A startup executive and investor draws on expertise developed at the premier venture capital firm Andreessen Horowitz and as an executive at Uber to address how tech’s most successful products have solved the dreaded "cold start problem”—by leveraging network effects to launch and scale toward billions of users. Although software has become easier to build, launching and scaling new products and services remains difficult. Startups face daunting challenges entering the technology ecosystem, including stiff competition, copycats, and ineffective marketing channels. Teams launching new products must consider the advantages of “the network effect,” where a product or service’s value increases as more users engage with it. Apple, Google, Microsoft, and other tech giants utilize network effects, and most tech products incorporate them, whether they’re messaging apps, workplace collaboration tools, or marketplaces. Network effects provide a path for fledgling products to break through, attracting new users through viral growth and word of mouth. Yet most entrepreneurs lack the vocabulary and context to describe them—much less understand the fundamental principles that drive the effect. What exactly are network effects? How do teams create and build them into their products? How do products compete in a market where every player has them? Andrew Chen draws on his experience and on interviews with the CEOs and founding teams of LinkedIn, Twitch, Zoom, Dropbox, Tinder, Uber, Airbnb, and Pinterest to offer unique insights in answering these questions. Chen also provides practical frameworks and principles that can be applied across products and industries. The Cold Start Problem reveals what makes winning networks thrive, why some startups fail to successfully scale, and, most crucially, why products that create and compete using the network effect are vitally important today.

If you want your startup to succeed, you need to understand why startups fail. “Whether you’re a first-time founder or looking to bring innovation into a corporate environment, Why Startups Fail is essential reading.”—Eric Ries, founder and CEO, LTSE, and New York Times bestselling author of The Lean Startup and The Startup Way Why do startups fail? That question caught Harvard Business School professor Tom Eisenmann by surprise when he realized he couldn’t answer it. So he launched a multiyear research project to find out. In Why Startups Fail, Eisenmann reveals his findings: six distinct patterns that account for the vast majority of startup failures. • Bad Bedfellows. Startup success is thought to rest largely on the founder’s talents and instincts. But the wrong team, investors, or partners can sink a venture just as quickly. • False Starts. In following the oft-cited advice to “fail fast” and to “launch before you’re ready,” founders risk wasting time and capital on the wrong solutions. • False Promises. Success with early adopters can be misleading and give founders unwarranted confidence to expand. • Speed Traps. Despite the pressure to “get big fast,” hypergrowth can spell disaster for even the most promising ventures. • Help Wanted. Rapidly scaling startups need lots of capital and talent, but they can make mistakes that leave them suddenly in short supply of both. • Cascading Miracles. Silicon Valley exhorts entrepreneurs to dream big. But the bigger the vision, the more things that can go wrong. Drawing on fascinating stories of ventures that failed to fulfill their early promise—from a home-furnishings retailer to a concierge dog-walking service, from a dating app to the inventor of a sophisticated social robot, from a fashion brand to a startup deploying a vast network of charging stations for electric vehicles—Eisenmann offers frameworks for detecting when a venture is vulnerable to these patterns, along with a wealth of strategies and tactics for avoiding them. A must-read for founders at any stage of their entrepreneurial journey, Why Startups Fail is not merely a guide to preventing failure but also a roadmap charting the path to startup success.

This vital book is a call to action: to reduce online harm, to protect the integrity of our digital lives and to uphold democratic participation and inclusion. A diverse group of contributors reveal the hidden impacts of technology on society and on individuals, exploring policy change and personal action to keep the internet a force for good. These voices arrive at a crucial juncture in our relationship to fast-evolving technologies.

This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be a very useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer. The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability.

Wall Street Journal, USA Today, and Publishers Weekly Bestseller The secret to leading growth is your mindset Snowflake CEO Frank Slootman is one of the tech world's most accomplished executives in enterprise growth, having led Snowflake to the largest software IPO ever after leading ServiceNow and Data Domain to exponential growth and the public market before that. In Amp It Up: Leading for Hypergrowth by Raising Expectations, Increasing Urgency, and Elevating Intensity, he shares his leadership approach for the first time. Amp It Up delivers an authoritative look at what it takes to transform an organization for maximum growth and scale. Slootman shows that most leaders have significant room to improve their organization's performance without making expensive changes to their talent, structure, or fundamental business model—and they don’t need to bring in an army of consultants to do it. What they do need is to align people around what matters and execute with urgency and intensity every day. Leading for unprecedented growth means declaring war on mediocrity, breaking the status quo, and making conflicted choices daily, all with a relentless focus on the mission. Amp It Up provides the first principles to guide that change, and the tactical advice for organizing a company around them. Perfect for executives, entrepreneurs, founders, managers, and leaders of all kinds, Amp It Up is a must-read resource for anyone who seeks to unleash the growth potential of a company and scale it to heights they never thought possible.

Genetic programming is a new and evolutionary method that has become a novel area of research within artificial intelligence known for automatically generating high-quality solutions to optimization and search problems. This automatic aspect of the algorithms and the mimicking of natural selection and genetics makes genetic programming an intelligent component of problem solving that is highly regarded for its efficiency and vast capabilities. With the ability to be modified and adapted, easily distributed, and effective in large-scale/wide variety of problems, genetic algorithms and programming can be utilized in many diverse industries. This multi-industry uses vary from finance and economics to business and management all the way to healthcare and the sciences. The use of genetic programming and algorithms goes beyond human capabilities, enhancing the business and processes of various essential industries and improving functionality along the way. The Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms covers the implementation, tools and technologies, and impact on society that genetic programming and algorithms have had throughout multiple industries. By taking a multi-industry approach, this book covers the fundamentals of genetic programming through its technological benefits and challenges along with the latest advancements and future outlooks for computer science. This book is ideal for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.