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A/B Testing Case Studies: Learning from the Best in Conversion Rate Optimization
Author: Dr. Evelyn Reed, PhD in Statistics, Senior Data Scientist at Conversion Catalyst, specializing in A/B testing and experimental design.
Keywords: A/B testing case studies, A/B testing examples, conversion rate optimization (CRO), website optimization, experimentation, data-driven decision making, A/B testing best practices, case study analysis, A/B testing results, statistical significance, A/B testing software.
Publisher: ConversionXL, a leading online resource for conversion rate optimization, known for its high-quality content and industry expertise.
Editor: Marcus Sheridan, Founder and CEO of The Sales Lion, renowned expert in inbound marketing and sales strategies.
Summary: This article provides a comprehensive exploration of A/B testing case studies, highlighting their importance in driving data-driven decision-making for website optimization and conversion rate improvement. It examines various successful A/B test examples across different industries, detailing the methodologies, challenges encountered, results achieved, and key takeaways. The article emphasizes the critical role of statistical significance and proper experimental design in generating reliable insights from A/B testing. It also discusses common pitfalls to avoid and offers practical advice for conducting and interpreting A/B tests effectively. The article serves as a valuable resource for marketers, website owners, and data analysts seeking to leverage the power of A/B testing to enhance their online performance.
Introduction: The Power of A/B Testing Case Studies
In the dynamic landscape of digital marketing, understanding user behavior is paramount to success. A/B testing, a cornerstone of conversion rate optimization (CRO), provides a powerful method for improving website performance and maximizing conversions. However, theory alone is insufficient. To truly grasp the intricacies and effectiveness of A/B testing, studying real-world examples – A/B testing case studies – is essential. These case studies offer invaluable insights into successful strategies, common pitfalls, and the practical application of statistical analysis. By examining diverse A/B testing case studies, businesses can learn from the successes and failures of others, ultimately refining their own A/B testing methodologies and boosting their ROI.
Analyzing Successful A/B Testing Case Studies: Key Elements
Effective A/B testing case studies should detail specific aspects of the experiment, enabling readers to learn and replicate successful strategies. Key elements to examine in any A/B testing case study include:
The Hypothesis: What specific change was being tested, and what was the expected outcome? A clear and well-defined hypothesis forms the foundation of any successful A/B test.
The Methodology: How was the test designed? What sample size was used? What statistical methods were employed to determine significance? Understanding the methodology allows for critical evaluation of the results.
The Variations: What were the different versions (A and B) being compared? What were the key differences between the control and variant?
The Results: What were the quantitative and qualitative results? Was statistical significance achieved? Were there any unexpected findings?
The Conclusions: What were the key takeaways from the test? What actions were taken based on the results?
A/B Testing Case Studies Across Industries: Examples and Analysis
Let's delve into several illustrative A/B testing case studies, showcasing diverse approaches and outcomes across various industries:
1. E-commerce: Optimizing Product Pages: An online retailer tested different variations of product page layouts, including changes to image placement, call-to-action buttons, and product descriptions. One A/B testing case study revealed that a more concise product description, coupled with a more prominent "Add to Cart" button, resulted in a 15% increase in conversion rates. This case study highlights the importance of optimizing individual elements on product pages.
2. SaaS: Improving Signup Forms: A Software as a Service (SaaS) company tested several variations of its signup form, modifying the length, fields requested, and overall design. An A/B testing case study showed that a shorter, simplified form, focusing only on essential information, led to a significant increase in completed signups, demonstrating the impact of reducing friction in the user journey.
3. Content Marketing: Enhancing Headlines and Calls to Action: A blog tested different headlines and calls to action (CTAs) for a particular article. An A/B testing case study illustrated that a more benefit-driven headline, combined with a stronger CTA, resulted in a 20% increase in click-through rates. This example emphasizes the power of compelling copywriting in driving user engagement.
4. Finance: Personalizing Website Content: A financial institution experimented with personalized website content based on user demographics and browsing history. An A/B testing case study demonstrated that personalized recommendations and targeted messaging significantly improved engagement and conversion rates. This case study highlights the significance of tailoring the user experience to individual needs.
5. Healthcare: Improving Patient Portal Usability: A healthcare provider tested different layouts and functionalities within their patient portal. A/B testing case studies in this area often reveal how changes to navigation, search functionality, and appointment scheduling processes can positively impact patient satisfaction and streamline workflows.
Common Pitfalls to Avoid in A/B Testing
While A/B testing offers significant advantages, several common pitfalls can compromise the accuracy and reliability of results:
Insufficient Sample Size: A small sample size can lead to statistically insignificant results, making it difficult to draw reliable conclusions.
Ignoring Statistical Significance: Simply observing a difference between variations is insufficient. It's critical to ensure that the observed difference is statistically significant, indicating that it's unlikely due to random chance.
Testing Too Many Variables Simultaneously: Conducting A/B tests with multiple changes at once can make it difficult to pinpoint the specific cause of any observed effect. It is best practice to change only one thing at a time.
Running Tests for Too Short a Period: The duration of the test should be long enough to capture sufficient data and account for variations in user behavior throughout the day and week.
Neglecting Qualitative Data: While quantitative data is crucial, qualitative data, such as user feedback and heatmaps, can provide valuable insights into why certain variations performed better or worse.
Best Practices for Conducting and Interpreting A/B Tests
To maximize the effectiveness of A/B testing, follow these best practices:
Define Clear Objectives and Hypotheses: Establish specific, measurable, achievable, relevant, and time-bound (SMART) goals.
Use a Robust A/B Testing Platform: Choose a reliable platform that provides accurate data and statistical analysis.
