How predictive analytics enhances client engagement
How predictive analytics enhances client engagement
The use of big data and artificial intelligence continues to reshape the way financial advice is delivered, but advisers need to be thoughtful about how they use these tools to maximise engagement, writes Cognizant's Carlo Lacota.
Australia has a mature and robust superannuation system that is the fifth largest in the world. With over $1.5 trillion in assets, the Australian superannuation system represents the second largest asset base (after banking) accounting for 24 per cent of all assets held by Australian FIs.
Growing at a compound annual growth rate of 9.1 per cent for the past 10 years, the Australian superannuation system is expected to reach $3 trillion to $4.5 trillion by 2020.
On the one hand, Australian wealth managers are experiencing significant cost pressures due to legislative changes, while on the other hand, customer expectations regarding digital experience and personalisation are increasing.
The emergence of robo-advisers is driving innovation in digital experience and data analytics.
In an era of easily accessible choices and information to compare wealth products, it is much easier for customers to overcome the 'switching inertia' and shift loyalties, especially during change in employment.
With little differentiation between products in the wealth market, personalised and engaging customer experience becomes critical in not just attracting new customers, but also retaining existing ones.
Predictive analytics and its sibling artificial intelligence (AI) are the hottest topics around.
We truly are in the age of 'big data' — this is not just because of the volume of data collected and available, but also because customers' understanding of the stored data about them and how it could be used has also matured.
Technical advances have made data collection, storage and sharing cheaper and less cumbersome, thereby promoting the use of analytics by wealth managers.
The availability of a vast amount of product, market and customer behaviour data is helping customers make investment decisions based on date/analysis of the past trends, peer group portfolio decisions, etc.
On the flip side, it may drive high net worth (HNW) customers away from robo/algorithmic advice, given its perception as 'black box' advice.
What is also emerging is the use of robo-advice in a safe and experimental way, where customers test their own perception against the robo-advice.
However, used in conjunction with face-to-face advice by financial advisers, data-driven algorithmic advice can potentially drive better investment decisions by customers.
This is why AI solutions, while being the coolest approach, may not be a great first step, they may tend to be 'black box' solutions that provide the mathematically 'best' recommendations, but not necessarily the ones that will be accepted, or at least not in the short-term — as you gain traction, you can move to even more complex approaches.
Another area currently being explored is integrating external data sources with internal data to derive deeper insights about customers.
Merging social media data may be exciting, but you are likely to get more insights by merging customer contact data with transaction data that resides within internal "silos".
In addition, external data sources make you think about the balance between 'useful and creepy' - how will your customers react to the insights you deliver? How would your stakeholders react to being on the front page of the paper?
There is no hard and fast line here, but it is important to be open and honest about the usage of data sources.
The Uber app monitors remaining battery on your phone in order to optimise performance, but to quote them, "We absolutely don't use that to push you a higher surge price."
Being open about what data is being used and how that can help your customers is critical.
Before jumping on the bandwagon, wealth managers need to consider a number of common do's and don'ts while rolling out their respective analytics strategies:
- Equate complexity with success
- Underestimate the importance of change management
- Let the available data or a particular methodology drive the process
- Look for solutions rather than problems
- Understand what success means for the end users
- Think about the security of customer data and associated analysis as one breach can take away customers' trust gained over a number of years
Many have moved beyond "What can I do with this data?" to "What is the customers' goal?"
The question that really should be asked is, "What can I deliver that will be accepted and drive value for the customers?" That is the question that data can answer.
Another challenge facing large traditional wealth managers is the decades of regulatory and taxation rules that are embedded in the product administration systems, making it cumbersome to quickly roll out changes.
Further, a complex technology landscape comprising multiple sources of customer-related information and ageing unintegrated systems makes it challenging to enable real-time analytics for supporting superior and dynamic customer experience.
Due to their smaller size as compared with traditional wealth managers, emerging robo-advisers are relatively nimble and can roll out changes to their IT systems much more quickly than their traditional counterparts to market new products or offer personalised portfolio advice.
Wealth managers need to offer simplified yet intuitive front-end experience to their customers, while 'masking' the underlying complexity of process and IT landscape.
To achieve this, wealth managers need to work closely with their internal and external IT partners to optimise their IT and process landscape through simplification and by leveraging emerging technologies.
Carlo Lacota is the assistant vice president of banking and financial services of Cognizant, and he wrote this piece in conjunction with Dushyant Kapoor, the company's director of banking and financial services consulting.
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