Iterating on experiments is often reactive and conducted as an afterthought. A lot of time is spent producing a ‘perfect’ test and if results are unexpected, iterations are run as a last hope to gain value from the time and effort spent on the test. But why subjectively try and execute the perfect experiment in the first instance and postpone the opportunity to uncover learnings along the way by running a minimum viable experiment which is then iterated on?
Experimentation is run at varying levels of maturity (see our Maturity Model for more information on this) however we see businesses time and time again getting stuck in the infant stages due to their focus on individual experiments. We see teams wasting time and resource trying to run one ‘perfect’ experiment when the core concept has not been validated.
In order to validate levers quickly without over investing in resource we should ensure hypotheses are executed in their most simple form – the minimum viable experiment (MVE). From here, success of an MVE gives you the green light to test more complex implementations and failure flags problems with the concept/execution early on.
A few years ago, we learnt the importance of this approach the hard way. Based off the back of one hypothesis for an online real estate business, ‘Adding the ability to see properties on a map will help users find the right property and increase enquiries’, we built a complete map view in Optimizely. A heavy amount of resource was used only to find out within the experiment that the map had no impact on user behaviour. What should we have done? Ran an MVE requiring the minimum resource in order to test the concept. What would this have looked like? Perhaps a fake door test in order to test the demand of the map functionality from users.
This blog aims to give:
- An understanding of the minimum viable approach to experimentation
- A view of potential challenges and tips to overcome them
- A clear overview of the benefits of MVEs
The minimum viable approach
A minimum viable experiment looks for the simplest way to run an experiment that validates the concept. This type of testing isn’t about designing ‘small tests’, it is about doing specific, focused experiments that give you the clearest signal of whether or not the hypothesis is valid. Of course, it helps that MVEs are often small so we can test quickly! It is important to challenge yourself by assessing every component of the test and its likelihood of impacting the way the user responds to an experiment. That way, you will be efficient with your resource and yield the same effect on proving the validity of the concept. Running the minimum viable experiment allows you to validate your hypothesis without over investing in levers that turn out to be ineffective.
If the MVE wins, then iterations can be ran to find the optimal execution – gaining learnings along the way. If the test loses, you can look at the execution more thoroughly and determine whether bad execution impacted the test. If so, re-run the MVE. If not, bin the hypothesis to avoid wasting resource on unfruitful concepts.
All hypotheses can be reduced to an MVE, see below a visual example of an MVE testing stream.
Potential challenges to MVEs and tips to overcome them
Although this approach is the most effective, it is not often fully understood, resulting in pushback from stakeholders. Stakeholders are invested in the website and moreover protective of their product. As a result, the expectation from experimentation is that a perfect execution of a problem will be tested which could be implemented immediately should the test win. However, what is not considered is the huge amount of resource this would require without any validity that the hypothesis was correct or that the style of execution was optimal.
In order to overcome this challenge we focus on working with experimentation, marketing and product teams in order to challenge assumptions around MVEs. This education piece is pivotal for stakeholder buy-in. Over the last 9 months, we have been running experimentation workshops with one of the largest online takeaway businesses in Europe and a huge focus of these sessions has been on the minimum viable experiment.
Overview of the benefits of MVEs
Minimum viable experiments have a multitude of benefits. Here, we aim to summarise a few of these:
The minimum viable experiment of a concept allows you to utilise the minimum amount of resource required to see if a concept is worth pursuing further or not.
Validity of the hypothesis is clear
Executing experiments in their most simple form ensures the impact of the changes are evident. As a result, concluding the validity of the experiment is uncomplicated.
Explore bigger solutions to achieve the best possible outcome
Once the MVE has been proven, this justifies investing further resource in exploring bigger solutions. Iterating on experiments allows you to refine solutions to achieve the best possible execution of the hypothesis.
- A minimum viable experiment involves testing a hypothesis in its simplest form, allowing you to validate concepts early on and optimise the execution via iterations.
- Push back on MVEs are usually due to a lack of awareness of the process and benefits they yield. Educate in order to show teams how effective this type of testing is, not only in gaining the best possible final execution for tests but also in utilising resource with efficiency.
- The main benefit of the minimum viable approach is that you spend time and resource on levers that impact your KPIs.