Employ Proper Sample Size Calculation: Ensure sufficient sample size to achieve statistically significant results.
Monitor the Test Closely: Regularly track results and make adjustments as needed.
Analyze Qualitative Data: Gather user feedback and utilize tools like heatmaps to understand user behavior.
Document Results Thoroughly: Maintain detailed records of the test design, results, and conclusions.
Conclusion: The Indispensable Role of A/B Testing Case Studies
A/B testing case studies provide invaluable lessons for businesses seeking to optimize their online presence and boost conversion rates. By analyzing successful examples and learning from past mistakes, businesses can refine their A/B testing methodologies, improve experimental design, and ultimately achieve greater success in their online marketing efforts. The ongoing study and analysis of A/B testing case studies are crucial for staying ahead in the ever-evolving digital landscape.
FAQs:
1. What is the difference between A/B testing and multivariate testing? A/B testing compares two versions (A and B), while multivariate testing compares multiple versions simultaneously.
2. How long should an A/B test run? The duration depends on factors like traffic volume and desired statistical significance, but generally, longer tests (several weeks) are preferred.
3. What is statistical significance in A/B testing? Statistical significance indicates that the observed difference between variations is unlikely due to chance, providing confidence in the results.
4. What are some common A/B testing metrics? Conversion rates, click-through rates, bounce rates, and average session duration are common metrics.
5. What is the role of a control group in A/B testing? The control group serves as a baseline for comparison, allowing for the accurate measurement of the impact of the variation.
6. What A/B testing software is recommended? Several platforms offer robust A/B testing capabilities, including Optimizely, VWO, and Google Optimize.
7. How can I analyze qualitative data from A/B tests? User feedback forms, surveys, heatmaps, and session recordings can provide valuable qualitative insights.
8. What are some common reasons for A/B testing failure? Insufficient sample size, poorly defined hypotheses, and inadequate testing duration are common reasons for failure.
9. How can I improve the accuracy of my A/B testing results? Ensure a large enough sample size, use proper statistical methods, and carefully control for extraneous variables.
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2. "A/B Testing Case Study: How We Increased Conversions by 30%": This case study focuses on a specific A/B test, detailing the methodology, results, and key takeaways.
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| a b testing case studies: A / B Testing Dan Siroker, Pete Koomen, 2015-07-27 How Your Business Can Use the Science That Helped Win the White House The average conversion rate—the rate at which visitors convert into customers—across the web is only 2%. That means it's likely that 98% of visitors to your website won't end up converting into customers. What's the solution? A/B testing. A/B testing is the simple idea of showing several different versions of a web page to live traffic, and then measuring the effect each version has on visitors. Using A/B testing, companies can improve the effectiveness of their marketing and user experience and, in doing so, can sometimes double or triple their conversion rates. Testing has been fundamental in driving the success of Google, Amazon, Netflix, and other top tech companies. Even Barack Obama and Mitt Romney had dedicated teams A/B testing their campaign websites during the 2012 Presidential race. In the past, marketing teams were unable to unleash the power of A/B testing because it required costly engineering and IT resources. Today, a new generation of technology that enables marketers to run A/B tests without depending on engineers is emerging and quickly becoming one of the most powerful tools for making data-driven decisions. Authors Dan Siroker and Pete Koomen are cofounders of Optimizely, the leading A/B testing platform used by more than 5,000 organizations across the world. A/B Testing: The Most Powerful Way to Turn Clicks Into Customers offers best practices and lessons learned from more than 300,000 experiments run by Optimizely customers. You'll learn: What to test How to choose the testing solution that's right for your organization How to assemble an A/B testing dream team How to create personalized experiences for every visitor And much more Marketers and web professionals will become obsolete if they don't embrace a data-driven approach to decision making. This book shows you how, no matter your technical expertise. |
| a b testing case studies: Trustworthy Online Controlled Experiments Ron Kohavi, Diane Tang, Ya Xu, 2020-04-02 Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice. |
| a b testing case studies: You Should Test That Chris Goward, 2012-12-21 Learn how to convert website visitors into customers Part science and part art, conversion optimization is designed to turn visitors into customers. Carefully developed testing procedures are necessary to help you fine-tune images, headlines, navigation, colors, buttons, and every other element, creating a website that encourages visitors to take the action you seek. This book guides you through creating an optimization strategy that supports your business goals, using appropriate analytics tools, generating quality testing ideas, running online experiments, and making the adjustments that work. Conversion optimization is part science and part art; this guide provides step-by-step guidance to help you optimize your website for maximum conversion rates Explains how to analyze data, prioritize experiment opportunities, and choose the right testing methods Helps you learn what to adjust, how to do it, and how to analyze the results Features hands-on exercises, case studies, and a full-color insert reinforcing key tactics Author has used these techniques to assist Fortune 500 clients You Should Test That explains both the why and the how of conversion optimization, helping you maximize the value of your website. |
| a b testing case studies: Stop Marketing, Start Selling Shaun Tinney, Jon MacDonald, 2015-09-04 Your guide to doubling online leads, customers, and revenue. The basic value proposition of any business is to help people get what they want. A website is no different. Nobody watches TV for the commercials, or visits your website to check out your latest marketing campaigns. If they're on your site, your marketing worked. Now it's time to help them get what they came for. The partners at The Good (http: //thegood.com), an ecommerce and lead generation advisory, have condensed their learnings from over a decade in the ecommerce space. Their battle tested process for growing online revenues for brands large and small is shared in this comprehensive and actionable path to doubling online leads, customers and revenue. This book offers a step by step guide to making websites that convert. In the age of empowered customers the best possible business case is to put the needs of your customers first. This book is a practical, no-nonsense approach to doing just that. It may not always tell you what you want to hear, but it certainly tells you what you need to hear. -Gerry McGovern, Author, CEO of Customer Carewords When you invite guests to your house, you want them to enjoy themselves and leave happy. You should have the same mindset with your website. In this book, The Good shows you how to create a customer experience that converts. -Stephen Lease, Founder, Simplify & Go |
