The Equity Kicker

This morning I read a good post from YC Alumn Vibhu Norby who describes the dangers of a big product launch. He had everything set up for success – 25k people signed up to be notified about the launch, forthcoming Techcrunch and AllThingsD articles, a great social media plan, etc etc but his initial bang was quieter than anticipated, they didn’t get top of the app store charts and then new user sign ups trended down over the next few days not up. The lesson that Vibhu took away is that it’s best not to have a launch at all. At Origami, his new startup, they simply put the service live and are focused on making their small number of users happy, and letting the user base grow by word of mouth.

Big launches are fantastic if they go well, and if you are hell bent on taking the quickest path to wealth and fame then the lure of the big launch will probably be irresistable, but most big launches fail, and that failure comes with a cost. Big launches come with high expectations and disappointment is hard to recover from – employee morale will suffer, investors may lose faith, you may pick up a hard to shake bad reputation with the press, and possibly worst of all, you may lose confidence yourself.

Why do most big launches fail? Because very few companies are clever enough, or lucky enough, to have a good product market fit on launch day. For most it takes a period of iteration as customers use the product before they find the sweet spot.

You could say that choosing to have a big launch for a startup is adopting a strategy of ‘be lucky’, and as we know, luck isn’t a strategy.

Next a caveat. If you are operating in a known market then the dynamics are different. In this case you have a much better chance of good product market fit out of the gate and a big launch might make sense. The new Samsung S4 is a good example here.  Also, companies operating in very crowded markets may not be able to think of another way to rise above the noise (although that raises the question of whether they should find another market….).

Wrapping up – the ‘big launch’ vs ‘build slowly from a passionate user base’ is one of many areas where startups can choose between being aggressive about getting as big as they can as quickly as they can or focusing on sustainability first and fast growth second. I’m all for shooting for the stars, but for me the second path is the better one. It locks down value more quickly and relies less on luck. It’s absolutely right to keep open the option of being lucky, and if a lucky break comes to double down, and double down hard, but going to early and relying on being lucky just isnt sensible.

Clayton Christensen has similar advice when he talks about going for profitability first, and scale second, whilst the likes of Steve Blank and Eric Ries have a similar message when they say that finding product market fit is a startup’s first job. These are wise words from wise men, but it can be hard to know exactly when product market fit has been reached (how sure should you be?) and profitability turns out to quite complex too, as many startups reach the point when their inheret profitability is apparent before their management accounts are showing month on month profits. For me the time to invest hard to scale fast is when scaling doesn’t require any great leaps of faith, i.e. demand and marketing channels are proven at a small scale and there are no obvious barriers to growth.

The power of behavioural economics is that identifies irrational cognitive processes to produce counter-intuitive insights which help us to better predict human behaviour. Consider the chart embedded above which shows the pain intensity over time experienced by experimental subjects. Patients in the B category clearly experienced more pain, but the surprising, counter-intuitive, result is that Patients in the A category remembered themselves as having had a worse experience.

In this case it is human memory which performs irrationally. Rather than accurately remembering and each element of an experience we are driven by what behavioural economists call the Peak-End rule which says that our memories are unduly influenced by the best and worst elements of an experience and by its final moments. In the ‘pain experiment’ which was conducted by Daniel Kahneman patients in Category A finished with much greater pain than those in category B and it is this worse ‘end moment’ which explains why they recall the pain as being worse than patients in category B (more details of the experiment here).

When I read about theories like this I like to test them on my own memories, and when I think back over the best and worst experiences of my life it is the peaks and troughs that I remember. For example – when I think about the best gigs that I’ve been to, I come up with a list where the best moments were AMAZING, not where the end to end experience was great. I can also see the importance of final moments, bands for example, routinely save their best tracks for the encore.

Turning to consumer internet, the peak-end rule tells us that to get consumers to have a great memory of an app it should deliver a real wow and maximise the peak, and that the impression in the moment when the consumer leaves the service is also important. Think about the Twitter ‘Fail whale’ and the amount of goodwill they got by having a bit humour having just delivered a service outage.

Interestingly, the amount of time spent using a service doesn’t impact how we recall it which implies that after a point engagement metrics are a poor guide to customer satisfaction (although they may be important for revenue). Google intuitively grasped this point in their early days when their goal was to get you off of Google and onto the page you wanted as quickly as possible.

You may have seen the recent brouhaha in the financial press about a 2010 research paper by Carmen M. Reinhart and Kenneth Rogoff of Harvard which found a correlation between high levels of government debt and low or negative economic growth. This paper was important because it underpinned the austerity policies adopted by many governments around the world since the financial crash – not least here in the UK. The brouhaha has come about because economists from the University of Massachusetts have repeated their analysis using the same data but different weightings and found that the median growth at highly indebted economies was much higher.

In the meantime other studies of different data have apparently found similar results to the original 2010 paper from Reinhart and Rogoff.

This is a big deal because whether to pursue austerity policies is perhaps the most important decision facing most developed economy politicians right now.

The problem is that there isn’t much data to go on and the data quality itself is questionable. There haven’t been many periods in history when governments were as indebted as they are now, and it is impossible to know if economics have changed to a sufficient extent that historical data isn’t relevant. If we understood economics well enough to have a causal theory linking debt and growth then minimal data proving that theory would be helpful, but in the absence of that causal theory we are simply looking at correlations of limited data sets. In this situation people with convictions based on their political persuasion use data to press their case rather than to determine the right answer. A very dangerous game.

This problem also exists in business where most of our ‘understanding’ is based on observed correlations with little understanding of causal relationships. With the best intentions in the world this can lead to slavish following of trends to the detriment of business performance. The problem is, if anything, more pernicious in business because the advice of most business gurus includes is backed up by some level of causal logic as well as observed correlations with success. The 1970-2000 mantra that a simple focus on shareholder value was the best way for companies to generate success is a good example. It started with an observation that there was a correlation – successful companies like Coca Cola were shareholder value focused – and then added the causal theory to back it up (in simplified form a company’s shareholders want value, which can only come from generating good cash and profits, which are the essence of a sustainable business), but the correlation has recently fallen apart and the causal link between a focus shareholder value and long term success is now hotly disputed. The problem arose because the causal theory was based on an erroneous assumption that shareholders would distinguish between short term profit creation that destroyed long term value and truly building for the long term.

So what to do?

I think there is huge value in drawing insight from the limited data available but it’s critical to sense check the conclusions that others come to before following their advice. The most important sense check is whether it feels right – intuitively it makes sense to me that indebted countries will grow more slowly because they are having to use more of their resources to repay debt and hence have less to promote growth. In a business context this intuition often has its roots in the vision and values of a company. Advice that is inconsistent with that world view won’t feel right.

The next sense check is the credibility of the theorist – most importantly, are they impartial, looking at the data and searching for conclusions, or did they start with the conclusion and then go looking for data to back it up.

Thirdly – do research. If an idea is important then it is worth investing time to form a considered view.

Finally – stay open minded and be prepared to try things and change. Don’t make the oh-so-common mistake of ignoring everything that doesn’t fit with existing beliefs (behavioural psychologists call this the ‘confirmation bias’) and be prepared to disucss half formed opinions and lightly held convictions. In a fast moving world with little data it’s the only way to progress.

These ideas apply to many decisions that startups have to take on an ongoing basis – should I have a freemium business model? should I sell director or via channel? should I pursue growth over profitability? etc. etc. etc.