The Optimal Confidence Point

Stromi Lof
5 min readNov 3, 2020

Earlier this year, our team have built two healthcare products/services in less than two months during the height of the Covid-19 pandemic. Using a Minimum Viable Product (MVP) approach, we had to make snappy decisions to be able to deliver value to users quickly.

In many industries such as healthcare and during a time of extreme demand for digital health products, there is no choice between delivering products fast and getting it right— products should be delivered timely and should be right (meaning they should deliver the intended value to patients and being clinically safe).

The Optimal Confidence Point

I wanted to capture some of the principles we used to successfully build products within the following conditions:

  • Unpredictable environment
  • Very little time
  • ‘Noisy’ environment with lots of ideas, suggestions, questions and challenges about our approach

I believe that product teams need to build sufficient confidence to be able to succeed. Then looking back at this period where our team had to work smartly, I started to identify some patterns and I came up with a umbrella term to gather all of those….The Optimal Confidence Point.

The Optimal Confidence Point is:

  • the sweet spot when the product team build sufficient confidence to know that their solution is viable and would deliver value to users/patients and business alike through the MVP .
  • also a framework that help teams prioritise and document product decisions.

At this point, it is worth noting that a product vision or a product strategy should have been drawn and agreed upon so there is a shared understanding across the team of what the product is trying to achieve.

The Optimal Confidence Point is based on five pillars (and their counterparts) that define what the product and its functionalities should aim for, to ensure that the solutions achieve the product strategy and deliver value.

This framework can be used at a product level and but can also work at a feature level too.

The Optimal Confidence Point and its pillars

How to use The Optimal Confidence Point ?

These are the main use cases:

  • Define your MVP (Minimum Viable Product) — this framework will help identifying the core features of your product and determine what should be included and what should be excluded from your MVP.
  • Filter ideas — When building a product, there is usually a lot of suggestions coming from all directions — users, stakeholders, team members, other teams etc. As a product manager you need to filter out all of this to find out ‘What matters the most now’ and ‘What does not’?
  • Document product decisions — Keeping a record of the rationale behind each product decision, it is useful to explain to anyone why the product was built this way, it also helps defend the choices made and ultimately bring more confidence to the team.


Here is a example based on a true story and that falls under the ‘Filter ideas’ use case.

Context: Our team was developing a mental health digital service earlier this year.

Suggestion: “Why don’t we use this ‘use your location’ feature. It is tried and tested feature used across various other of our products, you should add it.”

Team’s reaction: It is a genuinely interesting suggestion — Being short on time and with increased pressure to deliver, our team could just implemented this.

But how confident are we about this? Prior to make any final decisions, using a framework like The Optimal Confidence Point helped the team evaluate the idea and ultimately make a rationale decision, which was backed up by evidence (findings from usability testing in this case).

Table with the Optimal Confidence Point pillars (functional, essential, valuable, realistic and content)
Before usability testing — Using The Optimal Confidence Point to evaluate an idea
Table with the Optimal Confidence Point pillars (functional, essential, valuable, realistic and content)
After usability testing — Using The Optimal Confidence Point to evaluate the same idea

Outcome: As shown above, we decided to discard this idea. Using this kind of approach, team can more easily defend their choices and draw a line about which idea they are going to address and which ones that won’t be addressed.

Filtering out ideas become less uncomfortable as the team can rely on a tool that gives them confidence in their decisions.

Applying The Optimal Confidence Point for product validation

The Optimal Confidence Point can be used to prioritise between :

  • what should be tested now: a small number of important things that make up the first version of the MVP (e.g. only test core functionalities with core users across main user journeys).
  • what can wait : non essential product components for the first version of the MVP

Then the team can validate small batches of ideas quickly and then focus on delivering these to users.

After reaching The Optimal Confidence Point — any new interesting findings has no value for the first version of the MVP (MVP 1) — As shown below.

Optimal Confidence Point: chart showing the learning curve against confidence level horizontally and time  vertically
The implications of reaching The Optimal Confidence Point for product validation & testing

It does not mean that the team should stop gathering new insights— these new insights should be taken into consideration for future iterations.

But it does mean that the team should prioritise fiercely and slow down any new insights generation activities that won’t benefit the first MPV.

What’s next?

Our team have been building other products since the fast-paced 2020 Spring period. The conditions seem somehow quieter now.

However, as a product manager, I’m still using The Optimal Confidence Point principles to ensure that our products deliver as much value as possible to users and patients.

The framework might evolve as I keep using it and if it does, I will make sure to share more here.



Stromi Lof

Product Manager. Electron Libre. Nomad. Made in Martinique.