Every new idea starts with a hypothesis. You come up with an explanation of why it could work, then validate it with a lean experiment.
This sounds like a pretty straightforward, non-controversial way of building products, right?
But here’s a problem: When our entire focus is on a single hypothesis, it can blind us from exploring other possibilities. As a result, we tend to pick up every small signal to justify it (confirmation bias).
This isn’t a problem for an early-stage product. At that stage, any signal is better than nothing. However, if you are building a product at scale, it can mislead you into making regrettable decisions.
So, how do you avoid this trap?
A good solution is to force yourself to develop additional hypotheses from different angles before the validation begins:
- Alternative hypothesis
- Second-order hypothesis
Together with the main hypothesis, I call this the 4-hypothesis framework.
“Why might the idea not work, or worse, have a negative impact?”
A counter-hypothesis acts as a sanity check. If you can explain why your idea might fail, and the existing evidence already supports this rationale, you might want to re-evaluate the idea’s priority.
You will also get a sense of how “risky” the idea is. In other words, is there any chance that it can hurt your product?
The risk level informs you on how to run the validation:
If the risk is high, you might want to start with a smoke test, close beta, or small-scale A/B test.
If the risk is low or non-existent, you can do a full-on A/B test or launch it directly.
The purpose of counter-hypothesis isn’t to stop you from testing new ideas. On the contrary, it is meant to help you prioritize high-quality ideas and test them with confidence.
Even if an experiment fails, which happens 86% of the time, you will have a potential explanation to base your next idea on.
“Could the idea work, but not for the reason I thought?”
We often focus on validating whether an idea works but not on why it works. However, the latter is just as, if not more, important.
You might be wondering, “If the idea works, why does it matter?”
Well, it doesn’t…if this is the only idea you will ever have. But I doubt that’s the case.
Successful products are built on top of continuous, cumulative learning. Every validated hypothesis helps you piece together a wider product strategy. If you attribute your past experiment success to the wrong reason (a form of survivorship bias), your future decisions will suffer.
However, it’s not always easy to validate “why” an idea works. That’s why you should test a hypothesis from multiple angles, such as combining behavioral (what users do) and attitudinal (what users say) methods, before making it part of your long-term product strategy.
“Could the idea work, but introduce unintended effects?”
Besides the primary outcome you hope to achieve, an action can also introduce second or third-order effects:
- Raising the price might hurt acquisition and retention.
- Tailoring the onboarding flow for one group of users might harm the experience of another.
- A new feature might expand the product value but increase product complexity.
If you don’t anticipate possible second-order effects, you won’t know what to measure. As a result, those effects will go unnoticed, and you won’t be able to make a holistic decision. This often leads to a situation in which you “win the battle but lose the war.”
Example: Personal finance app
Let’s pretend you are a product manager for a personal finance app.
You have an idea to introduce “smart budgeting” as the default option over manual budgeting to increase the % of users who reach their monthly savings goals—a metric highly correlated with retention. You believe the best way to validate this idea is by running an A/B test among new users.
Most users don’t know how to set realistic, attainable budgets. Using machine learning to set budgets based on users’ demographic and spending data will produce better results.
Users might not understand what smart budgeting means. If they don’t trust it, they won’t use it. –> A complementary survey to understand users’ perception of the feature will be helpful.
The underlying AI might not be good enough. –> We need to run simulations first to make sure it performs better than manual budgeting.
Could the experiment work because smart budgeting has a simpler setup flow? –> We need to break down the funnel to measure Setup Started > Finished % and Setup Finished > Saving Goal Reached % to isolate the cause.
Could it be that smart budgeting optimizes for lower targets, so they will be easier to hit? However, hitting them will no longer provide the same value. –> We need to measure the retention rate of users who hit their savings goals to see whether the correlation weakens.
As you can see, these four types of hypotheses help you uncover extra considerations in your planning, so you can get the most out of the experiment.
It takes some practice to get a hang of thinking “against” your brilliant ideas, but once it becomes second nature, every experiment you run will return 2-3X the value.
Next time you plan to test a new idea, give this framework a try.