Tactics, hacks, experiments <- that's the sexy stuff we all love about growth!
But evaluation, processes, and documentation make up the foundation of a high performing growth team.
Over the years at HubSpot, we've used many different models to evaluate growth projects, and the processes to document our experiments.
It's interesting to learn how companies like Pinterest and Patreon do it.
Pinterest evaluates their growth projects using four factors, Hypothesis, Investments, Precedent, and Experience.
Will this project have a significant impact on metrics, how much time will the project take, is there any precedent that would suggest the project will be successful, and how could it impact user experience?
It's similar to how we evaluate projects.
What I like about Jeff's criteria is he includes the amount of ongoing maintenance work required if the project is successful,
He also highlights the potential impact on user experience, something that's often not considered if you're focused on a single metric, e.g., your North Star Metric.
Let's face it, not many of us like documentation :)
But, Tal covers an integral part of growth experiments -> documenting them!
He includes a sample template you can swipe for your growth experiments.
It includes many of the factors discussed in Jeff's post with some differences.
They differ a lot in how they state a Hypothesis, with Tal being a firm believer that a Hypothesis is not a prediction, merely a statement of what you believe to be true.
There is a separate section for your prediction. Patreon also includes the predicted upside of the experiment, how many people are involved in the test group (e.g., the experiment will be live for 10% of users), any analytics setup needed and plans to scale if it's successful.
Both of them include aspects of ongoing maintenance, how much time/effort it will be, how scalable is the idea, which is important because what happens after the experiment is successful can often be an afterthought and cause a lot of challenges if not considered up front.
Two great articles to ensure you're both evaluating experiments correctly, and documenting them.
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