Innovation is a key driver of growth and is only going to increase in importance for organisations and the economy as we look to come out of this pandemic. Yet innovation is hard, risky and rarely successful.
In a study cited by Robert G. Copper in his book Winning at New Products he reveals that ‘for every seven new product ideas only 1 succeeds.’ And “most startups fail” says startup guru and author of the Lean Startup, Eric Ries.
Why is this? One reason is that our traditional approach to managing business as usual projects isn’t suited to innovation. Where the traditional business plan relies on certainty, clear facts and data, and little change, when innovating we are dealing with uncertainty, complexity and ambiguity. The ideas that we are creating are new and don’t exist yet, so there is little to no data on them to build an accurate and realistic business plan. Furthermore, the business plan is also very time intensive and costly, and in innovation speed is of the essence and we’re often innovating on a tight budget.
With the pace of change we are seeing today in an increasingly volatile, uncertain, complex and ambiguous (VUCA) world it’s not just innovation that needs a new approach, but even so called ‘business as usual’ projects can no longer rely so heavily on traditional methods.
So how do you validate ideas when there is high uncertainty and ambiguity, and little data or hard evidence?
The experimentation method
A faster, less expensive and more accurate approach for innovation and managing in VUCA is to take an experimentation method to choosing which ideas to take into development and commercialisation.
Experimentation is a process of continually generating a broad range of hypotheses, prototyping and testing them in fast small-scale experiments, and feeding the more successful concepts while pruning the failed ones.
Here are six steps for running business experiments:
Map the business model
You want to start by mapping out your idea into a Business Model Canvas. This helps you look at your idea at a business model level and capture the four main areas of developing a new business or innovation – customers, offer, infrastructure and financial viability. At this stage you won’t have all the answers; you’ll have to make some informed guesses. More on these guesses soon.
In Steve Blank’s book The Startup Owner’s Manual, he states ‘Every business model has a degree of uncertainty. Whether it is a new product, market or technology, each adds risk.’ The focus on this step is to efficiently identify which of our assumptions (guesses) pose the greatest risk, so we can then systematically de-risk the business model through experiments.
To identify the riskiest assumptions I use a Risk Matrix, which plots assumptions from low to high uncertainty versus low to high importance. The riskiest assumptions, which should be tested first, are those that are both highly uncertain and highly important to your idea. It’s then important to write them up as hypotheses that can be tested as true or false.
Now we can start designing experiments to prove or disprove our hypotheses, de-risk our business model and validate the desirability, feasibility and viability of our ideas.
To design experiments I use an Experiment Brief which helps teams articulate: what they want to learn, what type of prototype and test is required, who the customer is, success criteria, duration of testing and action you will take if it passes or fails.
The key is to identify the most efficient way to test the riskiest assumptions and gain validated learning.
Prototype and test
The next step is to build the prototype in the minimal form required to test your hypothesis and then run the test as expediently as possible. You test your prototype with the target customers identified in the Experiment Brief. The type of experiment your run will depend on what you want to learn. At the start of the innovation journey uncertainty for your idea is high, so your experiments should be low-fidelity and low-cost, keeping the cost of failure low. A prototype doesn’t usually have to be very complex in order to learn what you need to know. In fact, you’ll be surprised at how much quality feedback a customer can give you on a storyboard sketch that is far from perfect. As you progress and certainty increases you can spend more on higher fidelity experiments.
Following your tests you need to analyse the results to see if you’ve validated or invalidated your hypotheses and to identify key learnings. The more vivid and understandable your results, the more definitive they’ll be for you and the more convincing they’ll be for stakeholders.
In addition to validation of your hypotheses you want to capture the following: what the customers liked and disliked about your idea, any suggested improvements, and new questions, hypotheses and ideas to explore in the next iteration.
You can then make an informed decision on how best to proceed – whether you should progress, pivot or perish the idea. Success also includes walking away from ideas that aren’t going to fly.
The next step is to update your Business Model Canvas and repeat the experimentation cycle.
At the end of this process you’ll have a short list of validated, robust and desirable concepts (solutions) with de-risked business models that are feasible and viable, as well as some that have been justifiably perished. Which is far more actionable than an expensive, static business plan made up of unproven assumptions.
Nathan Baird is the founder of customer-driven innovation and growth firm Methodry and author of Innovator’s Playbook: How to create great products, services and experiences that your customers will love! He is one of the world’s leading Design Thinking practitioners, a former Partner of Design Thinking for KPMG and helps teams build their innovation mastery and works alongside them to create new innovations. Visit www.methodry.com