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How behavioural economics and robo-advice change cost income strategies

In the words of Steve Jobs you need to “get closer than ever to your customers. So close that you can tell them what they need, well before they realise it themselves.” Digital and robo-advisors currently are far from that. They offer one size fits all customer experiences that do not personalize at all. This isn’t a simple thing to do. Organisations have more data on us as consumers than ever before, however few are harnessing the opportunity that this represents. In my book “Riding the waves, the future of banking” I write that organisations who will thrive in the digital age are those who not only understand what their customers are doing but also why they are doing it. This involves understanding both the data and the context of the people generating them.

Psychometric analytics as core competence

Behavioural analytics can help us understand what our customers are doing. Especially in digital and robo-advice environments, massive volumes from a rich variety of data sources fuel this. Understanding the real reasons as to why customers behave the way they do, their motivations, can be even harder. This cluster within behavioural analytics is called psychometric analytics. Simply asking people is, in line with Steve Jobs, not enough as people usually do not understand the real essence of their behaviours. Deep understanding of the why in consumer behaviour is essential for cutting edge personalization and engagement strategies.

Research based evidence

In my Robo-Advice PhD research I tap into financial motivations.  I performed a research amongst 3,000 people across the UK and The Netherlands to discover their financial motivations. I searched for deeper motivational variables that made them satisfied with digital and robo advice service concepts. The research gave insight into 4 overarching motivational financial profiles: “convenience seekers”, “uncertain unknowledgeable’”, “process adepts” and “financial innovators”. Applications are in highly personalizing new PSD-2 banking apps. Also, based on motivational insights developed, one of my companies “AdviceRobo” developed a psychometric credit score to score creditworthiness of thin-file customer segments like self-employed, start-ups and millennials. Understanding your customers’ psychometric profile can fuel a far more intimate understanding of your customers as well as the ability to better service them.

Opportunities for applying psychometrics

Machine learning usually forms the engine for the immense volume of both structured and unstructured psychographic data. If companies know how to un-tap psychometric data sources, machine learning can support their service development. Advanced psychometrics drives predictive servicing. Services like psychometric credit scoring predict creditworthiness. Psychometric targeting scores predict where to find the best fit customers and gives deep insights in how to attract them. Psychometric marketing scores support higher conversions in next best action engines, personalized loyalty programs and psychometric HR scores give deep insights in the match of teams and  job fits. All applications are built with so called “advanced psychometric data analytics”.  And those predictive personalized services will drive income breakthroughs in banks at ultra low robo-advice costs.

In this era

Where new data sources are released every day we as consumers become data-generators. As a reward for that data generation we expect companies to better personalize their offerings and services. With the machine learning technology in place, next generation digital and robo-advice service organisations invest in “building the automated Steve Jobs”. They develop the psychometric analytics competence of in depth understanding of where to find the best data to deeply understand what their customers want and why they want it.  They apply data-strategies in an open-PSD-2 data landscape. My prediction is that these kind of companies will be the winners of the hearts of many people. They get closer than ever to their customers. So close that they can tell them what they need, well before we consumers realise it ourselves.

 

 

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