On LinkedIn short quotes and reactions.
For the newsletter, deep dive.
CEO and founder of konversionsKRAFT, André Morys has influenced the experimentation industry for almost three decades.
Practitioners crossed his frameworks on CXL, his thoughts on multiple discussion panels, and his enthusiasm at Growth Marketing Summit hosted yearly in Frankfurt.
There is no innovation without risk. If you're not able to take risks, you're not able to innovate.
I see experimentation as a way to control the risk of innovation.
💬 Within an innovation ecosystem, experimentation is a means to an end. It's a decision-making tool that mature product teams leverage.
I would differentiate between different levels of innovation. Adaptation, Sustaining, and the third level, which is maybe the most difficult, Disruptive innovation.
💬 I subscribe to the "three types of innovation" spectrum.
Efficiency, Sustaining, and Transformative.
I believe Strategyzer delivers the best language for navigating this field.
Examples from Innovation @ Uber
Efficiency — Uber makes continuous incremental improvements to its core products, either in response to rider/driver feedback or based on experiments the company ran.
Sustaining — Next, Uber identified and developed new ride products, such as UberPOOL, that were optimized for a different set of customer preferences, expanding the initial value proposition.
Transformative — Uber EATS was a riskier bet based on fundamental changes to the business model and technology.
That is like a flywheel. I think most people and organizations are not aware of the fact that experimentation is a process.
💬 Experimentation and flywheel in the same sentence? It takes a flywheel to fly.
Many factors that are hard to measure are below the surface.
You cannot measure the influence of experimentation on your company's culture and how big and sustaining this effect will be, it will be maybe 10 times more important than the actual uplift you create.
💬 Hubbard, author of "How to measure anything", would call this a potential false dichotomy.
Requirements for a Decision (from the book)
A decision has two or more realistic alternatives, but it cannot be a false dichotomy.
A decision has uncertainty. If there is no uncertainty about a decision, then it’s not really much of a dilemma.
A decision has potentially negative consequences if it turns out you took the wrong position.
Hubbard says:
We saw is sometimes the case when managers want to know the value of IT or the value of clean drinking water.
If the alternative is not a serious consideration (i.e., doing without IT or clean water altogether) then there are not two viable alternatives.
Why do you want to measure the impact of experimentation?
Do you plan to do business without it? What's your best alternative?
People love gamification, you can motivate people by asking them the good old "Witch test won?" game and placing bets on hypotheses.
I think it's a good way to educate people and create some more pull toward experimentation. It humbles people.
So many companies, for example, focus on velocity and efficiency.
They want as many experiments as possible, which is right it's not wrong, but it's also not completely right.
💬 Start optimizing for quantity and layer quality as the program matures and the flywheel gets momentum, as I told you here.
We ask our clients what are your strategic goals, that's where it begins.
Connect your experimentation with your strategic goals.
If it's not connected, it might be irrelevant.
🎯 Bullseye