2019 is a big birthday year for innovation. The Internet has turned 30 and the Agile Manifesto has turned 18. Both have led to rapid change and growth — particularly in the development of technology and software — and the startups and scaleups that have taken advantage of this increase in pace have decreased the lifespan of larger companies dramatically — a trend that is still accelerating. The average age of an S&P 500 company is under 20 years, down from 60 years in the 1950s, according to Credit Suisse.
Big companies know they need to adapt. Some have done successfully, many unsuccessfully, to borrow from the new breed of disruptor on the market.
So can lean startup principles work for corporates and enterprise businesses?
1) Customer Development. This process is used to eliminate waste in the initial development process by surfacing new insights like whitespace opportunities and changing any assumed focuses to data driven ones. The team are encouraged to get out of the building and meet real customers from your targeted profile upfront to validate who they are and the problems that they face. The advantage of this is that you get more accurate feedback, that is in context and you have an opportunity to immediately ask any follow-up questions that you might need. It is also a good indicator of how many of these customers out there and how easy they are to find.
Yes — This is an easy process to transplant into the corporate environment. It simply involves moving processes outside of the building and meeting directly with your target customer — and not relying on traditional forms of customer analysis and information gathering like surveys and other formal market research methods like consumer surveys and focus groups that take place either online, by post or phone and often by another external company or team.
2) Validated Learning. This process simply means hypothesising ideas and testing any assumptions quickly, so you can iterate towards a successful solution faster and at less expense. It is the classic build, measure, learn cycle. The core of this process is data and the successful application of that data to your methodology and your decision-making. This should be the most natural step for corporate to adopt and implement because big companies like big data.
Yes (if you really want to) — The main problem big companies and corporates come across in this part of the process is bureaucracy and a lack of willingness by experienced executives and research and development departments to relinquish control and set aside what they know from their expertise, experience and gut instinct to rely on what the data tells them to do — especially when it goes against the norm.
3) Minimum Viable Product. A Minimum Viable Product is the minimum you need to test if something will work and is wanted by your market. An often cited example of this is the creator of Zalando, now one of the biggest shoe websites in the world, who started by simply taking pictures of products for his website and manually purchasing and resending the stock himself until he could prove there was a demand for his product. Tesla initially used their early-generation lithium-ion batteries in Lotus cars, generating valuable feedback at a fraction of the cost if they build their own car from scratch.
Yes and No — Obviously this does not work well for some products (especially packaged goods), however, this method is proven to be invaluable in the production of software and technology — saving time, money, and energy. You create elements of the proposition that can be developed and tested within the market to make sure it is good enough, making sure customers can see the value in spite of any initial flaws.
4) Innovation Accounting. Big companies have big cycles in place. Most processes are reviewed every year, some not for several years at a time. To maintain the agility that smaller companies have, you have to measure and adjust far more often. Quite often startups and scaleups are almost always working in Sprints.
Yes — Larger companies can adapt by simply setting goals and growth objectives that can be measured and used to adjust. There are plenty of commoditised tools that can make live-tracking your data and analytics possible, as well as more sophisticated custom solutions that can integrate into your core infrastructure and automate huge amounts of this process.