Easy is Not Enough — Part 2
In the second part of our ‘Easy is Not Enough’ series Scott Liddell, Head of Enterprise Solutions at Standard Life, continues his deep dive into the world of tech — from the positives of negative effort to the hunt for a Shazam for life.
What is digital?
To investigate the expectations of digital, it is important to first attempt a definition of what digital is. This isn’t necessarily straightforward.
Most digital solutions, and digital finance in particular, are based online or on mobile. However, online banking has existed for a generation and we can consider the birth of the mobile app to coincide with the launch of Apple’s mobile app store in 2008.
So what is digital and why is it different? Is it simply an abstract relaunch of existing concepts or is there something genuinely new? From the consumer’s perspective there is something new, expectation. A restatement of what should be possible in very simple terms. Everything should be possible and everything should be easy.
There should be a Shazam for birds
As an example, I recall an incident from summer 2017. On holiday, birds were drinking from a pool which led to the question: what kind of bird is that? The expectation is that everything can be found out with no effort. Google has made knowledge on demand commonplace and with the addition of voice, almost effortless.
Therefore, when you encounter a use case that can’t be solved with today’s technology, expectation quickly turns to frustration. The expectation is that it should be possible, there should be a Shazam for birds. This will happen — augmented reality technology will make this possible in the not too distant future. But for now, whenever something is encountered that takes more effort than current digital capabilities can fulfil there is disappointment.
Never mind birds — we’re looking for a Shazam for life
Mark Zuckerberg is known for undertaking yearly projects. In 2016, he explored the current art of the possible by building a home assistant called Jarvis to see how much of his life could be automated with AI. During the project Zuckerberg revealed the limitations of current technology but saw what potential there was. What certainly exists is the expectation. People who have embraced digital technology are already in the place where they believe that anything is possible, they believe there should be a Shazam for life. Every problem can be solved with little or no effort using the small device in their hand.
With the obvious maturity of existing digital channels new problems emerge. How can they be improved to match this level of expectation and, in a commercial sense, how can you differentiate? Constant improvement of web and mobile channels will see them converge towards very similar end points. This is analogous with the progression of iOS and Android, they are gradually converging to a very similar experience.
What’s better than quick and easy?
The differentiating experiences will progress beyond quick, beyond easy and move into negative effort propositions. When you can’t make anything any faster, when there is no optimisation left, the obvious next step is to give customers time back. The next progression of digital will be towards experiences that remove the need for any effort at all. This will all be built on a key commodity, data. Digital is data and exploitation of data to meet the expectations of highly distracted and time-poor consumers.
The use of data can be broadly sub-divided into two main areas, personalisation and prediction. Personalisation uses data to understand customer behaviour to allow their channel experience to be tuned specifically for them.
To use a simple example, consider a customer who makes the same payment on the same day of every month. This pattern will be clear in data and, with this knowledge, when the customer logs on to make that payment they can be presented with an interface that reduces their repeated action to one click — “Click here to make your regular payment of X to Y”. This matches the expectation of customers as it can significantly reduce the effort involved with a given transaction. They don’t need to find what they need to do, they may not even have to work out how to do what they need to do, it is presented for them as a simple one click action.
There are already examples of this emerging in other areas. Google, in particular, already have a number of negative effort propositions. Google works out where home and work is, calculates commute time and warns about traffic incidents. It does this with no setup time on behalf of the user, this is a zero click action. Google also mine a user’s GMail account and combine with map and travel data to provide a notification of when it is necessary to leave for an event. The user is no longer required to create a calendar event, calculate travel time or set a reminder to leave. All of this happens with no additional effort.
There is similarly no effort even to turn the behaviour on. The assumption is that a user will want the feature unless they indicate otherwise. Turning it on takes effort, that should be removed too. Turning it off is reduced to very little also.
Powers of prediction
Prediction can be used to further enhance the experience beyond personalisation. Using the power of analytical models and machine learning it is possible to determine what a customer is likely to do or need based on an extrapolation of the behaviour of the rest of the customer base. This can be very powerful when deployed for call avoidance or call deflection.
Prediction of intent can achieve significant channel shift. A suitable call to action can drive the customer to a digital channel or, with a sufficiently accurate model, start to consider carrying out automatic actions on behalf of the customer. This ability could reduce one click actions to zero click actions.
