Big data is much more than just a way to put people into a box. In fact, recent developments in software analytics allow data to deliver unique services to satisfy each client’s preferences, according to GlobalData Financial Services
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Financial services providers have started using big data analytics in order to better know their clients. It can help identify the main characteristics that distinguish individuals and then group clients that appear to be alike. Certain products and services can then be targeted at the client groups that are likely to be most responsive.
Yet big data critics note that, as a result, clients are put into different boxes only on the basis of data analytics. Assessing a client’s personality solely relying on statistics can prove misleading, as often cold data does not paint the whole picture.
Contrary to the general hype that the future is all about digital and data, Investec’s #MoreThanData campaign focuses on providing more face-to-face interactions to clients in order to really understand their distinctive needs, and ultimately provide services that are perfect only for that particular person.
However, a new approach to data analytics – namely mass customisation – has the potential to improve service customisation strategies. The term originates from Joseph Pine’s book, “Mass Customisation: The New Frontier in Business Competition,” published in 1992, and it has mainly been applied to the manufacturing sector. It refers to the ability to create very basic products with the possibility to customise them, which in the end allows providers to create tailor-made products at a cost close to mass production.
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By GlobalDataAt the Digital Integration in Wealth Management conference in February 2017, Objectway highlighted how the concept of mass customisation has started to appear in the wealth management and private banking sector. As of today, the wider industry has seen the implementation of this concept only on front-end solutions that allow clients to customise the colours, features, and item disposition of a mobile banking app, for instance.
The possibility to track usage data in real-time and automatically adapt a product accordingly adds an interactive element to the online experience. This can act as a time-saver when designing an app, as too often digital innovation officers find themselves in endless meetings trying to reach consensus on which area of the screen a particular icon should be placed.
In the future, wealth managers should aim at using the information derived from data mining to tailor products according to the specific preferences of each client. Moreover, information gathered through big data can be used as the underlying base of the client-advisor face-to-face discussion, helping to tailor investment products. Another example is the possibility to inform needs prediction in order to anticipate advice needs, such as adding inheritance advice as part of the main asset management proposition if the client is expecting a child.
Ultimately, mass customisation in wealth management has the potential to take off sooner than expected. According to our 2016 Global Wealth Managers Survey, the majority of players in Europe and Asia Pacific are already exploring the use of big data. Those that have refrained from doing so will have to catch up.
