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Many managers view marketing as a creative endeavor, not something that is measurable or manageable by numbers. But today’s leaders in the C-suite demand greater accountability. They want to know that they are getting a return on their marketing investment. And to get that ROI number, you need analytics. This expectation is intimidating for the many sales and marketing managers who rely on marketing instincts, not metrics, to do their work. But Marketing Analytics Roadmap: Methods, Metrics, and Tools demonstrates that employing analytics isn't just a way to keep the CEO off your back. It improves marketing results and ensures marketers a seat at the table where big decisions get made. In this book, analytics expert Jerry Rackley shows you how to understand and implement a sound marketing analytics process that helps eliminate the guesswork about the results produced by your marketing efforts. The result? You will acquire—and keep—more customers. Even better, you'll find that an analytics process helps the entire organization make better decisions, and not just marketers. Marketing Analytics Roadmap explains: How to use analytics to create marketing and sales metrics that guide your actions and provide valuable feedback on your efforts How to structure and use dashboards to report marketing results How to put industry-leading analytics software and other tools to good use How Big Data is shaping the marketing analytics landscape Sales and marketing teams that master marketing analytics will find them a powerful servant that enables agility, raises effectiveness, and creates confidence. Marketing Analytics Roadmap shows you how to build a well-planned and executed marketing analytics strategy that will enhance the credibility of your marketing team and help you not only get a seat at the big-decisions table, but keep it once there.
This book offers a direct, actionable plan CMOs can use to map out initiatives that are properly sequenced and designed for success—regardless of where their marketing organization is in the process. The authors pose the following critical questions to marketers: (1) How should modern marketers be thinking about artificial intelligence and machine learning? and (2) How should marketers be developing a strategy and plan to implement AI into their marketing toolkit? The opening chapters provide marketing leaders with an overview of what exactly AI is and how is it different than traditional computer science approaches. Venkatesan and Lecinski, then, propose a best-practice, five-stage framework for implementing what they term the "AI Marketing Canvas." Their approach is based on research and interviews they conducted with leading marketers, and offers many tangible examples of what brands are doing at each stage of the AI Marketing Canvas. By way of guidance, Venkatesan and Lecinski provide examples of brands—including Google, Lyft, Ancestry.com, and Coca-Cola—that have successfully woven AI into their marketing strategies. The book concludes with a discussion of important implications for marketing leaders—for your team and culture.
Today, an effective marketing analytics executive is even more important than a brilliant data scientist. That's because successful analytics investments now require managerial orchestration of many elements that go far beyond conventional definitions of analytics. Marketing and Sales Analytics examines the experiences of sales and marketing leaders and practitioners who have successfully built high value analytics capabilities in multiple industries. Then, drawing on their experiences, top analytics consultant Cesar Brea introduces overarching frameworks and specific tools that can help you achieve the same levels of success in your own organization. Brea shows how to: Establish the "ecosystemic" conditions for analytic success Reconcile the diverse perspectives that impact analytics initiatives ("Business v. IT," "Sales v. Marketing," "Analysts v. Creatives v. Managers," and "Everyone v. Finance") Decide what success will "look like" Agree on the questions to ask Organize both internal and external data Establish operational flexibility, and balance flexibility with efficiency Recruit the right people and organize them optimally Intelligently decide what to do yourself, and what to hire vendors for Balance research, analytics, and testing Implement proven research, analytics, and testing strategies Deliver results through storytelling (and recognize its limitations) Control the biases that creep into analytics research Maintain momentum, implement governance, and "keep score"
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Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management.
With businesses becoming ever more competitive, marketing strategies need to be more precise and performance oriented. Companies are investing considerably in analytical infrastructure for marketing. This new volume, Marketing Analytics: A Machine Learning Approach, enlightens readers on the application of analytics in marketing and the process of analytics, providing a foundation on the concepts and algorithms of machine learning and statistics. The book simplifies analytics for businesses and explains its uses in different aspects of marketing in a way that even marketers with no prior analytics experience will find it easy to follow, giving them to tools to make better business decisions. This volume gives a comprehensive overview of marketing analytics, incorporating machine learning methods of data analysis that automates analytical model building. The volume covers the important aspects of marketing analytics, including segmentation and targeting analysis, statistics for marketing, marketing metrics, consumer buying behavior, neuromarketing techniques for consumer analytics, new product development, forecasting sales and price, web and social media analytics, and much more. This well-organized and straight-forward volume will be valuable for marketers, managers, decision makers, and research scholars, and faculty in business marketing and information technology and would also be suitable for classroom use.
THE NEW MARKETING ANALYTICS A clear, common sense guide for implementing an effective data analytics strategy. In today's world, no matter the industry, businesses and organizations are competing in an information-based marketplace. To effectively compete, marketing leaders are tasked with timely analysis of mountains of customer and market information to make faster and better marketing decisions. It's not just the task of collecting information. It's the development of new insights and the ability to implement this new found knowledge to drive more profitable marketing - that's what counts. Marketers get paid to produce results. From chief marketing officers to independent small business owners, success is defined as growing a business, increasing profitability, having a better understanding of customers or being a market leader. This book will demonstrate how the degree of success is in direct proportion to the ability to successfully analyze both traditional and big data. The information and wisdom derived from data is the ultimate competitive advantage. Welcome to the New Marketing Analytics.
Master practical strategic marketing analysis through real-life case studies and hands-on examples. In Cutting Edge Marketing Analytics, three pioneering experts integrate all three core areas of marketing analytics: statistical analysis, experiments, and managerial intuition. They fully detail a best-practice marketing analytics methodology, augmenting it with case studies that illustrate the quantitative and data analysis tools you'll need to allocate resources, define optimal marketing mixes; perform effective analysis of customers and digital marketing campaigns, and create high-value dashboards and metrics. For each marketing problem, the authors help you: Identify the right data and analytics techniques Conduct the analysis and obtain insights from it Outline what-if scenarios and define optimal solutions Connect your insights to strategic decision-making Each chapter contains technical notes, statistical knowledge, case studies, and real data you can use to perform the analysis yourself. As you proceed, you'll gain an in-depth understanding of: The real value of marketing analytics How to integrate quantitative analysis with managerial sensibility How to apply linear regression, logistic regression, cluster analysis, and Anova models The crucial role of careful experimental design For all marketing professionals specializing in marketing analytics and/or business intelligence; and for students and faculty in all graduate-level business courses covering Marketing Analytics, Marketing Effectiveness, or Marketing Metrics
The key competing texts are practitioner-focused ‘how to’ guides, whilst our book combines rigorous theory with practical insight and examples, with authors from both the academic and business world, making it more adoptable as a student text; Unlike other books on the subject, this has a customer focus and an exploration of how big data can add value to customers as well as organisations; Enables readers to move from "big data" to "big solutions" by demonstrating how to integrate data analytics into specific goals and processes for implementation; Highly successful and well regarded both for students and practitioners