While you work hard building your startup, one of the biggest challenges you’ll face will be around your product’s ability to solve a big enough problem and its success in the market. Agile Analytics for Startups will help you navigate the complexity of early-stage business analytics, performance measurement, and the metrics that matter to your company. You can use the proven frameworks in this book to validate your product idea and the product/market fit, and understand your customers more granularly while you scale your business for automation. You can test and use many tools and solutions provided in the book and interact with different features of those solutions as you engage with other users of those products. This book will provide you with a step-by-step framework, examples and powerful solutions, from ideation to growth and all the way to scaling your business as you build your company with the power of analytics. -Agility is your advantage over large companies -Understand business analytics essentials and define how you will measure the success of your business early -Once you define your solution for “the problem” you tackle, validate your customer -Keep a short list of KPIs for the success of your product -Engage your customers throughout the development cycle -Product/market fit should happen before you go to market big -Keep testing your product, reiterate continuously -Know when to pivot as you modify and optimize your roadmap Be ready to speed up and maximize your output before the significant funding milestone(s)

An Executive Guide to Data Management

Packed with insider tips and tricks, this how-to guide is fully revised to cover the latest version of Google Analytics and shows you how to implement proven Web analytics methods and concepts. This second edition of the bestselling Advanced Web Metrics with Google Analytics is the perfect book for marketers, vendors, consultants, and Webmasters who want to learn the installation, configuration, tracking techniques, and best practices of Google Analytics. Google Analytics is a free tool that measures Web site effectiveness and helps users better understand how web site performance; this book is a detailed usage guide written by one of the software's original creators Explains what filters keep data accurate, how to measure Flash usage and tag for e-mail marketing, and what visitor segmentation provides the most useful feedback Examines principles and practices of Web analytics, then shows how to use GA's reports and how to track dynamic Web pages, banners, outgoing links, and contact forms Discusses advanced setups for configuring goals and filters, how to integrate GA with third-party systems, and how to leverage the new API Advanced Web Metrics with Google Analytics, Second Edition is valuable for both novice and experienced users of Google Analytics.

As a follow-up to the successful Competing on Analytics, authors Tom Davenport, Jeanne Harris, and Robert Morison provide practical frameworks and tools for all companies that want to use analytics as a basis for more effective and more profitable decision making. Regardless of your company's strategy, and whether or not analytics are your company's primary source of competitive differentiation, this book is designed to help you assess your organization's analytical capabilities, provide the tools to build these capabilities, and put analytics to work. The book helps you answer these pressing questions: What assets do I need in place in my organization in order to use analytics to run my business? Once I have these assets, how do I deploy them to get the most from an analytic approach? How do I get an analytic initiative off the ground in the first place, and then how do I sustain analytics in my organization over time? Packed with tools, frameworks, and all new examples, Analytics at Work makes analytics understandable and accessible and teaches you how to make your company more analytical.

This new Edition of Electronic Commerce is a complete update of the leading graduate level/advanced undergraduate level textbook on the subject. Electronic commerce (EC) describes the manner in which transactions take place over electronic networks, mostly the Internet. It is the process of electronically buying and selling goods, services, and information. Certain EC applications, such as buying and selling stocks and airline tickets online, are reaching maturity, some even exceeding non-Internet trades. However, EC is not just about buying and selling; it also is about electronically communicating, collaborating, and discovering information. It is about e-learning, e-government, social networks, and much more. EC is having an impact on a significant portion of the world, affecting businesses, professions, trade, and of course, people. The most important developments in EC since 2014 are the continuous phenomenal growth of social networks, especially Facebook , LinkedIn and Instagram, and the trend toward conducting EC with mobile devices. Other major developments are the expansion of EC globally, especially in China where you can find the world's largest EC company. Much attention is lately being given to smart commerce and the use of AI-based analytics and big data to enhance the field. Finally, some emerging EC business models are changing industries (e.g., the shared economy models of Uber and Airbnb). The 2018 (9th) edition, brings forth the latest trends in e-commerce, including smart commerce, social commerce, social collaboration, shared economy, innovations, and mobility.

Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.