Crossing the Chasm has become the bible for bringing cutting-edge products to progressively larger markets. This edition provides new insights into the realities of high-tech marketing, with special emphasis on the Internet. It's essential reading for anyone with a stake in the world's most exciting marketplace.

    It seems hard to believe today, but there was a time when Airbnb was the best-kept secret of travel hackers and couch surfers, Pinterest was a niche web site frequented only by bakers and crafters, LinkedIn was an exclusive network for C-suite executives and top-level recruiters, Facebook was MySpace's sorry step-brother, and Uber was a scrappy upstart that didn't stand a chance against the Goliath that was New York City Yellow Cabs.So how did these companies grow from these humble beginnings into the powerhouses they are today? Contrary to popular belief, they didn't explode to massive worldwide popularity simply by building a great product then crossing their fingers and hoping it would catch on. There was a studied, carefully implemented methodology behind these companies' extraordinary rise. That methodology is called Growth Hacking, and it's practitioners include not just today's hottest start-ups, but also companies like IBM, Walmart, and Microsoft as well as the millions of entrepreneurs, marketers, managers and executives who make up the community of GrowthHackers.com.Think of the Growth Hacking methodology as doing for market-share growth what Lean Start-Up did for product development, and Scrum did for productivity. It involves cross-functional teams and rapid-tempo testing and iteration that focuses customers attaining them, retaining them, engaging them, and motivating them to come back and buy more. An accessible and practical toolkit that teams and companies in all industries can use to increase their customer base and market share, this book walks readers through the process of creating and executing their own custom-made growth hacking strategy. It is a must read for any marketer, entrepreneur, innovator or manger looking to replace wasteful big bets and "spaghetti-on-the-wall" approaches with more consistent, replicable, cost-effective, and data-driven results.

    A generation ago, Christensen revolutionized business with his groundbreaking theory of disruptive innovation. Now, he goes further, offering powerful new insights.After years of research, Christensen has come to one critical conclusion: our long held maxim—that understanding the customer is the crux of innovation—is wrong. Customers don’t buy products or services; they "hire" them to do a job. Understanding customers does not drive innovation success, he argues. Understanding customer jobs does. The "Jobs to Be Done" approach can be seen in some of the world’s most respected companies and fast-growing startups, including Amazon, Intuit, Uber, Airbnb, and Chobani yogurt, to name just a few. But this book is not about celebrating these successes—it’s about predicting new ones.Christensen contends that by understanding what causes customers to "hire" a product or service, any business can improve its innovation track record, creating products that customers not only want to hire, but that they’ll pay premium prices to bring into their lives. Jobs theory offers new hope for growth to companies frustrated by their hit and miss efforts.This book carefully lays down Christensen’s provocative framework, providing a comprehensive explanation of the theory and why it is predictive, how to use it in the real world—and, most importantly, how not to squander the insights it provides.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    Adopting Lean will take time and commitment, but it’s vital for harnessing the cultural and technical forces that are accelerating the rate of innovation.* Discover how Lean focuses on people and teamwork at every level, in contrast to traditional management practices* Approach problem-solving experimentally, by exploring solutions, testing assumptions, and getting feedback from real users* Lead and manage large-scale programs in a way that empowers employees, increases the speed and quality of delivery, and lowers costs* Learn how to implement ideas from the DevOps and Lean Startup movements even in complex, regulated environments

    But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool.In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling.Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics.

    Richard Rumelt shows that there has been a growing and unfortunate tendency to equate Mom-and-apple-pie values, fluffy packages of buzzwords, motivational slogans, and financial goals with “strategy.” He debunks these elements of “bad strategy” and awakens an understanding of the power of a “good strategy.” A good strategy is a specific and coherent response to—and approach for overcoming—the obstacles to progress. A good strategy works by harnessing and applying power where it will have the greatest effect in challenges as varied as putting a man on the moon, fighting a war, launching a new product, responding to changing market dynamics, starting a charter school, or setting up a government program. Rumelt’snine sources of power—ranging from using leverage to effectively focusing on growth—are eye-opening yet pragmatic tools that can be put to work on Monday morning.Surprisingly, a good strategy is often unexpected because most organizations don’t have one. Instead, they have “visions,” mistake financial goals for strategy,and pursue a “dog’s dinner” of conflicting policies and actions.Rumelt argues that the heart of a good strategy is insight—into the true nature of the situation, into the hidden power in a situation, and into an appropriate response. He shows you how insight can be cultivated with a wide variety of tools for guiding yourown thinking.Good Strategy/Bad Strategy uses fascinating examples from business, nonprofit, and military affairs to bring its original and pragmatic ideas to life. The detailed examples range from Apple to General Motors, from the two Iraq wars to Afghanistan, from a small local market to Wal-Mart, from Nvidia to Silicon Graphics, from the Getty Trust to the Los Angeles Unified School District, from Cisco Systems to Paccar, and from Global Crossing to the 2007–08 financial crisis.Reflecting an astonishing grasp and integration of economics, finance, technology, history, and the brilliance and foibles of the human character, Good Strategy/Bad Strategy stems from Rumelt’s decades of digging beyond the superficial to address hard questions with honesty and integrity.From the Hardcover edition.

