Become great at advanced experimentation analysis
Go way beyond what most optimization experts know
By Chad Sanderson,
Head of Product, Data Platform @ Convoy
Course length: 4h 30min
Some of the companies that train their teams at CXL:
From zero to hero in self-serve experimentation
After you take this course, you won’t need to rely on what the testing tools say about results.
You’ll be able to go into the data yourself, understand it, query it, and perform advanced statistical techniques to get more value than any tool can really give you. That’s independence.
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Get to the top of the food chain in terms of the best CRO practitioners in the world
Having the ability to dive deep into experimental statistics and analysis using data and code is a skill that very few people have. You will be one of those rare phenomena. This will put you many steps ahead of all those optimization experts out there.
In this course, Chad will peel back the curtain on the world of advanced experimental statistics, data science, and discuss deep experimental understanding. All using common sense language.
- Learn what makes data analysis so important and why we should think very carefully about building our own metrics
- Understand how to generate sample size (the right way)
- Master principles of analysis and metric building
- Discover how to build, launch, and analyze experiments offline
- Use statistical techniques like regression and time series analysis for CRO
- Go beyond traditional A/B testing, focusing on unconventional methods that maybe you haven’t heard of before
- Generate powerful custom charts to communicate testing results
- Start working with R (even – or especially if your most hated thing is jumping into code since you’re not a developer).
This course will teach you how to go beyond traditional A/B testing with advanced statistical techniques (no math background required), clear away numerous misconceptions about common and uncommon testing tropes, and open your eyes to the business-wide transformative strength of experimentation.
Ideal for you if…
- You are a conversion optimization specialist looking to dive deep into experimental statistics and analysis using data and code
- You have a hand in analyzing, interpreting, or reporting the results of digital experiments
- Your business or client fully believes in the power of experimentation
This course is probably not for you if…
- You are an experienced data-scientist, researcher, or statistician with a deep knowledge of experimental design
- You are a CMO or VP. This course is less strategy, and more hands-on functional knowledge.
- You are content with the surface level testing results reported by your testing tool
Skills you should have before taking this course:
- Intermediate A/B Testing experience. We won’t be covering how or why you should be setting up experiments or using a testing tool.
- Foundational understanding of analytics best practices and reporting. We expect you to know how to choose and report on important metrics.
- Working knowledge of Microsoft Excel or an equivalent. We will be working with data!
I feel so much more confident in analysis and optimization after taking CXL’s courses.
I learn practical techniques that are applicable to any business from the best in class.
CXL showed me how to go from no optimization program to a fully matured program, and how to handle all the nuances that pop up along the way.
If you want to take the next step from dabbling to becoming a real CRO practitioner, the CXL material (plus a lot of practice) will get you there.
Learn processes for getting consistent results from leaders in the field.
Chad Sanderson is a Digital Optimization, Testing, and Personalization specialist focused on the strategy, design, implementation, and analysis of winning experiments.
Chad is a passionate believer in the power of data, combining deep actionable insights with UX and Content strategy to create better websites and mobile apps.
Your full course curriculum:
Advanced experimentation analysis
In this intro lesson, Chad explores the 'why' of experimentation analysis. When you come to understand the fundamentals, you will learn how to set you and your team apart when launching and managing experiments. By beginning to understand the various approaches of experimental analysis you will discover just how deep the rabbit hole goes.
- Who am I?
- What is this course?
- What will you learn?
- Why is experimentation analysis important?
- Testing beyond your tool
In this lesson, Chad dives deep into what it means to build metrics and why that's so fundamentally important to experimentation analysis. Analytics in short is looking to data for insight, which is critical in the context of experimentation. Begin to identify what you could be doing and learn how to work on metrics you actively create.
- Why do we need to prepare data?
- Formatting data in excel
- What is R Studio
- Installing R Studio
- Reading data
- What is sample size really
- The components of sample size
- Binomial metrics explained
- Sample ratio mismatch
- Continuous metrics explained
- Practice using R Studio
- N2/N1 Ratio
In this lesson, join Chad as he touches on the immense value of randomization and representational data and how to avoid alpha inflations when creating an honest analysis.
- X2 Test
- Executing tests within R
- Segmentation using dplyr
- Adjusting p values
- Confidence Intervals
- Plotting using ggplot2
- Sample size
- Size effect
- Statistical calculators
- Statistical power
- What can’t we experiment on using an A/B Testing tool?
- Regression Discontinuity Designs and Why We Use them
- Interrupted Time Series Analysis
In this lesson, Chad delivers practical advice and tips on experimentation for your organization at large. The lesson touches on areas of experimentation that may put your business at risk and ways you and your can team can design high impact experiments.
- Other forms of RDD
Join Chad in the last lesson to cover all the extra details and quality controls needed for proper experimentation analysis.
- Control vs. Treatments
- Setting up proper controls
- Response Surface Methodology
- Experimentation Brain Storming
- Testing offline
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