Behavioral Insights: organization structures for scaling experimentation 🧱

Behavioral Insights.

Here are a couple of articles on experimentation, machine learning and dashboards:

1. Organization structures for scaling experimentation
"Scaling experimentation is often  the toughest challenge for organizations." Alek Toumert from Optimizely describes 5 different structures an organization can adopt to drive this transition:

1. Center of Excellence

2. Testing Council (Execution)

3. Testing Council (Hybrid)

4. Individual Team

5. Hybrid

Read article


2. Rules of Machine Learning
Martin Zinkevich from Google wrote down 43 rules for successful implementing machine learning models. Well written and easy to read for everyone with some basic knowledge in this area. Some examples of his rules:

• Rule #1: Don’t be afraid to launch a product without machine learning.

• Rule #2: First, design and implement metrics.

• Rule #12: Don’t overthink which objective you choose to directly optimize.

• Rule #13: Choose a simple, observable and attributable metric for your first objective.

Read article


3. Towards Better Experiment Prioritization
Jakob Linowski from GoodUI tries to "... improve the way we prioritize our experiments and a/b tests - with a little less guesswork, a little more honesty." Read article

4. Marketoonist on KPI overload
"We are working in a golden age of metrics.  But the explosion in available data and metrics can give us KPI tunnel vision.  It can blind businesses to what’s really most important."

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• Strategic Analytics Program Manager at eBay (Amsterdam)

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Have a great week!
— Kevin