Future Work/Life is a weekly newsletter that casts a positive eye to the future. I bring you interesting stories and articles, analyse industry trends and offer tips on designing a better work/life. If you enjoy reading it, please share it!
Over the past couple of weeks, I’ve immersed myself in research for the book, which for all you new subscribers is called Work/Life Flywheel and is coming out in Autumn 2021. Helpfully, podcast interviews for the new series form part of that research and on last week’s show I discussed themes like testing out new ideas, and why scrappiness can be a positive when starting a new business with my guest Elizabeth Ogabi, author of Side Hustle in Progress.
As ever, the conversations inspire me to expand on the ideas in this newsletter, some of which will, in turn, make their way into the book - a content flywheel, you might say. And since I’ve been so productive on the writing front since the last edition of Future Work/Life, I’m splitting this week’s article into two parts - the second half will follow on Thursday.
In the meantime, I’m now looking for more contributors to interview for the book and would love any suggestions from you. If you know anyone who may be interested in speaking to me about topics like entrepreneurship, creativity, the impact of technology on work, establishing innovative and flexible cultures, career transitions and, of course, designing your work/life for success, please hook us up.
Cheers,
Ollie
Don’t fail fast. Learn fast
The chances are that at some point in your career, a fear of failure has stopped you from pursuing an idea. If that idea is starting a business of your own, then I'm sure being bombarded with stats like '90% of start-ups failing', or '20% of companies going bust within the first year', won't have helped.
Of course, if we all listened to the doom-mongers, we'd never start anything, and to counter this narrative, the common refrain in the start-up community nowadays tends to be, 'fail fast'. While all success stories are tinged with survivorship bias, there are plenty of intriguing examples of this approach working.
In 2006, Joe Gebbia and Brian Chesky launched a new website, which they'd designed to allow locals to rent out their spare beds while large conferences were taking place nearby. Soon afterwards, they discovered that people found it awkward exchanging money in person, and their new business idea stalled.
The two designers quickly began testing online payments, and they immediately saw a spike in new users. That was when Gebbia and Chesky - now Chief Product Officer and Chief Executive of AirBnB - realised they had a viable business model. As well as receiving a 3 per cent service fee from hosts, which covered the costs of online payment processing and funded the operation of the platform, they realised they could also charge between 6 and 12 per cent as service fees from guests.
So, the first iteration of their offering didn't work, but is 'failure' a fair description? No, a more accurate representation would be that they 'learned fast', which is also a far better lens through which to view effective innovation more broadly.
A culture of experimentation relies upon acknowledging that you rarely arrive at the perfect answer the first time. However, it also depends on creating a support structure that ensures 'failure' is never catastrophic. Successful organisations don't go all-in on an idea based on the hunch of one individual, much like you wouldn't invest all your savings without thoughtfully considering the possible downside.
If the objective is to learn, how do you design an experiment to optimise for doing it quickly?
Testing hypotheses
Cast your mind back to when you were twelve years old and first walked into a science lab at school. If you were anything like me, the opportunity to light up the Bunsen burners and set some sh*t on fire was one of the highlights of the week. In hindsight, this is worrying behaviour, but leaving that aside for a moment, your teacher would not have allowed you anywhere near the equipment before declaring a hypothesis about the experiment – a clear and measurable prediction of what you thought would happen.
The same is true in business – we should design experiments that consistently deliver actionable insights, irrespective of the results. That means we need to ask the right questions in advance:
- What are your underlying assumptions about the problem you're trying to solve?
- How will these assumptions play out?
- What effects might they have on the business or, for that matter, your work/life?
In the early days of Airbnb, for example, the hypotheses may have looked something like this:
'There's always a shortage of hotel rooms whenever a large conference takes place in the city. By enabling residents the chance to offer their homes and rent out their spare beds, we anticipate that 100 visitors unable to book a hotel will embrace the chance to stay with a local.'
When they proved that hypothesis, perhaps they then tested for scalability:
'As there's an appetite for the service, if we focus on increasing the total number of listings, we'll see a commensurate rise in bookings.'
When this 'failed', they would have to analyse whether the fundamental assumption – the more listings, the most bookings – was wrong or if it was some other factor. Through customer interviews (the customers being both hosts and guests in this case), they identified the problem lay with the awkward payment method, so their next hypothesis could have been:
'As people find paying face-to-face uncomfortable, we can remove the friction in the payment process by allowing online credit card payments, which will result in a five-times increase in listings, creating more options for potential guests and increasing the total number of bookings.'
While their hypothesis would have been closer to the result, they would have also realised unexpected upside because of the scale of new bookings and the additional revenue sources.
Look, I'm sure that isn't precisely how Joe and Brian's conversations went down, but you get the gist. The discipline of establishing a hypothesis forces you to think about both the desired outcome and possible reasons for failure – what some people call a 'premortem'.
Ensuring that the experiment is measurable ensures you have something against which to judge success – this is incredibly useful when assessing the quality of decision-making, which is notoriously tricky after the fact.
Any Other Business:
The debate rolls on over what to call the trend of people looking for new jobs. I’m not sure the name we give it matters as much as the reason people are deciding to leave in the first place, but the ‘Great Resignation’ is clearly a ‘thing’, even if some reports suggest more employees are ‘thinking about quitting’ than actually going ahead and doing it. What is most fascinating are the reasons driving it. Short-term considerations like competitive pay, poorly conceived hybrid working policies, and the increased demands of the job are, for now at least, more important than factors that typically come up in surveys, like autonomy and an opportunity for growth and progression in the role. I suspect this reflects both the residual effects of challenges arising from the pandemic and the lack of movement between roles during that time, but it’ll be interesting to see how it lasts.
As I’ve discussed many times, establishing positive company culture is not dependent on being in the same room at all times. In this Fast Company article, Jay Friedman from Goodway Group shares insights into how they’ve created a great culture virtually for over 13 years, which is reliant on “the behaviors and attitudes that you foster every day”.
I’m always interested in how people approach improving their productivity, although the truth is that no single action or system is enough to achieve positive long-term effects. Instead, one’s ability to be consistently productive is reflective of your overall lifestyle, autonomy to decide when, where and how to work, and relates to specific task factors like the challenge/skill ratio (in case you’re wondering, you should be aiming for a 4% difference between the challenge and your skill to achieve optimal flow and, therefore, significantly improve your productivity).
My geeky side is intrigued by people offering a different take on the subject, which is why I enjoyed Asish Parulekar’s analytical exploration of the variables that affect productivity in his Medium article.
Few businesses and individuals can say they’ve genuinely impacted humanity, which is why I enjoyed listening to Mike Maples’ interview with Stephane Bancel, CEO of Moderna, on Starting Greatness this week. They discuss the power of taking offensive risks to ‘seize unlikely futures with massive upside’ and what lessons we can learn from Moderna’s story to increase our own chances of creating significant breakthroughs.