| a b testing case studies: Clinical Case Studies for the Family Nurse Practitioner Leslie Neal-Boylan, 2011-11-28 Clinical Case Studies for the Family Nurse Practitioner is a key resource for advanced practice nurses and graduate students seeking to test their skills in assessing, diagnosing, and managing cases in family and primary care. Composed of more than 70 cases ranging from common to unique, the book compiles years of experience from experts in the field. It is organized chronologically, presenting cases from neonatal to geriatric care in a standard approach built on the SOAP format. This includes differential diagnosis and a series of critical thinking questions ideal for self-assessment or classroom use. |
| a b testing case studies: Smart Persuasion Philippe AIMÉ, Jochen GRÜNBECK, 2019-03-01 Conversions begin in the brain. Every purchase starts with a decision, and every decision is shaped by consumer psychology. This book explains how mental shortcuts (cognitive biases) affect your customers' decision making and shows you how to be more persuasive online. Philippe Aimé and Jochen Grünbeck are optimisation addicts and have been at the forefront of digital marketing since the beginning. Inspired by behavioural economists like Daniel Kahneman, Dan Ariely and Richard Thaler, the techniques described in Smart Persuasion leverage powerful decision-making biases to make marketing more effective. Alongside these behavioural insights, Smart Persuasion incorporates research from marketing experts such as Jonah Berger, Robert Cialdini and Roger Dooley. Principles relating to attention and perception, as well as the cognitive effects that make consumers predictably irrational, are distilled into concrete website optimisation strategies. Drawing from hundreds of unique studies, Smart Persuasion lists proven effects such as Anchoring and Framing. Each one is illustrated with case-studies, examples and ideas that you can apply immediately. Using the persuasive strategies outlined in this book will allow you to influence consumers more effectively, unlocking your website's potential. All profits from the sale of this book help provide educational resources for children in Africa. |
| a b testing case studies: Practical A/B Testing Leemay Nassery, 2023-05-23 Whether you're a catalyst for organizational change or have the support you need to create an engineering culture that embraces A/B testing, this book will help you do it right. The step-by-step instructions will demystify the entire process, from constructing an A/B test to breaking down the decision factors to build an engineering platform. When you're ready to run the A/B test of your dreams, you'll have the perfect blueprint. With smart, tactful approaches to orchestrating A/B testing on a product, you'll quickly discover how to reap all the benefits that A/B testing has to offer - benefits that span your users, your product, and your team. Take the reins today, and be the change you want to see in your engineering and product organizations. Develop a hypothesis statement that's backed with metrics that demonstrate if your prediction for the experiment is correct. Build more inclusive products by leveraging audience segmentation strategies and ad-hoc post analysis to better understand the impact of changes on specific user groups. Determine which path is best for your team when deciding whether to go with a third-party A/B test framework or to build the A/B testing platform in-house. And finally, learn how to cultivate an experimentation-friendly culture within your team. Leverage the A/B testing methodology to demonstrate the impact of changes on a product to your users, your key business metrics, and the way your team works together. After all, if you aren't measuring the impact of the changes you make, how will you know if you're truly making improvements? |
| a b testing case studies: Landing Page Optimization Tim Ash, Maura Ginty, Rich Page, 2012-03-29 A fully updated guide to making your landing pages profitable Effective Internet marketing requires that you test and optimize your landing pages to maximize exposure and conversion rate. This second edition of a bestselling guide to landing page optimization includes case studies with before-and-after results as well as new information on web site usability. It covers how to prepare all types of content for testing, how to interpret results, recognize the seven common design mistakes, and much more. Included is a gift card for Google AdWords. Features fully updated information and case studies on landing page optimization Shows how to use Google's Website Optimizer tool, what to test and how to prepare your site for testing, the pros and cons of different test strategies, how to interpret results, and common site design mistakes Provides a step-by-step implementation plan and advice on getting support and resources Landing Page Optimization, Second Edition is a comprehensive guide to increasing conversions and improving profits. |
| a b testing case studies: Your Customer Creation Equation Brian Massey, 2012-07-01 Finally-a book that shows marketers how to truly achieve real results from their websites. Brian Massey, The Conversion Scientist, takes the mystery out of how to create high-performing sites. By walking the reader through five online formulas-aka customer creation equations-he shows you how to determine the best formula your own particular business structure and how to optimize it for stellar results. Key to this process is setting up a digital conversion lab, and Brian shows you how. Jam-packed with easy-to-understand equations for things like increasing your conversion rate and decreasing your abandonment rate-as well as practical strategies for attracting prospects, turning buyers into triers, and morphing buyers into loyal brand advocates-this book will enable anyone to stop hoping for success and start enjoying higher profits. The Advanced Curriculum in Visitor Studies gives readers additional guidance on how to really understand their targets and customers-an understanding that is at the heart of all successful websites, and businesses, everywhere. |
