Growth Marketing Scholarship-Week 2-Running Growth Experiment-Review
I am accepted CXL institute Growth Marketing mini degree Scholarship program. It is 12 weeks long, this week is my 2nd week. I going to share with you my 2nd-week learning. The mini degree is divided into the following steps.
- Growth Marketing foundation
- Running Growth experiments
- Data and Analytics
- Channel-Specific Growth Skills
- Growth program Management
- Final exam — Growth Marketing
2nd week, I started the second step, which is entitled Running Growth Experiments. This part consists of 4 Courses and 2 Event Videos, as below,
Research and Testing by Peep Laja
Conversion Research by Peep Laja
A/B Testing Mastery by Ton Wesseling
Statistics Fundamentals for Testing by Ben Labay
A better way to prioritize A/B tests — Leho kraav
What to test next prioritizing your tests — P.Marol & J.foucher
The experimentation process is one of the most critical parts of any type of marketing. In growth marketing, it is very important. This week I cover 2 courses in this part, both taught by Peep Laja, one of the most respected CRO professionals in the world, and also the founder of the CXL Institute.
Research and testing by Peep Laja
This course is actually not a course, this is an event video, speaker is Peep Laja. He starts with optimization, optimization is compound interest for growth, so he taught us to 3 steps to optimize whatever you are optimizing. I listed 3 steps below.
- Test ( or make ) more effective change — In this step, we need to know if we are going to test more prioritized hypotheses and if the results of these tests will return into effective results.
- Reduce the duration ( and cost ) of optimization — CRO is very costly we need to reduce this, using the right optimization process.
- Improve the speed of experimentation — High frequency of testing increases the speed of results.
Some people are going to optimize using the list of tactics found somewhere. It does not work very well. The reasons are no way to prioritize, and the self-feeding loop of bullshit. Copying competitors or anyone’s optimization technique never works very well. Peep Laja taught me to use a systematic approach for the optimization process.
“If you can not describe what you are doing as a process, you do not know what you are doing” This quote by William Deming.
What does a good optimization look like? If you have a good optimization process you can answer these questions. What is the question?
Where are the problems?
What are the problems?
Why is this or that a problem?
Turning know issues into test hypotheses, then prioritizing tests and instant fixes. If we want to do things right we need a process. The process is The ResearchXL Framework. I cover more insight next course.
Conversion Research by Peep Laja
Every business wants more conversion, more revenue. If businesses want more money, they need good optimization. so every optimization project has to start with an optimization project. It diagnoses a website, and figure out where and how it’s leaking money. Once we know that we can go ahead and start plugging the holes.
Every conversion research consists of three categorize into three parts
- Experience-based assessment
- Qualitative research
- Quantitative research
Peep Laja highly recommends using the ResearchXL conversion optimization process framework. It created by Peep Laja.
The model indicates 6 steps to collect data and perform analyzes, in order to transform the points raised into actions. they are:
Heuristic Analysis — It is best to begin by understanding the user’s experience on the website. There are 4 features to consider when auditing a website for experience
Technical Analysis — It’s time to make sure everything is running smoothly from a technical standpoint. Then learn to identify several low-hanging fruits in terms of bugs, page speed, broken pages more areas, just optimizing these things can bring tremendous uplifts.
Digital Analytics — learn to set up measurements that will provide insights that will instruct, which areas you should spend the majority of your time optimizing.
Qualitative Research — by collecting qualitative data we can understand more personally understand user’s experience and pinpoint areas of friction to work on. This section taught me about customer surveys.
User Testing — user testing is used to identify which part of the message is not correctly set up, user-facing challenges buying products on your website, and much more.
Mouse Tracking Analysis — It provides valuable insight into viewing and information processing patterns using tools, Heat maps, clicks maps, scrolls maps, session replays, and much more
Following these 6 steps, It is possible to create a process, that converter very well. This ReaserchXL conversion rate optimization process model will be successful when it is able to inform,
Where the problems are?
What are the problems?
Why do these issues exist?
How to turn them into hypotheses
How to guides prioritization and instant corrections
After prioritization, we need to test our hypotheses, in order to avoid wasting time by repeating the same testing, it is crucial to continually self assess the efficacy of the testing program. These three metrics are most revealing and insightful moving forward,
2. Percentage of tests that provide a win
3. Impact per successful experiment
Peep Laja also lists 12 testing errors that he frequently sees, avoids them for efficient results of your testing.
1. Precious time wasted on stupid teats — testing hypotheses without proper research.
2. You think you know what will work — without proper research, test guessing the hypotheses and test it.
3. You copy other people’s tests — every business has a distinct problem, copying other people’s tests not work for you.
4. Your sample size is too low
5. You run tests on pages with very little traffic
6. Your tests don’t run long enough
7. You don’t test full weeks at a time
8. The data is not sent to third party analytics
9. You give up after your first test for a hypothesis fails
10. You are not aware of validity threats
11. You are ignoring small gains
12. You are not running tests at all time
These are the insight I learned last week. Next week I will go deeper into A/B testing and statistical fundamentals. Stay with me. Thanks for reading.