Canvas: Build your Design Hypothesis the right way
- Dec 25, 2021
- 3 min read
Updated: Feb 3
A simple canvas to create an effective and aligned hypothesis for your creative work
A hypothesis has a very simple structure. It's an idea, taken from wild form, and domesticated into accountability. It's an idea with its possible consequences. In a way, while ideas are dreams, hypotheses are plans. But in order to be effective for a business — that is, to save resources, accelerate goals and affect customer behaviors — it needs to include a few elements that are not part of the "scientific hypothesis". These elements, included here, will save you time and ensure your design task will yield a business-oriented result. It doesn't matter if you are designing a new app idea, a change on your website, or a new business-to-business solution. This structure helps you to ensure you are keeping up with design thinking, and keeping your project human-centric, business-oriented and data-friendly.
Your target
When starting with your target audience, my usual hunch is to start with your best paying customers (if you deal with an existing customer base).
Why? Because you have reliable data on their behavior, and you will have solid knowledge of the best part of your current state. If you want to build the next level, start from the existing ground.
Some designers like to start with a blank page. I think that is crazy. No business contacts a consultant with zero knowledge of to whom they will take the next step. Previous knowledge, however, should not be a legacy weight, but historical data for analysis.
If you really want to target a totally new audience, spend time researching their online search behavior. Make traffic tests to new ideas and only then setup interviews, preferably with the ones who clicked your traffic test ads!
But either way, start with a delimited audience — and be prepared to shift the target (or your product, or your message, or your channel) if they are not interested in what you offer!
If it works, what happens?
Could this question be simpler? Hardly. So give also a simple answer.
This helps the work to be precise and measurable.
Instead of "IF we make our site more appealing", state "IF we change our website to a one-pager booking system".
It's objective and goes straight to business objectives and goals (learn the difference between these two here).
And instead of dealing with long processes of "best scenarios", "worst case scenarios", use the structure of the hypothesis to plan and prepare for outcomes.
Use this common sensical question to answer clearly and unmistakably: what does my customer get out of it?
If the benefit is unclear or hard to measure, you may come to the conclusion that it is NOT a problem worth solving.
Go back to your business analytics (number of customers affected, probability it will affect business) and rethink.
Possible outcomes
Lastly, benefit from scientific rigor to plan a null hypothesis, that is, if everything fails, what happens?
You will be able to start thinking of the worst case scenarios, and adapt your solution to avoid them. It will also help you to plan new experiments in case your null hypothesis prevails.
State also your desirable outcome (the alternative hypothesis): what if it works? You will be able to prepare clearly on accounting the outcomes, and improve the solution even if it works.
Set timelines and deadlines
Establish properly the time of your experiment to begin and to end. You may want to have enough time and subjects tested in order to reach statistical relevance. In business, however, decision are also made with most-likely-to-work solutions. Either way, manage expectations by setting the schedule for your hypothesis to be tested. Be generous. It should take the time it takes.
