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To approach a new idea with RAT enables you to take a closer look at the market, your idea and target customer base before building and shipping a minimum viable product (MVP). Essentially, it validates and tests the riskiest assumptions of an idea to give a clearer view of what will work and what won’t so interest can be gauged and crucial changes can be made before launch.
The concept is based on the fact that often we can believe something without adequate supporting evidence. Congruence bias is a type of confirmation bias that describes our ability to jump to conclusions and think we’ve answered a question, when in reality we’re simply over relying on the information that has already been presented to us.
In order to truly test an assumption we must engage in direct and indirect testing, we must put theory into practice, and we must be open to challenging our current mindset. While RAT might require more planning and strategising upfront, it can save significant amounts of time and money as you move into the build and shipping phases of design.
Creating a Minimum Viable Product (MVP) is a popular way to test an idea fast. While this has its place, it isn’t necessarily the best or only way you can approach your product or service rollout.
There are some key differences between a MVP and RAT. First, when it comes to creating an MVP the focus is on speed - how quickly can you build a bare bones version of your product and get it into the hands of your target audience? It’s all about building fast, then measuring and learning.
When it comes to RAT, your focus is to learn and measure, and then build. In this instance, you’re more likely to launch your product with its best features, not only those that are the quickest or easiest to build.
RAT is all about understanding product-market fit before the product itself is ready to go, with the idea that by the time you’re in the build phase you know you’re creating something customers want and are willing to pay for.
In a startup post-mortem analysis, CB Insights found that 42% of startups fail due to lack of product-market fit. So ask yourself, what assumptions are you making, and what information do you need to test those assumptions? Your biggest risk is to make a product or service that no one actually wants.
There are some core areas to consider. We can narrow this down to some key questions:
Of course there are many more questions we could include and your list will look distinct to you, but this is a good place to start to begin to think about your idea more deeply.
As you begin to test your assumptions you may find your product is strong but is better suited to a different segment of the market, you may find it solves a different problem entirely, or you may realise there’s no fit at all. Still, in our experience this form of testing brings a lot of valuable information that can be utilised in subsequent builds and design.
A simple way to categorise your assumptions is to put them into three main groups: Problem, Solution or Implementation. Problem is about the customer’s pain point and how you’re solving it. This is where you look at the customer type and the need, action or behaviour of this group. Solution is about the detail of the product or service including features, and focuses on solution behaviour. Implementation is the action you will take to solve the problem and considers the solution method.
There are many ways to test assumptions and gain quantitative, qualitative or measurable information that can lead to clear decision making. You want to put something in the hands of your users and get real feedback - but this doesn’t have to be code.
It’s important to know where your target audience is, how to contact them, and communicate in an open and authentic manner. Your test could be as simple as setting up a landing page and getting people to sign up to gauge interest, it could be running online surveys, or creating a video of the product concept and asking for feedback.
Regardless of the specifics, an effective experiment includes hypothesis, riskiest assumptions, methods and minimum criteria for success. A good rule of thumb is to write a pass or fail rating. This would look something like, ‘For this product to succeed, we need to get [X amount] of [signups] out of [Y amount of users] in the next [period of time].’
A healthy RAT model takes a cyclical approach. You learn and gather data through experiments, measure data collected, build your product or service, iterate and continue. When you’re engaged in the validation process, the focus will shift from ‘I think’ to ‘we have evidence’.
As is the nature of going to market with an idea brought to life, we can never know 100% how it will be received, but we can lower the chance of getting it wrong and greatly increase our chance of success.
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