    Topics covered range from the difference between deductive and inductive reasoning, to a discussion of how to highlight the structure of information.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    But few of us realize just how many oversights, biases, and downright ethical nightmares are baked inside the tech products we use every day. It’s time we change that.In Technically Wrong, Sara Wachter-Boettcher demystifies the tech industry, leaving those of us on the other side of the screen better prepared to make informed choices about the services we use—and to demand more from the companies behind them.

    His factory is rapidly heading for disaster. So is his marriage. He has ninety days to save his plant—or it will be closed by corporate HQ, with hundreds of job losses. It takes a chance meeting with a colleague from student days—Jonah—to help him break out of conventional ways of thinking to see what needs to be done.The story of Alex's fight to save his plant is more than compulsive reading. It contains a serious message for all managers in industry and explains the ideas which underline the Theory of Constraints (TOC) developed by Eli Goldratt.

    Management is a key part of any organization, yet the discipline is often self-taught and unstructured. Getting to the good solutions of complex management challenges can make the difference between fulfillment and frustration for teams, and, ultimately, the success or failure of companies. Will Larson's An Elegant Puzzle orients around the particular challenges of engineering management--from sizing teams to technical debt to succession planning--and provides a path to the good solutions. Drawing from his experience at Digg, Uber, and Stripe, Will Larson has developed a thoughtful approach to engineering management that leaders of all levels at companies of all sizes can apply. An Elegant Puzzle balances structured principles and human-centric thinking to help any leader create more effective and rewarding organizations for engineers to thrive in.

    Now, two science professors give us the tools to dismantle misinformation and think clearly in a world of fake news and bad data.It's increasingly difficult to know what's true. Misinformation, disinformation, and fake news abound. Our media environment has become hyperpartisan. Science is conducted by press release. Startup culture elevates bullshit to high art. We are fairly well equipped to spot the sort of old-school bullshit that is based in fancy rhetoric and weasel words, but most of us don't feel qualified to challenge the avalanche of new-school bullshit presented in the language of math, science, or statistics. In Calling Bullshit, Professors Carl Bergstrom and Jevin West give us a set of powerful tools to cut through the most intimidating data.You don't need a lot of technical expertise to call out problems with data. Are the numbers or results too good or too dramatic to be true? Is the claim comparing like with like? Is it confirming your personal bias? Drawing on a deep well of expertise in statistics and computational biology, Bergstrom and West exuberantly unpack examples of selection bias and muddled data visualization, distinguish between correlation and causation, and examine the susceptibility of science to modern bullshit.We have always needed people who call bullshit when necessary, whether within a circle of friends, a community of scholars, or the citizenry of a nation. Now that bullshit has evolved, we need to relearn the art of skepticism.

    For years, Joel Spolsky has done exactly this at www.joelonsoftware.com. Now, for the first time, you can own a collection of the most important essays from his site in one book, with exclusive commentary and new insights from joel.

    Airbnb. Amazon. Apple. PayPal. All of these companies disrupted their markets when they launched. Today they are industry leaders. What’s the secret to their success?These cutting-edge businesses are built on platforms: two-sided markets that are revolutionizing the way we do business. Written by three of the most sought-after experts on platform businesses, Platform Revolution is the first authoritative, fact-based book on platform models. Whether platforms are connecting sellers and buyers, hosts and visitors, or drivers with people who need a ride, Geoffrey G. Parker, Marshall W. Van Alstyne, and Sangeet Paul Choudary reveal the what, how, and why of this revolution and provide the first “owner’s manual” for creating a successful platform business.Platform Revolution teaches newcomers how to start and run a successful platform business, explaining ways to identify prime markets and monetize networks. Addressing current business leaders, the authors reveal strategies behind some of today’s up-and-coming platforms, such as Tinder and SkillShare, and explain how traditional companies can adapt in a changing marketplace. The authors also cover essential issues concerning security, regulation, and consumer trust, while examining markets that may be ripe for a platform revolution, including healthcare, education, and energy.As digital networks increase in ubiquity, businesses that do a better job of harnessing the power of the platform will win. An indispensable guide, Platform Revolution charts out the brilliant future of platforms and reveals how they will irrevocably alter the lives and careers of millions.