| a b testing case studies: Conversion Optimization Khalid Saleh, Ayat Shukairy, 2010-11-01 How do you turn website visitors into customers? Conversion Optimization offers practical advice on how to persuade visitors to make a buying decision -- without driving them away through data overload or tedious navigation. You'll learn how to use marketing principles, design, usability, and analytics on your site to increase your buyer-to-visitor ratio, whether you're involved with marketing or designing a large ecommerce site, or managing a modest online operation. Based on the authors' broad experience in helping businesses attract online customers, this book addresses every aspect of the process, from landing visitors to finalizing the sale. You'll learn several techniques for blending successful sales approaches with the particular needs of the people you want to attract. Are you ready to do what it takes to get a double-digit conversion rate? Explore case studies involving significant conversion rate improvements Walk through different stages of a sale and understand the value of each Understand your website visitors through persona creation Connect with potential customers and guide them toward a conversion Learn how to deal with FUDs -- customer fears, uncertainties, and doubts Examine the path that visitors take from landing page to checkout Test any change you make against your original design The Web is unique in its ability to deliver this almost improbable win-win: You can increase revenue AND make your customers happy. Yet most websites stink. Worry not, Khalid and Ayat to the rescue! Buy this book to follow their practical advice on how to create high converting websites that your visitors love.--Avinash Kaushik, author of Web Analytics 2.0 and Web Analytics: An Hour A Day (both Sybex) |
| a b testing case studies: The Power of Experiments Michael Luca, Max H. Bazerman, 2021-03-02 How tech companies like Google, Airbnb, StubHub, and Facebook learn from experiments in our data-driven world—an excellent primer on experimental and behavioral economics Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments—also known as randomized controlled trials—designed to test the impact of different online experiences. Once an esoteric tool for academic research, the randomized controlled trial has gone mainstream. No tech company worth its salt (or its share price) would dare make major changes to its platform without first running experiments to understand how they would influence user behavior. In this book, Michael Luca and Max Bazerman explain the importance of experiments for decision making in a data-driven world. Luca and Bazerman describe the central role experiments play in the tech sector, drawing lessons and best practices from the experiences of such companies as StubHub, Alibaba, and Uber. Successful experiments can save companies money—eBay, for example, discovered how to cut $50 million from its yearly advertising budget—or bring to light something previously ignored, as when Airbnb was forced to confront rampant discrimination by its hosts. Moving beyond tech, Luca and Bazerman consider experimenting for the social good—different ways that governments are using experiments to influence or “nudge” behavior ranging from voter apathy to school absenteeism. Experiments, they argue, are part of any leader's toolkit. With this book, readers can become part of “the experimental revolution.” |
| a b testing case studies: The Paradox of Choice Barry Schwartz, 2009-10-13 Whether we're buying a pair of jeans, ordering a cup of coffee, selecting a long-distance carrier, applying to college, choosing a doctor, or setting up a 401(k), everyday decisions—both big and small—have become increasingly complex due to the overwhelming abundance of choice with which we are presented. As Americans, we assume that more choice means better options and greater satisfaction. But beware of excessive choice: choice overload can make you question the decisions you make before you even make them, it can set you up for unrealistically high expectations, and it can make you blame yourself for any and all failures. In the long run, this can lead to decision-making paralysis, anxiety, and perpetual stress. And, in a culture that tells us that there is no excuse for falling short of perfection when your options are limitless, too much choice can lead to clinical depression. In The Paradox of Choice, Barry Schwartz explains at what point choice—the hallmark of individual freedom and self-determination that we so cherish—becomes detrimental to our psychological and emotional well-being. In accessible, engaging, and anecdotal prose, Schwartz shows how the dramatic explosion in choice—from the mundane to the profound challenges of balancing career, family, and individual needs—has paradoxically become a problem instead of a solution. Schwartz also shows how our obsession with choice encourages us to seek that which makes us feel worse. By synthesizing current research in the social sciences, Schwartz makes the counter intuitive case that eliminating choices can greatly reduce the stress, anxiety, and busyness of our lives. He offers eleven practical steps on how to limit choices to a manageable number, have the discipline to focus on those that are important and ignore the rest, and ultimately derive greater satisfaction from the choices you have to make. |
| a b testing case studies: Experimentation in Software Engineering Claes Wohlin, Per Runeson, Martin Höst, Magnus C. Ohlsson, Björn Regnell, Anders Wesslén, 2012-06-16 Like other sciences and engineering disciplines, software engineering requires a cycle of model building, experimentation, and learning. Experiments are valuable tools for all software engineers who are involved in evaluating and choosing between different methods, techniques, languages and tools. The purpose of Experimentation in Software Engineering is to introduce students, teachers, researchers, and practitioners to empirical studies in software engineering, using controlled experiments. The introduction to experimentation is provided through a process perspective, and the focus is on the steps that we have to go through to perform an experiment. The book is divided into three parts. The first part provides a background of theories and methods used in experimentation. Part II then devotes one chapter to each of the five experiment steps: scoping, planning, execution, analysis, and result presentation. Part III completes the presentation with two examples. Assignments and statistical material are provided in appendixes. Overall the book provides indispensable information regarding empirical studies in particular for experiments, but also for case studies, systematic literature reviews, and surveys. It is a revision of the authors’ book, which was published in 2000. In addition, substantial new material, e.g. concerning systematic literature reviews and case study research, is introduced. The book is self-contained and it is suitable as a course book in undergraduate or graduate studies where the need for empirical studies in software engineering is stressed. Exercises and assignments are included to combine the more theoretical material with practical aspects. Researchers will also benefit from the book, learning more about how to conduct empirical studies, and likewise practitioners may use it as a “cookbook” when evaluating new methods or techniques before implementing them in their organization. |
| a b testing case studies: Case Studies & Cocktails Carrie Shuchart, Chris Ryan, 2011-03-15 After all the hard work on your application, you’re finally in to business school. Now what? The acceptance letter is just the beginning of your MBA experience. Even before classes start, you’ll face all kinds of new challenges: financing your degree, readjusting to homework, schmoozing recruiters. Now you can turn to this book, produced by Manhattan GMAT—one of the leading names in GMAT preparation—to ready you for the challenges you’ll face as a newly-minted MBA candidate.Case Studies & Cocktails will be your go-to guide as you prepare to enter your MBA program and throughout your time at b-school. The authors—MBAs themselves—have drawn on their own experiences and interviewed current students for the inside scoop on every aspect of b-school, from telling the boss you’re going back to school to balancing wine and cheese in one hand while networking. The result is both a handbook for the social side of school and an academic primer on the material you’ll have to master. The book even includes a glossary of need-to-know jargon, so you won’t feel lost when classmates start slinging around acronyms. |
