The Truth Curve
How much time should you spend on experimentation relative to just building the product? Spend too little, and you risk not learning enough. Spend too much, and you may invest so much in the idea that course correction becomes increasingly tricky.
The Truth Curve, detailed by Giff Constable in his book Talking to Humans, is a visualisation designed to trigger the conversation to reconcile “How much do we know?” and “What’s the next step?”.
The Y-axis on The Truth Curve represents the evidence you have to support working on your current hypothesis. The higher you go, the more confident you are that the hypothesis is valid.
The X-axis represents an increasing effort dedicated to the experiment you’re running to test the hypothesis. The further to the right, the more time, effort, and money you spend on the idea.
The green curved arrow (labelled ‘Confidence’) is the Truth Curve, representing the path you should follow as they test their hypotheses. Collect evidence and recognise where you are on the curve.
If your work falls above The Truth Curve (lots of evidence, lower investment), your team is at risk of analysis paralysis. You are potentially over-investing in testing and learning at the expense of building a solution.
Suppose your work falls below the Truth Curve (lack of sufficient evidence, heavy investment). In that case, your team is taking unnecessary risks by building out a solution that does not yet have the evidence to justify it.
If you start working on a new hypothesis without significant previous evidence, you are on the far left of the diagram in The Land of Wishful Thinking. You believe that your hypothesis is correct, but you lack evidence to support that conclusion. You want to start with low-cost experiments such as customer interviews or paper prototypes, which can provide some evidence to support your belief and justify further investment in experimentation to learn more. As you begin to collect positive evidence from experiments, your confidence level increases, as does the justification for a more significant investment in the following experiment.
As you move up and to the right on the Truth Curve, the questions you are asking will change. The focus will shift from “Should we build it?” to “Can we build a viable business with it?”.
You are trying to find a problem/solution fit on the left-hand side. Does the thing we plan to build solve a problem for a customer? If the evidence suggests “yes”, then you continue towards the right investing and experimenting further.
Eventually, the focus will shift to product/market fit. Is this a viable business for you to pursue and invest further? Sometimes, there will be a problem/solution fit, but a viable business model will not support it, so we choose to pivot away or stop.
As we move up the Truth Curve and start to get negative feedback about our hypothesis, we may need to stop. If the findings point to flaws in your assumptions, we must reassess whether this idea is worth pursuing. We must pivot away in a different direction or kill the idea altogether.
This can be challenging in traditional organisations where admitting failure can be perceived negatively and have consequences for those involved. Framing this as an investment in learning that eliminates significantly higher waste further down the line is essential. It is something to be grateful for! We have learnt something important at a low cost that will prevent us from developing a low-value product.