Growth Culture
Tl;dr
Growth is about experimentation — testing hypotheses and making ongoing improvements in order to steer the business towards its intended goal as quickly as possible. To do so, teams need to be able to identify and execute against the highest-impact opportunities.
There are three important traits.
(1) Focus on impact
Companies must align its resources and priorities towards whatever will move the needle the most. While this sounds obvious, in the absence of clear indicators of impact, people gravitate toward behaviors that are often mistaken as signals for productivity — think emails & meetings.
Focus on impact starts with a clear, measurable goal. What is the one thing that we absolutely need to crush? Which metric will be used to measure progress? There must be clarity from leadership on what matters and why — which provides clarity for what does not. Companies become inefficient in resource allocation when employees have different interpretations of their goals and fail to question authority. In such a scenario, motion is mistaken for progress.
Did we win or lose?
Facebook is a great example of an impact-driven culture.
FB’s biz = {# of users} x {time spent per user} x {ad rev per time spent}
Accordingly — product orgs were split into growth, engagement, and ads with their respective KPIs measured as MAUs, time spent, and revenue. There’s clarity of prioritization for each org that rolls up to a company-wide business model.
(2) Data-driven
A data-driven culture is crucial because employees’ intuitions are often incorrect. For instance, when Facebook launched a zero-rated version of its platform (Internet.org) in regions without internet access, we removed photos & videos from the feed to reduce bandwidth costs and make Facebook freely accessible. While this decision didn’t feel intuitively right, our data showed otherwise. We discovered that the utility value of Facebook (the ability to communicate with others) was highly valuable in regions without internet access, leading to higher retention rates than users in the West who had access to photos & videos. Data often challenges our intuition.
We need a work environment that allows everyone to challenge decisions across all levels to let the best data-driven ideas win. To allow data to precede over hierarchy, it must be transparent and accessible to all.
(3) Bias for experimentation
“If you are not embarrassed by the first version of your product, you’ve launched too late” — Reid Hoffman.
Experimentation involves a mindset of continually challenging and testing assumptions. Once a goal is set, we need to formulate hypotheses about what will have the most impact, then act quickly to validate them. This is a cyclical process.
To execute iteratively and measure everything, we have to accept numerous experiments, understanding that some will succeed and grow while others will fail. In fact, we have to accept a high failure rate. We can be wrong often and still achieve significant results because a small fraction of actions can account for the majority of impact. Over an extended period, companies that leverage the compounding power of rapid execution and iterative learning feedback loops build sustainable growth.
