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Chapter 5: How Digital Got its FANGs – the Behavioural Science of Digital Business

‘Making it easy’ in digital

The largest global digital companies – the so-called FANG group (Facebook, Amazon, Netflix, Google) – are worth a combined total of over $2,350 billion at the time of writing. But none were the first to launch in their respective markets, nor was the technology they employed unique. They did not deliver that growth solely through technical innovation.

In differing ways, every FANG company demonstrates how creating processes, systems, products and services that make it easy – by paying close attention to the psychology of customers – leads to business success.

And yet, providers of digital goods and services often seem to forget (or ignore) that consumers frequently want their decisions – and therefore their lives – made easy for them. That the digital customer experience should be short, not long, and require as little effort on their part as possible. And not just minimal physical effort, like next-day home delivery, but mental effort as well.

The most successful digital companies of the 21st century have minimised consumer cognitive effort. The FANGs have achieved market dominance in social, e-commerce, content and search by providing products that are not just driven by technological innovation, but go with the grain of behaviour, reduce friction, and make choice as easy as possible. They leverage instinctive system-1 biases and heuristics to provide experiences that are effortless, turning them into habits and making their products inherently addictive – with all the associated ethical and moral problems that causes, as we shall see later.

Let’s take a quick example. Do you remember the heady days of 1998? When the biggest Presidential scandal involved an act of infidelity with an intern, European countries agreed to work more closely and introduce a single currency, and Britney Spears informed us that, oops, she had done it again?

On 28 September of that year, a shiny new search engine called Google launched.

Google was not the first search engine. It wasn’t even among the first 50. In 1998, there were dozens of similar products available, including Yahoo!, Altavista, AOL, and Ask Jeeves.42 Yet, two Stanford dropouts, Sergey Brin and Larry Page, were still able to create a disruptive product, and one that quickly achieved a near monopoly.

Twenty years on, 85% of all internet searches in the UK are conducted using Google. It has been an astonishing rise, with virtually none of that growth fuelled by marketing. Most of Google’s marketing efforts have been focused on its other products, such as phones and voice assistants.

Google’s innovation in the search market was not technological – their background tech was essentially the same as the other search companies – but psychological. The information Google provided was the same as other providers, but the way it was presented to users was different. What they were saying was the same, but how they said it was better. According to Nir Eyal, in his excellent book Hooked:

“Google’s PageRank43 algorithm proved to be a much more effective way to index the web. By ranking pages based on how frequently other sites linked to them, Google improved search relevancy. Compared with directory-based search tools such as Yahoo!, Google was a massive time-saver. Google also beat out other search engines that had become polluted with irrelevant content and cluttered with advertising. From its inception, Google’s clean, simple homepage and search results pages were solely focused on streamlining the act of searching and getting relevant results.

“Simply put, Google reduced the amount of time and the cognitive effort required to find what the user was looking for.”44

Leaving aside the pitfall of survivorship bias (see below), this is a great example of how the digital behemoths have been driven by an evidence-based understanding of the psychology of consumers. Through that understanding, they have delivered products and experiences that are more useful, memorable, cognitively effortless and – therefore – more addictive than those of their competitors.

Survivorship bias

Source: xkcd

The joke in the great cartoon above, of course, is that winning the lottery is down to blind luck. The time put in bears no relation to the chances of winning, but successful people will often post-rationalise success as due to positive character traits.45

Hence the expression: “History is written by the victors.”

How this biases our behaviour is that we tend to regard stories of success as more influential than unsuccessful ones, and often ignore the effect of chance on positive outcomes.

One famous example of survivorship bias is explained by David McRaney in his book, You Are Now Less Dumb. It is the story of Abraham Wald, a statistician working for the Applied Mathematics Panel for the US Air Force during WWII.

The Air Force top brass were concerned about the low survival rates of bomber crews – at one stage they had a 50% chance of surviving a tour of duty. They wanted to increase the chances of crews returning unharmed by reinforcing the armour on the planes. But they couldn’t reinforce the plane throughout, because it would make it too heavy to fly and steel was in very short supply.

The military had recorded data on where the returning planes accumulated most bullet holes – along the wings, around the tail gunner, and down the centre of the body – and naturally their first thought was to reinforce those areas.

McRaney explains: “The mistake, which Wald saw instantly, was that the holes showed where the planes were strongest. The holes showed where a bomber could be shot and still survive the flight home, Wald explained. After all, here they were, holes and all. It was the planes that weren’t there that needed extra protection, and they had needed it in places that these planes had not.”

Wald experimented to determine which of the other areas needed the most protection, saving countless lives. His theories are still in use today.46

By only looking at the planes that survived, the US Air Force gained a false picture of what success looked like. This bias blinded them to the fact that there is as much to be learnt from failure (if not more), and negated scepticism about positive outcomes. Accordingly, we should overcome our natural tendency to hide failures, because if we do not, they are much more likely to be repeated. And we should accept that often our successes happen due to pure good fortune.

This is not only fundamental to a growth mindset, but is also the basis of experimentation. If you test five things in an experiment, and only two work, that isn’t a bad outcome, nor a failed experiment.47 You will have gained five valuable insights that will deliver a business advantage – by doing the things that work, and also by not doing the things that don’t.

Daniel Kahneman says: “A stupid decision that works out well becomes a brilliant decision in hindsight.”

