Leseprobe
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Design for Impact
Erin Weigel
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Sozialwissenschaften, Recht, Wirtschaft / Wirtschaft
Beschreibung
Design for Impact is a down–to–earth A/B testing guide. It features the Conversion Design process to operationalize effective experimentation in your company. In it, Erin Weigel gives you practical tips and tools to design better experiments at scale. She does this with self–deprecating humor that will leave you smiling—if not laughing aloud. As a bonus, The Good Experimental Design toolkit presents everything you learn into step–by–step process for you to use each day.
Who Should Read This Book?
If you're a curious person working in tech who wants to deliver impactful work, you should read this book. If you're a business leader looking to help your team make better decisions, you should read this book. If you want to level–up your approach to experimentation, you should read this book. In short, everyone—from CEOs to marketers, engineers, product people, through to designers and content folks—should read this book.
Takeaways
- Learn a fun, balanced approach to digital product experimentation to get your whole team testing customer–centric ideas.
- Stop making changes and start making improvements with the Conversion Design process.
- Follow the Good Experimental Design toolkit so that you and your entire team design for impact together.
- Clear up confusion around A/B testing with helpful tools and practical advice.
- Look for loads of actionable tips for effective product experimentation to give your team insight into the big picture.
- Make the complex math behind why experimentation works easy and understandable.
Kundenbewertungen
probability procedural memory, Null Hypothesis, true negative, false urgency, statistical significance, Conversion Design, quantitative research, hindsight bias, independent variable, User experience, experimentation culture, explanatory research, back-end tracking, minimum detectable effect, attitudinal insight, false positive, A/A testing, form validation, burn-in effect, error matrix, data volatility, cognitive dissonance, PM, product management, return rate, CX, confusion matrix, error message, relative change, Double Diamond, market research, box plot, customer lifetime value, hypothesize phase, bell curve, interaction effect, localization, organizational psychology, decide phase, customer confidence, sampling distribution, design thinking, hidden costs, cognitive distancing, customer experience, true positive, alternate hypothesis, evidence collection, Texas Sharpshooter fallacy, prioritize phase, observational study, Goodhart’s Law, controlling confound, test phase, cause/effect relationship, dependent variable, fixed horizon test, cherry picking, task efficiency, cultural difference, company culture, information redundancy, p-value- sampling distribution, confounding variable, UX, commitment bias, novelty effect, product-market fit, baseline conversation, Compound Effect, significance, causality, Sample Ration Mismatch, experimental design, randomization, trust triangle, leading indicator, clustering illusion, edge-case bug, p-values, conversion rate, sample size, backlog, confidence interval, confirmation bias, confirmshaming, cognitive bias, data noise, qualitative research, A/B testing, Laplacian Curve, deceptive pattern, Product Value Chain, Experimentation Cultural Maturity, framing techniques, data dredging, descriptive research, anaylze phase, false negative