The absence of decisions backed up by data usually reminds me of a plane being flown without the presence of any measurement instruments or at least very basic ones.
Especially today, this shouldn’t be necessary anymore.
Businesses are inundated with data from all angles. Every click, every interaction, and every piece of feedback can offer insights.
Similar to a physicist or social scientist that postulates hypotheses and confirms or rejects them, a data-driven company runs experiments all the time using live data.
A prominent example of this commitment to experimentation is Netflix. Famous not just for its content but also for its culture of relentless testing, Netflix consistently runs A/B tests on everything—from the thumbnails that best catch viewers’ attention to the algorithms powering content recommendations. This culture of experimentation has played a pivotal role in making Netflix a global entertainment powerhouse. You can listen to how this works first hand in the Tim Ferris show #496 with Netflix founder Marc Randolph on “Building Netflix, Battling Blockbuster, Negotiating with Amazon/Bezos, and Scraping the Barnacles Off the Hull”.
Another intriguing personal account comes from Bryan Johnson. In the “Diary of a CEO” podcast with Steven Bartlett, Johnson revealed that he has conducted over 200 experiments to decipher which factors most profoundly influence his sleep, both positively and negatively.
These meticulous approaches to data and experimentation, whether at the personal level or in a corporate setting, underline the importance of empiricism in decision-making. As part of the ValueWorks data-driven company culture series, let’s delve deeper into why experiments play such a pivotal role in the heart of every data-centric organization.
Especially today, this shouldn’t be necessary anymore.
Businesses are inundated with data from all angles. Every click, every interaction, and every piece of feedback can offer insights.
Similar to a physicist or social scientist that postulates hypotheses and confirms or rejects them, a data-driven company runs experiments all the time using live data.
A prominent example of this commitment to experimentation is Netflix. Famous not just for its content but also for its culture of relentless testing, Netflix consistently runs A/B tests on everything—from the thumbnails that best catch viewers’ attention to the algorithms powering content recommendations. This culture of experimentation has played a pivotal role in making Netflix a global entertainment powerhouse. You can listen to how this works first hand in the Tim Ferris show #496 with Netflix founder Marc Randolph on “Building Netflix, Battling Blockbuster, Negotiating with Amazon/Bezos, and Scraping the Barnacles Off the Hull”.
Another intriguing personal account comes from Bryan Johnson. In the “Diary of a CEO” podcast with Steven Bartlett, Johnson revealed that he has conducted over 200 experiments to decipher which factors most profoundly influence his sleep, both positively and negatively.
These meticulous approaches to data and experimentation, whether at the personal level or in a corporate setting, underline the importance of empiricism in decision-making. As part of the ValueWorks data-driven company culture series, let’s delve deeper into why experiments play such a pivotal role in the heart of every data-centric organization.
Why Experiments Matter
1. Removing Guesswork:
Data-driven experiments allow companies to move beyond mere assumptions. Just like how a physicist or a social scientist would operate, businesses postulate hypotheses and confirm or reject them in a rapid manner. Instead of relying on intuition or industry hearsay, companies can now base their decisions on tangible results.
Data-driven experiments allow companies to move beyond mere assumptions. Just like how a physicist or a social scientist would operate, businesses postulate hypotheses and confirm or reject them in a rapid manner. Instead of relying on intuition or industry hearsay, companies can now base their decisions on tangible results.
2. Mitigating Risk:
Before rolling out a significant change or a new feature, running an experiment can give a clearer idea of the expected impact. This can prevent costly mistakes and unwarranted changes to established workflows or products.
Before rolling out a significant change or a new feature, running an experiment can give a clearer idea of the expected impact. This can prevent costly mistakes and unwarranted changes to established workflows or products.
3. Continuous Improvement:
The beauty of experimentation lies in its cyclical nature. Whether a hypothesis is proven right or wrong, there’s always a lesson to be learned, which can then feed into the next hypothesis.
Experiments in Action: Increasing Product Stickiness
Imagine hypothesizing that a new feature in your mobile app will enhance user engagement and make the product more integral to the user’s daily routine (i.e., increasing its stickiness).
How would this play out in a data-driven culture?
1. Define the Hypothesis:
“Our new feature X will result in a 20% increase in daily active users over a period of 60 days.”
2. Design the Experiment: This could involve rolling out the feature to a control group while keeping a separate group without this feature.
3. Monitor Key Metrics: Regularly track metrics such as daily active users, session duration, and feature-specific interactions for both groups.
4. Analyze and Draw Conclusions: After 60 days, compare the engagement metrics of both groups. Has the feature-driven group shown a statistically significant increase in daily active users?
5. Make Data-Informed Decisions: If the experiment proves successful, the feature can be rolled out to all users. If not, it’s back to the drawing board, armed with new insights.
2. Design the Experiment: This could involve rolling out the feature to a control group while keeping a separate group without this feature.
3. Monitor Key Metrics: Regularly track metrics such as daily active users, session duration, and feature-specific interactions for both groups.
4. Analyze and Draw Conclusions: After 60 days, compare the engagement metrics of both groups. Has the feature-driven group shown a statistically significant increase in daily active users?
5. Make Data-Informed Decisions: If the experiment proves successful, the feature can be rolled out to all users. If not, it’s back to the drawing board, armed with new insights.
The Bigger Picture
While the experiment above centered on product stickiness, this methodology can be applied across various aspects of a business. From marketing strategies to HR policies, the ethos of “test, analyze, and iterate” can drive more informed and effective decisions.
To sum it up, experiments aren’t just for the lab. When it comes to fostering a data-driven company culture, they’re an indispensable tool, providing clarity in an uncertain world and paving the way for true innovation. Because in every test, in every hypothesis validated or rejected, lies the promise of progress and a step closer to excellence.
So, the next time you’re faced with a big decision, remember: don’t just guess—test!
To sum it up, experiments aren’t just for the lab. When it comes to fostering a data-driven company culture, they’re an indispensable tool, providing clarity in an uncertain world and paving the way for true innovation. Because in every test, in every hypothesis validated or rejected, lies the promise of progress and a step closer to excellence.
So, the next time you’re faced with a big decision, remember: don’t just guess—test!
