Key takeaways

  • Many businesses invest in technology without thinking about how it will help customers.
  • Technologies such as virtual reality, predictive analytics and fintech should all be driven by customer needs.
  • Behavioural science and behavioural economics could be the key to understanding how tech capabilities can solve customer problems.

Chindogu is the curious Japanese art of inventing everyday objects that, on the surface, seem like a perfect solution to an everyday problem. But there’s a twist. The defining feature of a chindogu invention is that in using it, a person would encounter so many new problems, or suffer so much embarrassment, that it’s rendered almost useless¹.

My personal favourites are the chopstick fan – for when you just can’t wait for your noodles to cool down, the baby onesie mop – in which your baby can learn to crawl and clean the floor at the same time, and the all-over plastic bathing costume – to help aquaphobes to swim without touching the water.

Believe it or not, Chindogu is a cautionary tale for business leaders. Technological innovation that isn’t driven by human experience will always be what chindogu practitioners (yes, that’s a thing) call ‘unuseless’: not absolutely useless but also not something that practically improves people’s lives.

Unuseless technology is a problem for businesses. Advanced technology capabilities and the potential of big data can be dazzling, enough to forget that technology is merely the tool for solving problems. Technology without a customer strategy is a toy, not a business solution.

The thought
behind the action

Two of the best ways to understand customers are through the twin lenses of behavioural science, which considers how people make decisions, and behavioural economics, which looks more closely at economic decisions. Every technological or analytical tool needs a strategy informed by behavioural science: this should help keep the horse firmly in front of the cart.

Let’s look at three technological advancements: virtual reality (VR), predictive analytics and fintech, to consider how behavioural science can drive their design and use.

Virtual reality

We’re on the cusp of a virtual reality revolution, with analysts predicting it will be a US$150 billion market by 2022². However, for many businesses, VR remains a toy in the corner. Not enough of them are using it to change the relationship between the business and the customer.

But what if VR could give us the power to see into the future?

Any behavioural economist will tell you that people are bad at long-term decision making. There are a couple of reasons for this. First, we have a preference for things to remain the same unless the incentive to change is strong. This is called status quo bias. Second, we prioritise present rewards more heavily than future ones. This is known as temporal discounting.

Financial services companies grapple with the challenges posed by status quo bias and temporal discounting every day. Similarly, governments are desperate for us to make healthy decisions now so they don’t have to pay for our medical bills later.

Imagine if we could visualise our future selves. If we could see the results of not putting away money for retirement. If we could picture the effect of eating junk food now on our body in ten years’ time.

In 2011, a Stanford University study required participants to make financial transactions with realistic computer renderings of their future selves using virtual reality. Participants who interacted with their virtual future selves exhibited an increased tendency to accept later monetary rewards over immediate ones³.

What if VR was a part of the application process for a superannuation policy, or if politicians were obligated to view islands affected by climate change before making environmental policy decisions? Virtual reality, used in conjunction with such theories as status quo bias and temporal discounting, could be the shield against bad long-term decision making.

Predictive analytics

Predictive analytics is giving businesses previously unimaginable capabilities to predict future events. Take spatial analytics. From pedestrian accidents to increased traffic during peak hour, transport authorities can now input almost any variable into a predictive model and determine, among other things, how many buses or trains are needed, at what times they’ll be needed and what groups of people will be catching them.

But analytics can only take us so far. If the data tells us that we’ll need to close a road for 32 minutes in the event of an accident, what’s next? How do we actually promote safer driving to lower pedestrian accidents?

Behavioural science addresses the ‘last mile problem’4. Whilst analytical models are great at targeting, segmenting and predicting, behavioural change is still needed to ‘go the last mile’ and actually change people’s behaviour in light of what those models tell us.

Spatial analytics and behavioural science have already been combined in the process of urban planning to make huge improvements to cities around the world.

Think of it as a two-step process. The first step is driven by the technology, with spatial analysis used to create an exact digital replica of a city’s transportation system. The second step goes ‘the last mile’ using behavioural economics to help cities implement incentives that reward commuters for shifting their travel away from peak times. One success story for this approach is the Indian city of Bangalore, which convinced 17% of commuters to travel outside of peak hours using incentives such as lottery tickets5.

When it comes to analytics, behavioural economics is the tool needed to run that last mile from understanding people to encouraging them to change their actions.

Fintech

The tech-enabled sector that’s making financial services more efficient, fintech isn’t just hype. Funding for fintech startups more than doubled from 2014 to 2015, reaching US$12.2 billion.

It isn’t technological innovation that separates the best from the rest in fintech. It’s a deep understanding of customer psychology.

Take the peer-to-peer insurance startup, Lemonade. Using Lemonade’s app, customers can buy home insurance in three minutes (rather than the three days they may have to wait with a traditional insurer). That’s only part of the story. Lemonade has understood a tension in the insurance business: insurers make money by denying claims. An insurer’s profit is (very simply) the difference between the premiums that people pay and the amount of claims that the company pays out, which can lead to serious resentment from customers.

With this in mind, Lemonade has removed the economic incentive for denying claims. The company invites users to form small groups of policyholders, who then pay premiums into a pool to cover claims. Lemonade takes a flat fee for its services (which incorporates reinsurance) and if there are any leftover funds in the pool at the end of the year they get donated to a charity of the customer’s choice.

They call this final step the Giveback. The Giveback aims to reduce the number of fraudulent claims the company pays out. Lemonade has made the bet that customers will be less likely to submit fraudulent claims if they know that the money won’t be coming out of the insurer’s pocket, but the pocket of their chosen charity. Lemonade, and the fintech industry, are proof that innovation must be both technological and behavioural.

Data science
or behavioural science?

New York Times bestselling author and former editor-in-chief of Wired, Chris Anderson has said: “This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behaviour… Forget taxonomy, ontology and psychology… With enough data, the numbers speak for themselves6”.

I disagree. The conflict between behavioural science and data science, between technology and psychology, isn’t real. In fact, they should work hand in hand. Technology gives us the tools to measure and transform what we do. Behavioural science gives us the all-important why: why we act in a certain way and how we can become better as people, as businesses and as communities. The Chindogu problem of unuseless technology can be avoided if we strive to keep people – and specifically behavioural science – at the heart of technological innovation.

Contributor

Will Kingston

Will Kingston is a management consultant in PwC’s Experience and Insights practice.

More About Will Kingston