| a b testing case studies: HBR Guide to Dealing with Conflict (HBR Guide Series) Amy Gallo, 2017-03-14 Learn to assess the situation, manage your emotions, and move on. While some of us enjoy a lively debate with colleagues and others prefer to suppress our feelings over disagreements, we all struggle with conflict at work. Every day we navigate an office full of competing interests, clashing personalities, limited time and resources, and fragile egos. Sure, we share the same overarching goals as our colleagues, but we don't always agree on how to achieve them. We work differently. We rub each other the wrong way. We jockey for position. How can you deal with conflict at work in a way that is both professional and productive--where it improves both your work and your relationships? You start by understanding whether you generally seek or avoid conflict, identifying the most frequent reasons for disagreement, and knowing what approaches work for what scenarios. Then, if you decide to address a particular conflict, you use that information to plan and conduct a productive conversation. The HBR Guide to Dealing with Conflict will give you the advice you need to: Understand the most common sources of conflict Explore your options for addressing a disagreement Recognize whether you--and your counterpart--typically seek or avoid conflict Prepare for and engage in a difficult conversation Manage your and your counterpart's emotions Develop a resolution together Know when to walk away Arm yourself with the advice you need to succeed on the job, with the most trusted brand in business. Packed with how-to essentials from leading experts, the HBR Guides provide smart answers to your most pressing work challenges. |
| a b testing case studies: Statistical Methods in Online A/B Testing Georgi Zdravkov Georgiev, 2019-09-28 Statistical Methods in Online A/B Testing is a comprehensive guide to statistics in online controlled experiments, a.k.a. A/B tests, that tackles the difficult matter of statistical inference in a way accessible to readers with little to no prior experience with it. Each concept is built from the ground up, explained thoroughly, and illustrated with practical examples from website testing. The presentation is straight to the point and practically oriented so you can apply the takeaways in your daily work.It is a must-read for anyone looking for a deep understanding of how to make data-driven business decisions through experimentation: conversion rate optimizers, product managers, growth experts, data analysts, marketing managers, experts in user experience and design. The new research presented and the fresh perspective on how to apply statistics and experimentation to achieve business goals make for an interesting read even for experienced statisticians.The book deals with scientific methods, but their introductions and explanations are grounded in the business goals they help achieve, such as innovating under controlled risk, and estimating the effect of proposed business actions before committing to them. While the book doesn't shy away from math and formulas, it is to the extent to which these are essential for understanding and applying the underlying concepts. The presentation is friendly to readers with little to no prior knowledge in statistics. Artificial and impractical examples like dice rolling and betting are absent, instead statistical concepts are illustrated through scenarios which might well be mistaken with the last couple of A/B tests you managed.This book also doesn't shy away from the fact that much of the current statistical theory and practice in online A/B testing is misguided, misinterpreted, or misapplied. It also addresses the issue of blind copying of scientific applications without due consideration of the unique features of online business, which is widespread. The book will help you avoid these malpractices by explicitly pointing out frequent mistakes, while also helping you align your usage of statistics and experimentation with any business goals you might want to pursue. |
| a b testing case studies: Statistical Inference as Severe Testing Deborah G. Mayo, 2018-09-20 Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification. |
| a b testing case studies: Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease Institute of Medicine, Food and Nutrition Board, Board on Health Sciences Policy, Board on Health Care Services, Committee on Qualification of Biomarkers and Surrogate Endpoints in Chronic Disease, 2010-06-25 Many people naturally assume that the claims made for foods and nutritional supplements have the same degree of scientific grounding as those for medication, but that is not always the case. The IOM recommends that the FDA adopt a consistent scientific framework for biomarker evaluation in order to achieve a rigorous and transparent process. |
| a b testing case studies: Research Methods in Human-Computer Interaction Jonathan Lazar, Jinjuan Heidi Feng, Harry Hochheiser, 2017-04-28 Research Methods in Human-Computer Interaction is a comprehensive guide to performing research and is essential reading for both quantitative and qualitative methods. Since the first edition was published in 2009, the book has been adopted for use at leading universities around the world, including Harvard University, Carnegie-Mellon University, the University of Washington, the University of Toronto, HiOA (Norway), KTH (Sweden), Tel Aviv University (Israel), and many others. Chapters cover a broad range of topics relevant to the collection and analysis of HCI data, going beyond experimental design and surveys, to cover ethnography, diaries, physiological measurements, case studies, crowdsourcing, and other essential elements in the well-informed HCI researcher's toolkit. Continual technological evolution has led to an explosion of new techniques and a need for this updated 2nd edition, to reflect the most recent research in the field and newer trends in research methodology. This Research Methods in HCI revision contains updates throughout, including more detail on statistical tests, coding qualitative data, and data collection via mobile devices and sensors. Other new material covers performing research with children, older adults, and people with cognitive impairments. - Comprehensive and updated guide to the latest research methodologies and approaches, and now available in EPUB3 format (choose any of the ePub or Mobi formats after purchase of the eBook) - Expanded discussions of online datasets, crowdsourcing, statistical tests, coding qualitative data, laws and regulations relating to the use of human participants, and data collection via mobile devices and sensors - New material on performing research with children, older adults, and people with cognitive impairments, two new case studies from Google and Yahoo!, and techniques for expanding the influence of your research to reach non-researcher audiences, including software developers and policymakers |
