Marketing Analytics Using Excel
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Marketing Analytics
Author: Wayne L. Winston
language: en
Publisher: John Wiley & Sons
Release Date: 2014-01-13
Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results. Practical exercises in each chapter help you apply and reinforce techniques as you learn. Shows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools Reveals how to target and retain profitable customers and avoid high-risk customers Helps you forecast sales and improve response rates for marketing campaigns Explores how to optimize price points for products and services, optimize store layouts, and improve online advertising Covers social media, viral marketing, and how to exploit both effectively Improve your marketing results with Microsoft Excel and the invaluable techniques and ideas in Marketing Analytics: Data-Driven Techniques with Microsoft Excel.
Marketing Analytics Using Excel
Author: Rahul Pratap Singh Kaurav
language: en
Publisher: SAGE Publications Limited
Release Date: 2025-03-15
Marketing Analytics Using Excel is the essential introduction to data-driven marketing, which simplifies complex concepts and offers practical, real-world applications. This comprehensive yet accessible guide encourages an in-depth understanding of marketing analytics, from fundamental topics and basic Excel functions to more advanced topics such as AI and predictive analytics. Packed with practical examples and easy-to-follow, fully worked problems which demonstrate how theoretical concepts are applied in real-world situations, this book also includes: • Industry case studies from leading companies like Zappos, Amazon, Netflix, and Spotify, providing insights into how marketing analytics is applied in various industries. • Exercises, activities and discussion questions to reinforce learning. • A focus on open access tools and career prospects which encourages readers to develop further. This no-nonsense guide minimises the intimidation factor of complex formulas and instead focuses on practical, real-world applications, making it essential reading for Marketing students and anyone looking to upskill. Dr Rahul Pratap Singh Kaurav is Associate Professor at FORE School of Management, New Delhi, India. Dr Asha Thomas is an Assistant Professor at Wroclaw University of Science and Technology (WUST), Poland.
Data Science for Marketing Analytics
Author: Tommy Blanchard
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-03-30
Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results 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 exercisesBook 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 Matplotlib, to read data from Python, manipulate it, and create plots, 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 MatplotlibStudy clustering techniques, such as hierarchical and k-means clusteringCreate customer segments based on manipulated data Predict customer lifetime value using linear regressionUse classification algorithms to understand customer choiceOptimize classification algorithms to extract maximal informationWho 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.