Selling Vision The X Xy Y Formula For Driving Results By Selling Change

A groundbreaking approach to selling in a world demanding change Leaders, sales managers and professionals have found themselves stuck at a crossroads between the past and the future of selling, and they need a roadmap to help them embrace the challenges they face at such a critical juncture. Selling Vision is a step-by-step guide to creating and selling change. By implementing new change management strategies into their unique X→XY→Y selling methodology, the authors: · Propose a new logic for thinking about and executing major sales transformations · Examine these transformations from the customer’s perspective and how their changing buying patterns suggest a particular way of focusing selling activities · Consider the perspective of salespeople and what they can do to sell change to their customers · Look at how sales leaders and managers can change the way their organizations sell products or services · Highlight the pivotal moments that determine the success of major change initiatives Based on their unique X→XY→Y selling methodology, Schachter and Cheatham provide a proven sales strategy to help any sales leader, manager, or professional. For sales leaders, their approach provides a path for transforming the sales organization. For sales managers, it describes how to inspire change in the behavior of salespeople. And for salespeople, it offers a new way of selling that will have a dramatic impact on their performance. For any business executive, Selling Vision provides a faster path to driving change. This book provides immediate actions you can take and experiments you can conduct to find the right direction for future sales efforts at any level of an organization. How you respond to changing sales dynamics will determine your company’s success, that of your customers, and, to a great extent, your own personal career goals and future.

This text on mathematical problem solving provides a comprehensive outline of "problemsolving-ology," concentrating on strategy and tactics. It discusses a number of standard mathematical subjects such as combinatorics and calculus from a problem solver's perspective.

This book provides a comprehensive introduction to modern auction theory and its important new applications. It is written by a leading economic theorist whose suggestions guided the creation of the new spectrum auction designs. Aimed at graduate students and professionals in economics, the book gives the most up-to-date treatments of both traditional theories of 'optimal auctions' and newer theories of multi-unit auctions and package auctions, and shows by example how these theories are used. The analysis explores the limitations of prominent older designs, such as the Vickrey auction design, and evaluates the practical responses to those limitations. It explores the tension between the traditional theory of auctions with a fixed set of bidders, in which the seller seeks to squeeze as much revenue as possible from the fixed set, and the theory of auctions with endogenous entry, in which bidder profits must be respected to encourage participation.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

When you borrow a plate from grandma, does she ask you to pay a deposit? Of course not. Likewise, blocking your non-paying ("freemium") customers from the core experience of your product, is like chopping your own leg off while running a marathon. Yet, this is just one of the crucial mistakes that most SaaS companies make right off the bat. Think about it. Do YOU have... Stalled accounts taking up valuable space? Sub-par clients who only expect freebies and don't ever use the full features of your product? Low conversion from free accounts to paid? Then, you might have a shot-yourself-in-the-foot problem. In this book, you'll find the easy, 6-step formula you can apply to your operations today that can change absolutely everything. You'll be able to count your company among giants like Mixpanel, Ubisoft, and Outsystems when you: Captivate clients' attention from the get-go. Make it easier for clients to get good at using your software so they are more likely to use it. Create a fool-proof checklist to make your product go viral. Match services with behaviors, and get users addicted to your product. Win rave reviews by making clients feel like VIPs. Use this strategy at each level in your team to supercharge its effect. Rinse and repeat, and watch your business grow while you sleep. In short, you'll discover why putting your customer first is the ultimate secret to growing your company. And how you can achieve astronomical conversions and customer loyalty without even trying. Check out what others are saying:

If the very thought of budgets pushes your sanity over the limit, then this practical, easy-to-use guide is just what you need. Budgeting Basics and Beyond, Third Edition equips you with an all-in-one resource guaranteed to make the budgeting process easier, less stressful, and more effective. Written by Jae Shim and Joel Siegel, the new edition covers Balanced Scorecard, budgeting for nonprofit organizations, business simulations for executive and management training, and much more!

In the past, when goods and services were simpler, measurement of quality was self-evident. As business became more complicated, so too did the implementation of quality management and our ability to measure it. Ultimately, the practice of quality strayed from being a business practice to become much more of an engineering discipline producing plen

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.