| a b testing case studies: Case Studies in Immunology Raif Geha, Luigi Notarangelo, 2016-02-05 Case Studies in Immunology, Seventh Edition is intended for medical students and undergraduate and graduate students in immunology. It presents major topics of immunology through a selection of clinical cases that reinforce and extend the basic science. Each case history is preceded by essential scientific facts about the immunological mechanisms o |
| a b testing case studies: Experimentation Works Stefan H. Thomke, 2020-02-18 Don't fly blind. See how the power of experiments works for you. When it comes to improving customer experiences, trying out new business models, or developing new products, even the most experienced managers often get it wrong. They discover that intuition, experience, and big data alone don't work. What does? Running disciplined business experiments. And what if companies roll out new products or introduce new customer experiences without running these experiments? They fly blind. That's what Harvard Business School professor Stefan Thomke shows in this rigorously researched and eye-opening book. It guides you through best practices in business experimentation, illustrates how these practices work at leading companies, and answers some fundamental questions: What makes a good experiment? How do you test in online and brick-and-mortar businesses? In B2B and B2C? How do you build an experimentation culture? Also, best practice means running many experiments. Indeed, some hugely successful companies, such as Amazon, Booking.com, and Microsoft, run tens of thousands of controlled experiments annually, engaging millions of users. Thomke shows us how these and many other organizations prove that experimentation provides significant competitive advantage. How can managers create this capability at their own companies? Essential is developing an experimentation organization that prizes the science of testing and puts the discipline of experimentation at the center of its innovation process. While it once took companies years to develop the tools for such large-scale experiments, advances in technology have put these tools at the fingertips of almost any business professional. By combining the power of software and the rigor of controlled experiments, today's managers can make better decisions, create magical customer experiences, and generate big financial returns. Experimentation Works is your guidebook to a truly new way of thinking and innovating. |
| a b testing case studies: Entity-Oriented Search Krisztian Balog, 2018-10-02 This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms. |
| a b testing case studies: Forecasting: principles and practice Rob J Hyndman, George Athanasopoulos, 2018-05-08 Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. |
| a b testing case studies: Why Startups Fail Tom Eisenmann, 2021-03-30 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. |
| a b testing case studies: Introductory Business Statistics 2e Alexander Holmes, Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| a b testing case studies: Numbersense: How to Use Big Data to Your Advantage Kaiser Fung, 2013-07-12 How to make simple sense of complex statistics--from the author of Numbers Rule Your World We live in a world of Big Data--and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it--whether we realize it or not. Where do you send your child for the best education? Big Data. Which airline should you choose to ensure a timely arrival? Big Data. Who will you vote for in the next election? Big Data. The problem is, the more data we have, the more difficult it is to interpret it. From world leaders to average citizens, everyone is prone to making critical decisions based on poor data interpretations. In Numbersense, expert statistician Kaiser Fung explains when you should accept the conclusions of the Big Data experts--and when you should say, Wait . . . what? He delves deeply into a wide range of topics, offering the answers to important questions, such as: How does the college ranking system really work? Can an obesity measure solve America's biggest healthcare crisis? Should you trust current unemployment data issued by the government? How do you improve your fantasy sports team? Should you worry about businesses that track your data? Don't take for granted statements made in the media, by our leaders, or even by your best friend. We're on information overload today, and there's a lot of bad information out there. Numbersense gives you the insight into how Big Data interpretation works--and how it too often doesn't work. You won't come away with the skills of a professional statistician. But you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up. Praise for Numbersense Numbersense correctly puts the emphasis not on the size of big data, but on the analysis of it. Lots of fun stories, plenty of lessons learned—in short, a great way to acquire your own sense of numbers! Thomas H. Davenport, coauthor of Competing on Analytics and President’s Distinguished Professor of IT and Management, Babson College Kaiser’s accessible business book will blow your mind like no other. You’ll be smarter, and you won’t even realize it. Buy. It. Now. Avinash Kaushik, Digital Marketing Evangelist, Google, and author, Web Analytics 2.0 Each story in Numbersense goes deep into what you have to think about before you trust the numbers. Kaiser Fung ably demonstrates that it takes skill and resourcefulness to make the numbers confess their meaning. John Sall, Executive Vice President, SAS Institute Kaiser Fung breaks the bad news—a ton more data is no panacea—but then has got your back, revealing the pitfalls of analysis with stimulating stories from the front lines of business, politics, health care, government, and education. The remedy isn’t an advanced degree, nor is it common sense. You need Numbersense. Eric Siegel, founder, Predictive Analytics World, and author, Predictive Analytics I laughed my way through this superb-useful-fun book and learned and relearned a lot. Highly recommended! Tom Peters, author of In Search of Excellence |
| a b testing case studies: Buyer Legends Bryan Eisenberg, Jeffrey Eisenberg, Anthony Garcia, 2015-02-17 By New York Times Bestselling Authors Bryan and Jeffrey Eisenberg with Anthony Garcia, Buyer Legends: An Executive Storytellers Guide describes a business process that combines the emotional power of storytelling with hard data to open new opportunities, spot gaps and optimize your sales and marketing.By using Buyer Legends in your organization you will:* Improve communications - Your whole team will see and understand both the bigger picture and the important details* Improve execution - You will turn big directives into purposeful and more effective actions* Improve testing. You will understand how to plan and implement more effective and impactful tests * Make more money. You will see improved conversion rates that make the up-front planning worth the time and effortAfter reading this, you will have more insight as to why your marketing execution may not be meeting expectations and why your team might be struggling to get it. You will learn how to communicate your brand's story intent and the responsibility of each critical touch