples and exercisesBook DescriptionMarketing Analytics with Python, shows you all stages of data analytics - going from a raw data set to segmenting a population, to modeling different parts of the population based on their segments.You’ll begin by studying how to use Python libraries, such as Pandas and Matplotlib to read data from Python, manipulate it, and create plots, using both categorical and continuous variables, as well as time series data. Then, you’ll learn how to segment a population into different groups and use different clustering techniques to evaluate the customer segmentation. You’ll also explore ways to evaluate and select the best segmentation approach. Then, you’ll go on to create a linear regression model on customer value data to predict lifetime value. You’ll learn 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, such as modeling customer choice of product.What you will learnAnalyze and visualize data in Python using Pandas and MatplotlibStudy clustering techniques, such as Create customer segments based on the data you’ve manipulatedPredict customer lifetime value using linear regressionUse classification algorithms to understand customer choiceOptimize classification algorithms to extract maximum information Who This Book Is ForMarketing Analytics with Python is designed for developers and marketing analysts looking to bring new and more sophisticated tools to their marketing analytics efforts. It'll help you to 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 is not necessary.About the AuthorTommy Blanchard earned his PhD from the University of Rochester, did his postdoctoral training at Harvard. He now leads the Data Science team at Fresenius Medical Care North America. His team performs advanced analytics and creates predictive models to solve a wide variety of problems across the company.Pranshu Bhatnagar works as a Data Scientist in the telematics, insurance and mobile software space. He has previously worked as a Quantitative Analyst in the FinTech industry and often writes about Algorithms, Time Series Analysis in Python and similar topics. He graduated with honours from Chennai Mathematical Institute with a degree in Mathematics and Computer Science and has done certification courses in Machine Learning and Artificial Intelligence from International Institute of Information Technology, Hyderabad. He is based out of Bangalore, India.Debasish Behera works as a Data Scientist for a large Japanese Corporate Bank where he applies machine learning/AI for solving complex problems. He has worked on multiple use cases involving AML, predictive analytics, customer segmentation, chat bots, and natural language processing. He currently lives in Singapore and holds a master’s in business analytics (MITB) from Singapore Management University.
Author: Tommy Blanchard