Product Analytics
Applied Data Science Techniques for Actionable Consumer Insights| By: | Joanne Rodrigues |
| Publisher: | Pearson Technology Group |
| Print ISBN: | 9780135258521 |
| eText ISBN: | 9780135258620 |
| Edition: | 1 |
| Copyright: | 2021 |
| Format: | Page Fidelity |
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Use Product Analytics to Understand and Change Consumer Behavior at Scale
Product Analytics is a complete, hands-on guide to generating actionable business insights from customer data. Experienced data scientist and enterprise manager Joanne Rodrigues introduces practical statistical techniques for determining why things happen and how to change, at scale, what people do. She complements these with powerful social science techniques for creating better theories, designing better metrics, and driving more rapid and sustained behavior change.
This book fills the gaps that many other data science book leave behind: how to start a new data science project; how to conceptualize complex ideas; building metrics from the statistic and demographic fundamentals; projecting consumer populations and material needs for a business; and causal inference beyond simple A/B testing techniques, such as difference-in-difference, regression discontinuity, propensity score matching, and uplift modelling.
Writing for entrepreneurs, product managers, marketers, and other business analytics professionals, Rodrigues teaches through intuitive examples from both web and offline environments. Avoiding math-heavy explanations, she guides you step by step through choosing the right techniques and algorithms for each application, running analyses in the R programming language, and getting answers you can trust.
Whatever your product or service, this guide can help you create precision-targeted marketing campaigns, improve consumer satisfaction and engagement, and grow revenue and profits.
- Develop core metrics and effective KPIs for user analytics in any web product
- Truly understand statistical inference, and the differences between correlation and causation
- Build intuitive predictive models to capture user behavior in products
- Tease out causal effects from observational data using modern, quasi-experimental designs and statistical matching
- Improve response through uplift modeling and other sophisticated targeting methods
- Project business costs and product population changes via advanced demographic techniques