point within every level of your organization, from the boardroom to the stockroom. The Buyer Legends process IS one of the final pieces of a complex puzzle that has been missing from most modern marketing efforts.Wouldn't you like to have an edge in turning your brand into a legend?Having worked first hand with the Eisenbergs on mapping our customers' critical paths and creating scenario narratives, I can confidently say the Buyer Legends process works. My team's focus at Google is on acquiring SMB advertising clients. And if you've ever worked with these types of businesses, you know there is huge diversity through the spectrum of small and medium businesses. We'd miss opportunities and gaps by over-aggregating (i.e. taking too high level a view) though often the challenge was in effectively communicating our insights. The Buyer Legends framework allowed us to more effectively focus our efforts, improving the bottom line. And equally important, to make a more compelling case for change with our marketing, engineering and product colleagues.Paul JeszenszkyHead of Global B2B Digital Marketing Center of Excellence, GoogleThe most clear-headed and useful guide ever for developing relevant and resonant stories about your business.Jay BaerPresident, Convince & ConvertBuyer Legends introduced me to a structured process which uses storytelling techniques to align our brand story to our customers experiences. With so many of our customers having an unique experience every day on Airbnb, it is our task to collect and communicate a collective narrative in the Airbnb brand story. While the Airbnb storyboarding technique, as described in the introduction, gives a clear overview of the customers journey, the motives of our customers and their experiences are many. With global differences on how people travel, making their decision where to stay, and experience the more local hospitality Airbnb provides, Buyer Legends is the marketing tool which binds Persona's, storyboards and our brand story. This is a powerful combination.Dennis GoedegebuureHead of Global SEO, Airbnb |
| a b testing case studies: Testing 1 - 2 - 3 Johannes Ledolter, Arthur J. Swersey, 2007 This book gives students, practitioners, and managers a set of practical and valuable tools for designing and analyzing experiments, emphasizing applications in marketing and service operations such as website design, direct mail campaigns, and in-store tests. |
| a b testing case studies: Universal Methods of Design Bella Martin, Bruce Hanington, 2012-02 Universal Methods of Design is an immensely useful survey of research and design methods used by today's top practitioners, and will serve as a crucial reference for any designer grappling with really big problems. This book has a place on every designer's bookshelf, including yours! —David Sherwin, Principal Designer at frog and author of Creative Workshop: 80 Challenges to Sharpen Your Design Skills Universal Methods of Design is a landmark method book for the field of design. This tidy text compiles and summarizes 100 of the most widely applicable and effective methods of design—research, analysis, and ideation—the methods that every graduate of a design program should know, and every professional designer should employ. Methods are concisely presented, accompanied by information about the origin of the technique, key research supporting the method, and visual examples. Want to know about Card Sorting, or the Elito Method? What about Think-Aloud Protocols? This book has them all and more in readily digestible form. The authors have taken away our excuse for not using the right method for the job, and in so doing have elevated its readers and the field of design. UMOD is an essential resource for designers of all levels and specializations, and should be one of the go-to reference tools found in every designer’s toolbox. —William Lidwell, author of Universal Principles of Design, Lecturer of Industrial Design, University of Houston This comprehensive reference provides a thorough and critical presentation of 100 research methods, synthesis/analysis techniques, and research deliverables for human centered design, delivered in a concise and accessible format perfect for designers, educators, and students. Whether research is already an integral part of a practice or curriculum, or whether it has been unfortunately avoided due to perceived limitations of time, knowledge, or resources, Universal Methods of Design serves as an invaluable compendium of methods that can be easily referenced and utilized by cross-disciplinary teams in nearly any design project. This essential guide: - Dismantles the myth that user research methods are complicated, expensive, and time-consuming - Creates a shared meaning for cross-disciplinary design teams - Illustrates methods with compelling visualizations and case studies - Characterizes each method at a glance - Indicates when methods are best employed to help prioritize appropriate design research strategies Universal Methods of Design distills each method down to its most powerful essence, in a format that will help design teams select and implement the most credible research methods best suited to their design culture within the constraints of their projects. |
| a b testing case studies: Text Mining and Analysis Dr. Goutam Chakraborty, Murali Pagolu, Satish Garla, 2014-11-22 Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program. |
| a b testing case studies: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| a b testing case studies: One Tequila Tricia O'Malley, 2015-07-08 Tequila Key is just like any other small town and I'm just like any other small town psychic. Scratch that. Tequila Key is a world onto itself and some people might think that I am one crayon short of the box. And, if we're being totally honest, Tequila Key is just like any other small town - if that town boasts a voodoo priestess and a few white witches for flavor. Turquoise blue water and the best margaritas this side of Mexico make it hard to leave. I'm Althea Rose, co-owner of Luna Rose Potions & Tarot Shop, and I've just stumbled into a love triangle while trying to save my best friend from being accused of murder. See? Just like any other small town. |
| a b testing case studies: Causation in European Tort Law Marta Infantino, Eleni Zervogianni, 2017-12-28 This book takes an original and comparative approach to issues of causation in tort law across many European legal systems. |
| a b testing case studies: The Million-Dollar, One-Person Business, Revised Elaine Pofeldt, 2018-01-02 The self-employment revolution is here. Learn the latest pioneering tactics from real people who are bringing in $1 million a year on their own terms. Join the record number of people who have ended their dependence on traditional employment and embraced entrepreneurship as the ultimate way to control their futures. Determine when, where, and how much you work, and by what values. With up-to-date advice and more real-life success stories, this revised edition of The Million-Dollar, One-Person Business shows the latest strategies you can apply from everyday people who--on their own--are bringing in $1 million a year to live exactly how they want. |
