52 Things We Wish Someone Had Told Us About Customer Analytics is for anyone who uses customer information to make business decisions: CMOs, CEOs, product owners and the people who provide that information, e.g. data scientists, market researchers, business analysts. By tying impact to tools and techniques, through real-life stories, we hope to help decision makers better understand how to use customer data while helping data analysis providers understand how to create output that end users will value.This book provides 52 real-life anecdotes that illustrate important learnings about customer analytics. It draws from the worlds of big data and customer insights. It is our contribution to help managers do a better job using customer analytics (what to do and what not to do) so that the analytics actually makes a difference.Books on customer analytics (data science, business analysis, market research, whatever you like to call it) primarily exist in two categories: as academic texts, which discuss theoretical approaches to data analysis problems; or as technical texts, which teach the statistics or computer programming required to conduct an analysis. As the focus of these books is on analysis tools and techniques, fictitious examples are often used to explain main topics. Our book fills in the missing gap between these approaches by providing real-life, practical stories, tying analysis directly to business value.---"Essential reading for those who want to cut through all the hype of big data. This book has practical advice on how to have real financial and business impact, from the experienced authors who have done this in real life."John ForsythFormer Principal (Partner), McKinsey, former Head, McKinsey's Global Customer Insights Practice---"Mike and Alex have delivered an entertaining and highly readable romp through many aspects of customer analysis-from qualitative focus groups through to terrabytes of big data; and utilizing many real-world examples to reinforce their points. They employ a relentless focus on the use of analysis to deliver meaningful and impactful business value ... and that should matter to you, too, whether you're the CEO, the product owner or a junior analyst delivering the work."George HaylettFormer Asia Analytics Head for Amex, Citibank and HSBC---"Significance. Reliability. Confidence. These and other such terms can be a mantra for both suppliers and buyers of data and analytics. Whether it be big data, qualitative research or something in between; sampling, statistics and "findings" are often the drivers of customer or business analytic exercises. But what about relevance? If the results cannot direct business decisions, what does it matter how "accurate" they are? Used correctly, such analytics are an enormously powerful driver of business performance and profitability. But only if the findings have business salience or business significance. Otherwise, aren't they just another type of BS? In this book, Mike and Alex Sherman lay out some wonderful examples of how the time and money spent on business analytics can transform decision-making or be a complete waste of time. It contains great lessons for buyers and users of such services. But I would also commend it to consultants and suppliers. We shouldn't need to sell what a computer can do with data. We should be promoting what humans and businesses can do by asking the right questions of the results."Adrian ChedoreFormer CEO of Synovate---"This book thoughtfully and practically reminds us that, as we continue to further automate consumer insight analytics efforts with the newest analytics and AI technology, human thinking and human understanding of the fundamental purpose of the analysis, and of the questions that are essential to understanding that purpose, becomes even more important."Professor Steven MillerVice Provost (Research), Singapore Management University

Make personalized marketing a reality with this practical guide to predictive analytics Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences. You'll learn how to use machine-learning technologies to improve customer acquisition and customer growth, and how to identify and re-engage at-risk or lapsed customers by implementing an easy, automated approach to predictive analytics. Much more than just theory and testament to the power of personalized marketing, this book focuses on action, helping you understand and actually begin using this revolutionary approach to the customer experience. Predictive analytics can finally make personalized marketing a reality. For the first time, predictive marketing is accessible to all marketers, not just those at large corporations — in fact, many smaller organizations are leapfrogging their larger counterparts with innovative programs. This book shows you how to bring predictive analytics to your organization, with actionable guidance that get you started today. Implement predictive marketing at any size organization Deliver a more personalized marketing experience Automate predictive analytics with machine learning technology Base marketing decisions on concrete data rather than unproven ideas Marketers have long been talking about delivering personalized experiences across channels. All marketers want to deliver happiness, but most still employ a one-size-fits-all approach. Predictive Marketing provides the information and insight you need to lift your organization out of the campaign rut and into the rarefied atmosphere of a truly personalized customer experience.

An instant New York Times bestseller, Dan Lyons' "hysterical" (Recode) memoir, hailed by the Los Angeles Times as "the best book about Silicon Valley," takes readers inside the maddening world of fad-chasing venture capitalists, sales bros, social climbers, and sociopaths at today's tech startups. For twenty-five years Dan Lyons was a magazine writer at the top of his profession--until one Friday morning when he received a phone call: Poof. His job no longer existed. "I think they just want to hire younger people," his boss at Newsweek told him. Fifty years old and with a wife and two young kids, Dan was, in a word, screwed. Then an idea hit. Dan had long reported on Silicon Valley and the tech explosion. Why not join it? HubSpot, a Boston start-up, was flush with $100 million in venture capital. They offered Dan a pile of stock options for the vague role of "marketing fellow." What could go wrong? HubSpotters were true believers: They were making the world a better place ... by selling email spam. The office vibe was frat house meets cult compound: The party began at four thirty on Friday and lasted well into the night; "shower pods" became hook-up dens; a push-up club met at noon in the lobby, while nearby, in the "content factory," Nerf gun fights raged. Groups went on "walking meetings," and Dan's absentee boss sent cryptic emails about employees who had "graduated" (read: been fired). In the middle of all this was Dan, exactly twice the age of the average HubSpot employee, and literally old enough to be the father of most of his co-workers, sitting at his desk on his bouncy-ball "chair."

Cloud native infrastructure is more than servers, network, and storage in the cloud—it is as much about operational hygiene as it is about elasticity and scalability. In this book, you’ll learn practices, patterns, and requirements for creating infrastructure that meets your needs, capable of managing the full life cycle of cloud native applications. Justin Garrison and Kris Nova reveal hard-earned lessons on architecting infrastructure from companies such as Google, Amazon, and Netflix. They draw inspiration from projects adopted by the Cloud Native Computing Foundation (CNCF), and provide examples of patterns seen in existing tools such as Kubernetes. With this book, you will: Understand why cloud native infrastructure is necessary to effectively run cloud native applications Use guidelines to decide when—and if—your business should adopt cloud native practices Learn patterns for deploying and managing infrastructure and applications Design tests to prove that your infrastructure works as intended, even in a variety of edge cases Learn how to secure infrastructure with policy as code

Erin Ruel′s 100 Questions (and Answers) About Survey Research covers the entire survey research process, starting with developing research questions and ending with the analysis and write-up. It includes the traditional survey topics of design, sampling, question writing, and validity; includes a chapter on research ethics; covers the important topics of preparing, cleaning, and analyzing data; and ends with a section on how to write up survey results for a variety of purposes. Useful as a supplementary text in the classroom or as a reference guide for anyone starting a new survey project, the guidance is presented in a FAQ style to allow readers to jump around the book, so as to accommodate the nonlinear and iterative nature of research.

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.