Most popular innovation practices are designed to speed up experimentation to learn from them. However, there has been significantly less focus on how that learning flows throughout the innovation process to support decisions, which is why we are experimenting in the first place. I have been lucky to practice and research innovation in various sectors which allowed me to understand, not just what to do, but why do it. through my PhD at the university of Cambridge I have immersed myself in how companies do digital transformation. I have been working with many of our industry partners to understand how to improve digital innovation success in terms of both the process and information flow. The findings i share in this blog are based on interviews, case studies, and pilot projects with multiple companies.
In this blog i will attempt to share my findings on how to optimize the speed and value of innovation projects. I would even argue that this approach is fundamental for improving the probability of success. In other words, aim for greater value to reduce risk. Counter-intuitive!
What does good innovation look like!
I have been puzzled by this question for a while, actually years. I finally found a meaningful answer. Now that i see it in front of me, it is painfully obvious. It basically states that a great innovation is one that clearly answer the 3 following questions positively:
- Is it desired by the customer?
- Is it feasible to do?
- Is it commercially viable to run?
Some people call it the innovation venn while others call it desirability, feasibility, and viability. I was not able to trace its origin but it is mostly associated with IDEO’s design thinking approach.
So, a great innovation is one that combine those three attributes. But, what is a good innovation process to get there. I would define it as one that allows you to achieve success with speed, low cost, and tangible evidence of its performance against desirability, feasibility, and viability.
This is such a great way to evaluate the success of innovation. Unfortunately, the hard thing about innovation is that you only recognize it when you get it right. This criteria is focused on evaluation and has very little on execution. Enter “The Lean Startup”, an innovation process pioneered by Eric Ries.
Experience is not sufficient, Fail fast to Succeed sooner
There is a funny relation between experience and innovation. Good experience means that you are great at what you already know, opposite to innovation which is new by definition. This doesn’t mean that companies can’t be great at innovation, it only means that you won’t have all the answers beforehand. Innovative companies are ones that work to reduce this uncertainty both systematically and rapidly.
This is the most fundamental idea that The Lean Startup contributed to revolutionizing how modern startups are built. It is also as applicable to multinational corporates which is the core contribution of “The Startup Way” book. Because guess what, innovation is always new regardless of experience. There is a wealth of great innovation practices in both books, I highly recommend them!
The scientific process of The Lean Startup methodology is a great way to experiment with innovation. The goal is to build a Minimum Viable Product (MVP) to test your ideas before deciding to invest further. While no plan withstand the first contact with reality, the goal is to maximize learning to increase the probability of your next MVP success. Once you find yourself in the awesome spot of desirability, feasibility, and viability then it is time to scale up. There are two guidelines here:
- If your MVP takes more than 3–6 months then your MVP needs an MVP
- Always set measurable goals before building an MVP. If you wait and see, guess what, you will always see something
Fail fastER, Fail MORE often to Succeed EVEN sooner
For a very long time i was convinced that The Lean Startup method is the only way of speeding up innovation cycle until i learned about “Design Sprints”. Design Sprints were pioneered by Google Venture to help startups validate their ideas fast, and here is how i explain it. If the whole idea from building an MVP is learning then why do we have to build it in the first place, can’t we just mock it up! It might be a good idea to iterate on this until we have evidence from the customer that this is a desirable innovation before building it. Luckily a Design Sprint only takes five days. The speed and cost of iterating is way more affordable.
From working through multiple jobs, i witnessed firsthand how Design Sprints can speed up innovation, demonstrate tangible productivity, and increase willingness to collaborate. However, after the Design Sprint workshop is over, the innovation honeymoon tends to slowly fade away. You are suddenly trying to swim against the organizational politics tide. There is very little guidance on what to do next. The next section is my contribution to this challenge.
Bringing it all together for optimized speed & value
Innovation is a search journey for answers. Just like any search, it is constrained by time, cost, and unknowns. This is so fundamental that it is what a “search algorithm” even tries to minimize. It is well established that the cost of design change increases over time, even if you were agile. Therefore, it is safe to say that the earlier you can get to Desirability, Feasibility, and Viability (DFV) fit the higher your probability of success. Creating such a trail of evidence is also helpful to get further funding.
Design Sprints are great for moving very fast from a vague idea to a solid concept. However, there should be a stronger emphasis on explicitly stating your idea hypotheses. As part of my research i have created a basic template that helps with this. The goal is to list the most important 3 features that shapes the idea and state DFV below it to define the evaluation metrics.
Another advantage of design sprints is answering desirability very early on. In facts you only need to invest 5 days of your team’s time, thanks to the user testing activity. Apparently user testing would allow you to identify up to 80% of your idea’s design issues by interviewing 5 customers only. Nonetheless, I found that what you learn can be influenced by the design and quality of the user tests. Be objective and neutral to avoid hearing what you wish.
Once you get the design right then you would aim for an MVP. You still need experiment as the solution feasibility and commercial viability won’t be completely understood. Building an MVP would force you to think how the product or service would operate and hence test its feasibility. Viability on the other hand would require actually launching the MVP and learning from the market response. Data is king here, make sure that you set the right metrics and be able to track them. Here is a summary of the steps:
- There are three milestones to hit which are DFV
- Consolidate your idea and test its desirability through a design sprint
- Use the design sprint findings as an evidence to fund your MVP
- Limit your MVP to the top 3 new features to test feasibility
- Launch your MVP ASAP to evaluate commercial viability
- Use MVP metrics as evidence to fund your next MVP
- learn, improve and repeat until you are ready to scale up
Finally, although this approach should significantly increase your innovation probability of success, you won’t always succeed. Innovation success is the product of D*F*V, so if one is 0 then the product of all three is 0. If you get here then it means that you should pivot and try to solve your challenge from a different angle. The only thing worse than scrapping your idea is watching others do it for you, whether it is management or the market. Remember the golden rule, the smartest people in your organization have a lot to learn from the collective wisdom of your customers and market. success would require you to maintain patience, passion, and persistence for doing the right thing.
I am keen on learning more. Please share your challenges, thoughts, and requests for blogs around digital innovation!