The data capabilities to do this are not particularly new. Big Data, in its various guises, has existed for a number of years but, for the most part, it has been used for human readable insight. To meet the expectations of the digital consumer this needs to shift to machine actionable insight, data that can directly drive channel behaviour, rules and customer communications — most likely in the form of push notifications via the chosen medium.
Cool Effort
There are instances where the amount of effort, as measured in time, is not the determining factor. Rather than striving for negative effort it is almost possible to drive value and engagement from cool effort. Cool effort is something that, although perhaps taking as long, or longer than other mechanisms, is desirable to the user simply because they enjoy doing it.
One of the most advanced places to see this in the development of cars. The development of cars looks to add new features with every new generation but in many cases the new, modern version actually takes longer than that which it replaces. A motorised seat takes longer to get pushed back than the mechanical handle version. In this example, it is generally perceived as an improvement because it feels better.
It is possible to create direct emotional hooks to experiences and interactions outside of the obvious “make it easy” paradigm.
Snapchat, viewed solely as a communications tool, is sub-optimal. It takes longer to send a message and take a picture than it does to simply send a message. The addition of a picture, however, makes the effort cool. There is a payoff for the effort and this supersedes optimisation.
It may not look immediately straightforward to look for a cool effort proposition in digital finance but there is no doubt that they are emerging and will increasingly be the battleground for service and experience differentiation.
Pitfalls
There are a number of pitfalls to this approach which need to be navigated. Firstly, the use of data to analyse customer behaviour can easily lead to a negative response. There may be concerns, in a finance context, relating to a company appearing to know too much about the customer and this could drive a reduction in trust. The effect of this will be reduced if the customer perceives value in the outcome. Similarly, the customer may find predictions “spooky” and become unnerved — this is related to the ‘uncanny valley’ response to humanoid robots.
This leads into the emerging field of data ethics. As the predictive models get more powerful, the decisions relating to how the analysis could or should be used get ever more complex. For example, models looking at financial transactions can show the emergence of a gambling addiction and there are already models that look to predict and prevent suicides. There needs to be clear guidance on how such models can be used.
The use of data is essential to meet the expectations of the time-poor and distracted digital consumer but it must be approached with care with an emphasis on customer value and enhanced experience.
Invisible Butterflies
Big Data approaches work best when they are applied to all data. Many current implementations of predictive analytics often work on data shards e.g. website data or product propensity models. This has a significant issue which I refer to as ‘invisible butterflies’. The butterfly effect comes from chaos theory and suggests that small causes have large impacts. The efficacy of predictive models built on any subset of data can be compromised by invisible butterflies that exist in the data that is not being considered by the model.
Consider a standard churn model. There may be indicators in financial data to suggest that a customer has a high propensity to churn. This can easily miss customers who have raised a complaint, have made repeated calls after a period of sparse contact or who have clicked on the help section online. Only a complete view of data can avoid this.
The invisible company
The standard approach to improving a digital finance experience is to concentrate on improving the quality of interactions. The near future will challenge this norm. With data capabilities, the dawn of Open Banking and a range of alternative channels — voice assistants, social channels — it is entirely possible that interactions will no longer happen in any channel provided by the company itself. The challenge of this invisible company is that it moves towards the loss of the primary relationship and, with it, a reduction in the ability to cross-sell other products.
This needs to be countered with an experience worth the time and attention of the customer. The future will be a battle for eye balls — can the expectations of the digital consumer be met to deserve the small amount of time they are prepared to invest in the interaction?
This needs a change in mindset. Digital finance needs to move away from delivering an optimised way of performing transactions to an effortless, high value way to facilitate the financial lives of customers.
A Shazam for financial life
By treating time as a currency that is invested by customers, you can ensure that timely experiences deliver sufficient value to drive engagement and the core experience will increasingly become a differentiator in how customers choose who they transact with because they will be increasingly be more selective with how they do it. The use of data sits at the heart of any new experience and advanced, machine-actionable insight is a prerequisite bedrock of a modern, digital experience. A Shazam for financial life is possible and the banks that embrace this possibility and deliver a powerful, personalised, frictionless customer experience will set the direction for the future.
You can follow Choices and Scott here on Medium for more, or drop us a line if you know someone we should speak to about building the future of life savings. Email simon_lyle@standardlife.com
