Use the Experiment Cheatsheet to find out the experiment you should run next
Erik van der Pluijm - Sep 02 2019 - 5 min read
|Time||± 30 minutes|
|Difficulty||3 / 5|
|People||3 - 5|
|Author||Erik van der Pluijm|
|License||CC BY SA 4.0|
In your innovation journey, there will be many moments where you haven’t got enough information to move forward with confidence. Experimentation will help you to find that information, by validating or invalidating assumptions. Experimenting takes time and effort, so it makes a lot of sense to be selective in what to validate first. But how to prioritize?
This Assumption Mapping canvas is a lightweight tool that can help you prioritize your assumptions and to find which one to try to validate first. A perfect tool to use before you start experimenting.
The first step is to collect the assumptions you want to map. Assumptions can come from anything. They come from your vision, business idea, things your customers say, your choice of business model… They can even be hiding in plain sight: things you take for granted or are biased about could be based on (false) assumptions. Finding assumptions is a big part of your job as an innovator.
To do this, keep a space on your innovation wall during your project where you put sticky notes for your assumptions. You could even use this canvas to map them directly. Convince your team to keep tracking assumptions.
An ‘assumption’ in this context is something that needs to be true in order for your project to be successful. Assumptions come in three categories.
For example, if you want to create a new app that helps people to invest their savings, some assumptions could be:
If you ‘collect’ an assumption, put it on the wall. Use color coding to figure out if it is a viability, feasibility, or desirability assumption. Finally, also put on the sticky what parts of your idea would be affected if this assumption were to be invalidated. Would it mean a small adjustment? Back to the drawing board? Or Game Over?
If you keep doing this during your other brainstorming, visioning, and customer understanding sessions, you’ll quickly end up with a lot of assumptions.
If you have a fresh start, look at your vision, customer journey and personas, and your business model canvas or lean canvas to get a first stab. Go over these with your team and try to find the (implicit) assumptions you make. What would need to be true for your idea to work?
The next step is that for each of your assumptions, you want to have an ‘evidence level’. It makes little sense to focus on experiments for assumptions that have a high evidence level.
Once you have a wall filled with 10-20 assumptions, it’s time to start mapping them. Make sure each assumption is color coded for its category (desirability, viability, feasibility). Also, make sure you have labeled each with ‘no impact’, ‘small adjustment’, ‘back to the drawing board’, and ‘game over’.
Place the sticky notes on the canvas, on the vertical axis. On top, put the ones that have ‘game over’. On the bottom, the ones with ‘no impact’ and ‘small adjustments’.
Next, move the sticky notes on the horizontal axis. THe stickies with ‘high’ and ‘very high’ evidence move all the way to the left. Stickies with ‘low’ evidence move to the right. Find the appropriate position for each.
There are in essence 5 big groups that each post-it can end up in. Look at the checklist below, and find the post-its that are low-hanging fruit or that are high-risk. Use that to inform your choice.
In some cases, multiple assumptions end up close together in the top right quadrant and a discussion will happen. To make this a bit simpler, try to do the following:
Warning! In some contexts the order desirability, viability, feasibility might not be true: feasibility could come first. For example, if you already know that laws of physics prevent you from building a anti-gravity device, proving its desirability won’t help you much. The same can be true for contexts with high compliance requirements: if you know it will be against the law, you might want to fix that problem first, before looking at desirability.
Use the result as input for your next experiment.
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