Pretotyping tests are the perfect way to gain valuable market research for products that aren’t built yet. This could be a new product you plan to bring to market and you are looking for it’s product-market-fit or an extension to an existing product line and you want to find out what the pricing should be or the features it should have.
The main benefit to using Pretotyping over other forms of market research is that it is fast to setup and gives you high quality and reliable behavioural insight. All of this BEFORE developing any physical product or MVP. The cons however are that you need to have a clear idea of what you want are testing for.
Pretotyping is not great at giving broad data sets, it is perfectly designed to give you accurate insight into one specific variable at a time.
Have no fear though, we are here to help you setup your Pretotyping tests by first giving you a sense of direction.
This direction comes from your hypothesis, as is one of the most important aspects of setting up a quality Pretotyping test. It will help direct the research and give you measurable and meaningful results.
What is a hypothesis?
A hypothesis is an assumption based on insights that have been gathered through previous research. It is not a guess on what you think is going to happen in your upcoming research, but more a theory on what the results will be for your upcoming research based on previous data gathered.
The data gathered doesn’t have to be extensive to start building a hypothesis, it doesn’t even have to be primary research, you can use existing data sets if needed. What matters is that you aren’t going into Pretotyping tests guessing what will happen, you have an idea of what you expect, then Pretotyping can validate that.
There are 6 forms of hypothesis that you can create and each have a particular purpose when running market research and gathering consumer insights. Let’s first look at the statements that aren’t ideal for Pretotyping and why that is:
1. Alternative hypothesis
This type of hypothesis statement will outline what you believe the outcome of your testing will be, based on previous insights of course. There are 2 subcategories within this that give it a direction or not.
For example a directional hypothesis in Pretotyping would look like this:
Launching a blue version of this product will improve conversion rates
A non-directional hypothesis would look like this:
Launching a blue version of this product will influence the conversion rates
Alternative hypothesis statements aren’t particularly useful when it comes to Pretotyping. As mentioned earlier in this article, Pretotyping excels when testing for specific areas and data sets. Running with a hypothesis like this will only make it harder to analyse the results later on.
If you did decide to run your tests based on this type of hypothesis, you would be looking to use the directional version so it gives the test a better focus. The only way this works is because you have the direction of where you think results will go AND the dataset you think will be influenced.
2. Null hypothesis
The null type hypothesis is an opposite to the alternative hypothesis. You will be looking at what won’t happen, or more specifically how this test won’t change any of the outcomes defined in the variables.
For example a null hypothesis statement in Pretotyping would look like this:
Launching a blue version of this product will not change the conversion rate
This type of statement is similar in use to the alternative hypothesis, it lacks the required focus to be truly effective for Pretotyping and there would be better ways to build a hypothesis for your tests.
3. Simple hypothesis
Simple hypothesis are just that, simple. They have little depth to them and just outline the relationship between the variables.
For example a simple hypothesis in Pretotyping would look like this:
Releasing new product colours increase conversions
There is very little use for a simple hypothesis in Pretotyping, it would be better to use an alternative hypothesis over this type.
4. Complex hypothesis
Despite it’s name, it isn’t that complex to create a complex hypothesis. It is similar in style to the simple hypothesis but instead has more variables to relate.
For example a complex hypothesis in Pretotyping would look like this:
Releasing new product colours will increase conversions, decrease customer acquisition cost and increase the size of our target market.
This type of statement is again not great for Pretotyping for similar reasons to the simple hypothesis. But it is actually worse than that.
Because of the way Pretotyping works, you want to run each test with as specific a goal as possible, if you are trying to test and measure multiple metric changes at the same time, you are likely to develop a test that is very complex and so you can’t easily attribute the increase in conversions to a particular area of the test.
5. Empirical hypothesis
This hypothesis is created as theories are still being developed and validated. It includes enough detail to make it relevant but without being too off course when the final insights are gathered.
An example of an empirical hypothesis in Pretotytping would be:
Launching a blue version of our product will increase the conversion rate more than launching a yellow version
It is great to use this type when you don’t have much data to start with and are just getting traction with your insights. Within Pretotyping, it isn’t the best option but gives you a good starting point to run tests against.
The reason for this is that you have a direction that you are testing, i.e. is a blue product better than a yellow product, and a variable to measure that against, i.e. conversion rates.
We’ve looked at the hypothesis statements that aren’t ideal for Pretotyping and discussed why, so let’s look at the best type of statement you can use.
6. Statistical hypothesis
This type of hypothesis is based purely on data you have already collected or verified through other sources. It is a logic-based analysis where you research into a specific area and gather insights based on that sample group.
An example of statistical hypothesis in Pretotyping would be:
Launching a blue version of our product will increase the conversion rate from 5% to 20% in the age group of 16 - 24 year olds
As you can see, this is our most detailed hypothesis yet. It outlines what we are looking at, launching a blue product, how that is going to effect our variables, conversion rate, and in which group of people that will happen, 16 - 24.
This is the perfect type of hypothesis for Pretotyping and what you should be aiming for. The problem is getting the data for your hypothesis, so let’s talk through that next.
6b. XYZ hypothesis
Similar to a statistical hypothesis, this type is endorsed by the father of Pretotyping, Alberto Savoia so is perfect for this task.
It structures your hypothesis in a way that gives it a lot of focus easily, all you have to do is fill in the gaps. Here’s how is looks:
At least X% of Y will do Z
As you can see, all that is left is to fill in the XYZ of the hypothesis to have your final statement. An example of this would be:
At least 5% of supermarket shoppers will buy hand cream for €12 - 15