Marketing for Scientists


Book Description

It's a tough time to be a scientist: universities are shuttering science departments, federal funding agencies are facing flat budgets, and many newspapers have dropped their science sections altogether. But according to Marc Kuchner, this antiscience climate doesn't have to equal a career death knell-it just means scientists have to be savvier about promoting their work and themselves. In Marketing for Scientists, he provides clear, detailed advice about how to land a good job, win funding, and shape the public debate. As an astrophysicist at NASA, Kuchner knows that "marketing" can seem like a superficial distraction, whether your daily work is searching for new planets or seeking a cure for cancer. In fact, he argues, it's a critical component of the modern scientific endeavor, not only advancing personal careers but also society's knowledge. Kuchner approaches marketing as a science in itself. He translates theories about human interaction and sense of self into methods for building relationships-one of the most critical skills in any profession. And he explains how to brand yourself effectively-how to get articles published, give compelling presentations, use social media like Facebook and Twitter, and impress potential employers and funders. Like any good scientist, Kuchner bases his conclusions on years of study and experimentation. In Marketing for Scientists, he distills the strategies needed to keep pace in a Web 2.0 world.




Persuading Scientists


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Catalytic Experiences


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Machine Learning and Artificial Intelligence in Marketing and Sales


Book Description

Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer programming.




Data Science for Marketing Analytics


Book Description

Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook Description Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is for This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily.




Hands-On Data Science for Marketing


Book Description

Optimize your marketing strategies through analytics and machine learning Key FeaturesUnderstand how data science drives successful marketing campaignsUse machine learning for better customer engagement, retention, and product recommendationsExtract insights from your data to optimize marketing strategies and increase profitabilityBook Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learnLearn how to compute and visualize marketing KPIs in Python and RMaster what drives successful marketing campaigns with data scienceUse machine learning to predict customer engagement and lifetime valueMake product recommendations that customers are most likely to buyLearn how to use A/B testing for better marketing decision makingImplement machine learning to understand different customer segmentsWho this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.




R.E.D. Marketing


Book Description

Create breakthrough marketing campaigns by harnessing the power of R.E.D. Marketing: a transparent and flexible methodology straight from marketing powerhouse Yum! Brands. Sidestep the marketing books, courses, and even TED talks that offer hypothetical explanations that sound sensible and embrace the proven, systematic approach of R.E.D. Marketing, which the recent CEO and current CMO of Yum! Brands applied to lead Taco Bell and KFC to double digit growth. This book, filled with simple frameworks and engaging stories, will help everyone in your company understand what really works for driving sustainable brand growth and business success. In 2011, Greg Creed had just been elevated from President to CEO of Taco Bell, a brand in deep distress at the time. It was on his shoulders to turn things around quickly along with co-author and CMO, Ken Muench. Together, they developed the R.E.D (Relevance, Ease, Distinctiveness) method. It’s simple methodology does not require complicated terms and a PhD to understand, it’s actually quite simple—marketing works in three very different ways: Relevance—Is it relevant to the marketplace? Ease—Is it easy to access and use? Distinction—Does it stand out from competition? By combining actual examples from Yum! and other recognizable brands of every size around the world with the latest findings in marketing, neuroscience, and behavioral economics, and the author’s own experience marketing three different brands across 120 countries, your brand can set and achieve a truly breakthrough marketing campaign utilizing R.E.D Marketing.




Python for Marketing Research and Analytics


Book Description

This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.




The History Of Marketing Science


Book Description

The field of marketing science has a rich history of modeling marketing phenomena using the disciplines of economics, statistics, operations research, and other related fields. Since it is roughly 50 years from its origins, The History of Marketing Science is a timely review of the accomplishments of marketing scientists in a number of research areas.Different research areas of marketing science, such as Pricing, Internet Marketing, Diffusion Models, and Advertising, are treated to a highly readable and easy-to-digest historical analysis by the contributing authors. Each chapter provides a chronological timeline of key historical developments in the area of marketing science covered. Readers of other disciplinary backgrounds outside of economics, statistics, and operations research will be more than able to appreciate the development of marketing science as a field of research and its pioneers through the book.




Merchants of Doubt


Book Description

The U.S. scientific community has long led the world in research on such areas as public health, environmental science, and issues affecting quality of life. These scientists have produced landmark studies on the dangers of DDT, tobacco smoke, acid rain, and global warming. But at the same time, a small yet potent subset of this community leads the world in vehement denial of these dangers. Merchants of Doubt tells the story of how a loose-knit group of high-level scientists and scientific advisers, with deep connections in politics and industry, ran effective campaigns to mislead the public and deny well-established scientific knowledge over four decades. Remarkably, the same individuals surface repeatedly-some of the same figures who have claimed that the science of global warming is "not settled" denied the truth of studies linking smoking to lung cancer, coal smoke to acid rain, and CFCs to the ozone hole. "Doubt is our product," wrote one tobacco executive. These "experts" supplied it. Naomi Oreskes and Erik M. Conway, historians of science, roll back the rug on this dark corner of the American scientific community, showing how ideology and corporate interests, aided by a too-compliant media, have skewed public understanding of some of the most pressing issues of our era.