An online course in growth marketing [part 3] ‘Review’
By: Bahaaldin Alhassan
Tags: Cxl Minidegree, Cxl, Cxl Institute, Google Analytics, Growth

So two exhausting weeks have passed with every new information I implement in this thirsty knowledge mind of mine am feeling too overwhelmed but cxl institute is doing a great job to slice and dice the whole thing, our final stop was conversion research and we covered amazing details about It the instructor couldn't do a better job. So next stop… what cxl institute has for us?.

Running growth experiments module continues with a new lesson called A/B testing mastery course, sounds cool but as I started surfing through the videos I found myself too overwhelmed and I couldn't catch up with the advanced details regarding pervious knowledge in statistics, felt like the world stopped for a minute but guess what? The next lesson is Statistics fundamentals for testing.

This saved my day, but I really wished that they prioritized this one first it would have saved much time wasted on A/B testing, anyway I dived into the course module. in the intro it said:

If you don’t know basic statistics, then you can’t properly test results or even case studies of A/B testing. This course summarizes the statistical concepts that every digital marketer and every CRO should know.

This course will cover:

Sampling — Populations, Parameters, & Statistics.

Mean, Variance, and Confidence Intervals.

What statistical significance (p-value)is and isn’t.

Statistical Power.

Sample size and how to calculate it.

Regression To The Mean & Sampling Error.

and 4 Statistics Traps to Look Out For.

the instructor Ben Labay does a fantastic job explaining the fundamentals essential to know before you get into hypothesis testing so you can properly test results or case studies of A/B testing.

Overview of basic statistical concepts and how they pertain to conversion optimization.

See the full ConversionXL blog post-Ben mentioned here.

after completing it you well-armed for the A/B testing mastery course with much less confusion.

Understand testing approaches that work (and pitfalls that don’t) to get more wins and insights from optimization efforts.

Ton Wesseling; the instructor is a total genius, must say that the banner that says learn from the top 1% marketers had my credibility point.

Ton covers the whole thing which is:

here ton showed that the breakthrough of A/B testing was in 2010 where the number of experiments being run increased with google optimize.

here ton shows what are the uses of hypothesis testing which includes deployment, research, and lean deployment.

To understand in what situations A/B-testing can be valuable and how it should be applied in those situations.

Introduce the “ROAR” rule-of-thumb model.

Explain statistical Power.

Learn how to calculate if you have enough data to conduct A/B-tests.

Understanding when to pick what kind of KPI.

Understanding how to set up an OEC.

Understanding the 6V research model and how it can be used to generate user behavior insights.

Understand why and how to gather insights on View, Voice, Versus, Validated, Verified, and Value.

Understand why it’s good to write a hypothesis before you run a growth or research experiment.

Learn how to write a proper hypothesis.

Understand when you should apply what kind of prioritization model.

Learn how to use a prioritization model and transfer it to an A/B-test roadmap.

Designing an A/B-test.

Developing an A/B-test.

Quality Assuring an A/B-test.

Learn how to configure a proper A/B-test and what consequences certain options may have.

Understand pre-testing en post-testing.

Understand the value of making an analytics connection.

Learn how to calculate the scheduled length of your A/B-test.

Understand how you could shorten the length of your A/B-tests.

Understand why you should not continue an A/B-test when it’s finished.

Understand what needs to be monitored during your A/B-test.

Understand when it makes sense to stop your A/B-test early.

Understand when you're A/B-test is a winner.

Learn what to do when your A/B-test is not a winner.

Understand when to do behavioral analysis.

Understand what information is valuable to present to which people.

Be able to create an A/B-test outcome presentation template that leads to action.

Understand why the result of your A/B-test program is not the sum of all A/B-winners.

Learn how to make a proper business case calculation of you’re A/B-test program.

Understand the balance between quality and quantity while scaling up A/B-testing.

Understand the most logical maturity growth path (and the big pitfall) of A/B-testing throughout your entire organization.

Understand the value of building an archive.

Understand what is interesting to “tag” while filling your archive.

Learn how meta-studies of your A/B-tests can be used for better decision-making.

next, we go to data and analytics cxl provides the following:

Google Analytics for beginners

Intermediate Google Analytics

Google Tag Manager for beginners

Attribution

Excel and Sheets for marketers

I have dived into google analytics for beginners what I learned is :

the instructor does a hell good job at introducing the platform to you.