Making search easy: Google

In Google’s case, while behavioural insight drove the fundamentals of the product, part of their success was also sheer, dumb luck – it is a fairly open secret that the original Google interface was so clean and simple because Larry Page didn’t know how to code in HTML.

Despite this, such was the effectiveness of the approach that the Google homepage remains largely unchanged in 20 years (aside from updated branding and the ‘Google doodles’48).

The Google homepage in 1998 (top) and now (bottom)

Source: Hooked by Eyal, N./Google

One example of how Google makes the experience of searching as cognitively easy as possible is the choice architecture of providing two simple buttons: ‘Google Search’ and ‘I’m Feeling Lucky’.

Have you ever actually used the ‘I’m Feeling Lucky’ button to conduct your Google search? Me neither. My friends at Google assure me this isn’t unusual. A 2007 analysis concluded that fewer than 1% of all search queries are conducted using that button.49 When users do click it, they go straight to the top result page, skipping the results listings. That means that Google shows zero ads (and therefore gets zero ad clicks) on 1% of all Google search queries. The analysis concluded that this button costs Google as much as $100m per year in lost ad revenue.50

Yet the button still remains. If ‘I’m Feeling Lucky’ loses Google money when people use it, and hardly anyone uses it anyway, then why is it still there? Nostalgia? The anecdotal feedback from Google insiders is that this placebo choice remains because it subconsciously implies that Google will always give you the best possible result.51

This subtle nudge, unchanged in 20 years, gives users faith in search results, and increases their self-efficacy (confidence and trust) in the brand to provide the right solution – as with the banking customers and the re-framing of security checks example in chapter 3. It is Google’s way of saying that no matter which button you press, it will deliver the result you want. Google is there (it implies) to serve your needs as a user – rather than its advertisers.

Subsequent Google innovations, such as auto-completing queries, spelling corrections and so on, are also based on further improving the cognitive ease of the user experience. This includes using data on your previous search behaviour to make the results you see tailored to you (one change to the homepage since 1998 is the ability to sign in to your Google account). That personalised experience makes it even more effortless, as we like things more if we think they are unique to us.52 All of which serves to drive our Google addiction, and make it the go-to search engine for 85% of internet users.

Using data to leverage social proof: Netflix

This personalisation effect has also been harnessed by Netflix to achieve market dominance using social proof – our innate desire to follow the herd. They have deployed machine-learning algorithms and customer data to create bespoke experiences for each user – the Netflix pages that you and I see will be different – and also to highlight what other people are doing.

Much has been made of the binge-watching phenomenon, how it has changed the nature of TV viewing, and how specific content makes the site uniquely addictive rather than the site itself. Undoubtedly, if the content on Netflix was solely repeats of ‘Allo ‘Allo and Heartbeat (like some other channels I can mention), it would not have caught on quite so well with younger TV viewers.

But far more relevant is the fact that more than 80% of all content watched on Netflix is actually based on its recommendation engine. That is, content recommended to a viewer according to a percentage-based ranking, based on things previously watched – effectively a numerical social proof value.

Example of Netflix recommendations

Source: Netflix

No one outside Netflix knows exactly how that percentage matching works (is it based on what other people who liked that also watched? Or who stars in/writes/directs the show? Or genre?).53 The mechanism for it was copied by Netflix from dating sites, which provide matches based on compatibility with other users (such as shared interests).

There is huge potential for this combination of data and behavioural insight to be applied in many other contexts to deliver the most compelling content.

“Netflix are looking at the content itself, the storyline, the characters, a whole range of data points about you and the content,” says Steve Thompson, an experienced digital training consultant who works with media businesses across Europe. “And then using that insight to create future content. As a user you will also start to see recommendations down to your consumption of the content. They can easily use machine learning to nudge people into choices about what content they choose in the future.”

The net effect is a kind of evolutionary natural selection of content – with only the strongest (i.e. most popular) content surviving, but maintaining sufficient variety to keep us interested. This keeps the content gene pool sufficiently broad – and making the best possible product overall.

Netflix not only personalises recommendations, but how they recommend it. The artwork chosen to promote a particular show will be personalised to what is most appealing to each user, featuring different images or actors based on what is most likely to be clicked on.

“We don’t have one product but over a 100 million different products,” say Netflix. “With one for each of our members with personalized recommendations and personalized visuals.”54

As a result, users implicitly trust the mechanism simply because it takes the pain out of having to choose what to watch. In 2020, the paradox of choice of watching TV is that there is such a huge number of channels and content available it becomes an impossibly hard ‘choice-maximisation’ problem.

Netflix solves it by providing a simple, ‘choice-satisficing’ solution: if you liked that, you’ll like this. And what do you know? Three out of four times that simple nudge works, along with others, like automatically starting the next episode and the ability to skip the credits, and keeps people watching.

The paradox of choice

The paradox of choice is a term coined by Professor Barry Schwartz (and the title of his 2004 book). Schwartz’s thesis is that in modern, westernised societies, we are now overwhelmed with abundant choice in almost every aspect of modern life: utilities, healthcare, pensions, beauty, work, love, religion, identity. But the paradox is, counter-intuitively, that increasing the choices available does not make us happier, nor make us more likely to choose the option we will like best.

“Autonomy and freedom of choice are critical to our wellbeing, and choice is critical to freedom and autonomy,” says Schwartz. “Nonetheless, though modern Americans have more choice than any group of people ever has before, and thus, presumably, more freedom and autonomy, we don’t seem to be benefiting from it psychologically.”55