Don’t let perfection be the enemy of progress. In any early stage business, speed to execute is paramount.
Nothing is ever finished and nothing is ever perfect.
It is the essence of entrepreneurship is to have grand plans without consideration for what resources you have right now.
Our approach is to support founders with a framework for decision making focused around progressive moves towards a vision, without being blocked by what is possible today.
Born in agile and grounded in continuous improvement, we call these learning loops.
Questing for perfection may be a manifestation of the fear of starting. There is no glory in starting, and it feels deeply uncomfortable to put yourself out there.
Learning loops for fast time to value
The purpose of learning loops via a series of smart experiments is to continuously establish and implement ‘hunches’ that your team has for ways in which you can improve your Acquisition, Activation and Retention and ultimately, the revenue of your business.
We counsel founders to embed these principles into our workflow, while delivering “quick wins”, and identifying high value opportunities for larger/longer term value creation.
The point is for everyone to ‘always be testing’, to enable an environment where coming up with a hypothesis, running a test to validate it and concluding with the results from that test is standard practice.
Prioritise testing
The most critical element is to ensure that whatever is being tested is prioritised. Some considerations include:
- What is the potential Reach of this idea?
- What is the potential Impact of this idea?
- How Confident are we that this can be done?
- What Effort is involved?
Step 1 - Identify problem areas
- Where are the kinks in the funnel (pirate metrics)?
- What do we think the problem is? (User research)
Step 2 - Workshop solutions
- What can we do to solve the problem?
- Aim here is to form the basic hypothesis (“by doing X, I expect Y result”)
Step 3 - Prioritise solutions
- Put into a RICE-style framework (reach, impact, confidence, ease)
- Involves some rigour! Are our assumptions valid? What evidence are they based on? How are we justifying confidence levels?
Step 4 - Find the MVP
- What is the quickest/ easiest way to validate our hypothesis?
- We should from a strong hypothesis at this stage, (ie. “If we are right I will scale the programme, if we are wrong we will move on to X.”)
Something is always going to be not working
What are you going to focus on, and what are you going to leave to burn?
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