Big data is much more than just a way to put people into a box
Big data critics often note that, as a result, clients are put into different boxes only on the basis of data analytics.
Big data critics often 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.
However, financial services providers have started using big data analytics in order to better know their clients, to identify the main characteristics that distinguish individuals and be able to offer them certain products and services that are likely to be most suitable.
The mass customisation approach to data analytics 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.
And mass customisation in wealth management has the potential to take off sooner than expected.
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.
Wealth managers should aim at using the information derived from data mining to tailor products according to the specific preferences of each client. Also, 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.