| a b testing case studies: The Love Hypothesis Ali Hazelwood, 2021-09-14 The Instant New York Times Bestseller and TikTok Sensation! As seen on THE VIEW! A BuzzFeed Best Summer Read of 2021 When a fake relationship between scientists meets the irresistible force of attraction, it throws one woman's carefully calculated theories on love into chaos. As a third-year Ph.D. candidate, Olive Smith doesn't believe in lasting romantic relationships--but her best friend does, and that's what got her into this situation. Convincing Anh that Olive is dating and well on her way to a happily ever after was always going to take more than hand-wavy Jedi mind tricks: Scientists require proof. So, like any self-respecting biologist, Olive panics and kisses the first man she sees. That man is none other than Adam Carlsen, a young hotshot professor--and well-known ass. Which is why Olive is positively floored when Stanford's reigning lab tyrant agrees to keep her charade a secret and be her fake boyfriend. But when a big science conference goes haywire, putting Olive's career on the Bunsen burner, Adam surprises her again with his unyielding support and even more unyielding...six-pack abs. Suddenly their little experiment feels dangerously close to combustion. And Olive discovers that the only thing more complicated than a hypothesis on love is putting her own heart under the microscope. |
| a b testing case studies: Case Studies in Clinical Psychological Science William O'Donohue, William T. O'Donohue, Scott O. Lilienfeld, 2013-03-14 Case Studies in Clinical Psychological Science demonstrates in detail how the clinical science model can be applied to actual cases. This book's unique structure presents dialogues between leading clinical researchers regarding the treatment of a wide variety of psychological problems. |
| a b testing case studies: Case Study Method Roger Gomm, Martyn Hammersley, Peter Foster, 2000-10-17 This is the most comprehensive guide to the current uses and importance of case study methods in social research. The editors bring together key contributions from the field which reflect different interpretations of the purpose and capacity of case study research. The address issues such as: the problem of generalizing from study of a small number of cases; and the role of case study in developing and testing theories. The editors offer in-depth assessments of the main arguments. An annotated bibliography of the literature dealing with case study research makes this an exhaustive and indispensable guide. |
| a b testing case studies: Case Studies and Theory Development in the Social Sciences Alexander L. George, Andrew Bennett, 2005-04-15 The use of case studies to build and test theories in political science and the other social sciences has increased in recent years. Many scholars have argued that the social sciences rely too heavily on quantitative research and formal models and have attempted to develop and refine rigorous methods for using case studies. This text presents a comprehensive analysis of research methods using case studies and examines the place of case studies in social science methodology. It argues that case studies, statistical methods, and formal models are complementary rather than competitive. The book explains how to design case study research that will produce results useful to policymakers and emphasizes the importance of developing policy-relevant theories. It offers three major contributions to case study methodology: an emphasis on the importance of within-case analysis, a detailed discussion of process tracing, and development of the concept of typological theories. Case Studies and Theory Development in the Social Sciences will be particularly useful to graduate students and scholars in social science methodology and the philosophy of science, as well as to those designing new research projects, and will contribute greatly to the broader debate about scientific methods. |
| a b testing case studies: Case Study Research in Counselling and Psychotherapy John McLeod, 2010-09-22 Case-based knowledge forms an essential element of the evidence base for counselling and psychotherapy practice. This book provides the reader with a unique introduction to the conceptual and practical tools required to conduct high quality case study research that is grounded in their own therapy practice or training. Drawing on real-life cases at the heart of counselling and psychotherapy practice, John McLeod makes complex debates and concepts engaging and accessible for the trainees and practitioners at all levels, and from all theoretical orientations. Key topics covered in the book include: - the role of case studies in the development of theory, practice and policy in counselling and psychotherapy - strategies for responding to moral and ethical issues in therapy case study research - practical tools for collecting case data - ′how-to-do-it′ guides for carrying out different types of case study - team-based case study research for practitioners and students - questions, issues and challenges that may have been raised for readers through their study. Concrete examples, points for reflection and discussion, and recommendations for further reading will enable readers to use the book as a basis for carrying out their own case investigation. All trainees in counselling, psychotherapy and clinical psychology are required to complete case reports, and this is the only textbook to cover the topic in real depth. The book will also be valuable to people who intend to use existing case studies to inform their practice, and it will help experienced practitioners to generate publishable case reports. |
| a b testing case studies: Business Experiments with R B. D. McCullough, 2021-03-26 BUSINESS EXPERIMENTS with R A unique text that simplifies experimental business design and is dedicated to the R language Business Experiments with R offers a guide to, and explores the fundamentals of experimental business designs. The book fills a gap in the literature to provide a text on the topic of business statistics that addresses issues such as small samples, lack of normality, and data confounding. The author—a noted expert on the topic—puts the focus on the A/B tests (and their variants) that are widely used in industry, but not typically covered in business statistics textbooks. The text contains the tools needed to design and analyze two-treatment experiments (i.e., A/B tests) to answer business questions. The author highlights the strategic and technical issues involved in designing experiments that will truly affect organizations. The book then builds on the foundation in Part I and expands the multivariable testing. Since today’s companies are using experiments to solve a broad range of problems, Business Experiments with R is an essential resource for any business student. This important text: Presents the key ideas that business students need to know about experiments Offers a series of examples, focusing on a specific business question Helps develop the ability to frame ill-defined problems and determine what data and analysis would provide information about that problem Written for students of general business, marketing, and business analytics, Business Experiments with R is an important text that helps to answer business questions by highlighting the strategic and technical issues involved in designing experiments that will truly affect organizations. |
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