What are the three stages of creation of value?

For leaders in business, having access to the right insights at the right time is critical to navigating the uncertainty ahead. Only rookies try to fly blind!

“Leadership requires creating conditions that enable employees to do the kinds of experimentation that entrepreneurship requires.”― Eric Ries

We gather insights primarily to make good decisions. Whether we are launching new value propositions, expanding into new markets, innovating with existing customers, or starting-up whole new venture, quality insights are vital for building confidence, unlocking investment, and maximising our chance of success.

Though there are many learning activities within business, I propose we can break down our learning needs into three stages, based on the kind of decisions we need to make:

• Discovery — uncovering problems that we could solve
• Validation — proving that we can capitalise on opportunities
• Optimisation — measuring and improving our solutions

Though these stages take us neatly from problem to solution, if we zoom out over a business’s lifecycle we find that the relationship is more circular.

For example: after the initial success of an offering, a business may want to expand into adjacent problems, launch in new markets, or tailor solutions for specific segments.

Discovery

When embarking on any endeavour, whether a new business, product, or service, it is important that we have a real and clearly framed problem that we are trying to solve.

For our problem, we want to gather insights about:

• Need — Is the customer need pervasive and strong enough?
• Value — Is the potential value of solving the problem big enough?
• Cost — Is the potential cost of solving the need reasonable?

Discovery learning often starts with generative research to identify core needs. These needs are then quantified with business and technical analysis to understand if they are worth solving and can be solved respectively.

If we identify a need where we have a positive answer to all three questions, we have discovered a good business opportunity, a problem that is worth solving. We are ready for validation!

Validation

Validation learning is a fast way to prove that an opportunity exists, and that you can find an effective approach to capture it. This helps us build a solid business case for both developing a new offering or launching an existing offering into a new market.

For our proposal, we want to evaluate:

• Proposition— Does the value proposition resonate with our target audience? Do they perceive it meeting their needs? How likely are they to try it out?
• Scope— What is the most valuable aspect of the proposition? What are the table stakes? What differentiates us from the market?
• Market — Who is our initial audience? What channels can we use? At what cost and speed can we reach them?

Typically we answer these questions through a lean combination of qualitative user testing, quantitative experiments and business analysis, being synthesised together into a recommendation. Validation learning isn’t about spending time trying to find the perfect solution, but instead quickly proving that the problem can be solved effectively.

Once we have the insights to make a clear case to continue or abandon the proposal, we have achieved our validation learning goal. If we get significant negative responses, chances are our problem is not strong enough, or not framed correctly. This can be resolved by doing more discovery learning.

Optimisation

The goal of optimisation is to continue to iterate and improve our solution, to build product-market fit, and increase our market share. To make good decisions, we want to shift conversations away from personal opinions and into more objective discussions based on evidence.

For our solution, we want to understand:

• Impact— How is our solution performing? How does it compare to our goals and market benchmarks? How is this changing over time?
• Issues— What problems are we seeing? Where in our funnel are they occurring? What impact could we have if they are addressed?
• Alignment— What indicators of product-market fit do we see? What new features are being requested? What existing features are underused?

Optimisation learning can come from various kinds of data analysis, user testing, and experimentation, conducted in an iterative fashion. By building a detailed picture of what is working, what is not working, and what is missing, we can better inform our ongoing prioritisation, roadmap, and strategy.

It is always good practice to prioritise making optimisations in the highest impact parts of your funnel first. If you are seeing issues in the solution’s impact or alignment, further validation of the opportunity or discovery of the problem might be needed before continuing to optimise the solution.

Recap

We can break down our learning into three stages: discovering the problem, validating the opportunity, and optimising the solution. Depending on what we are trying to achieve with our business, each stage guides us towards insights that help us make clearer decisions overall.

In discovery, we want to uncover real customer needs, the value we can unlock, and the potential costs of solving them to help us better decide what problems are worth solving.

In validation, we want to understand how our proposition is resonating, what scope is meaningful and how to capture market-share to help us better decide how to capture an opportunity.

In optimisation, we want to continuously measure our impact, uncover delivery issues and check overall market fit to help decide how and where we can improve our offerings.

Let me know your thoughts below!

If you are interested in reading about specific topics regarding design, business or philosophy, let me know in the comments.

About me: I’ve spent the last decade leading teams in framing, building, and scaling new businesses with design thinking. As a design entrepreneur, I am curious, self-aware, and critical in my approach of taking people on the journey of lean impactful innovation.