Today’s FinTech companies have an opportunity to radically personalize the experience of financial services for consumers and businesses. Accomplishing this transformation will require combining a unique and somewhat tricky set of ingredients.
So why hasn’t the world’s greatest enabler of scale and volume – the internet – driven personalized experiences to customers in financial services the way it has in other markets? There are a number of enabling factors that are critical to this transformation.
Unique and Proprietary Data
In order to offer a unique and personalized experience, companies need to have some source of data that sets them apart. Too often, firms make the erroneous assumption that simply hiring a team of data scientists or implementing a leading-edge technical solution will yield the novel insights they seek. Any company looking to personalize its offering must first consider if they have data that provides insight into their customers and the environment in which they live and work, if those datasets are rich and not widely available, and if customers and partners explicitly consent to the sharing of this data. In the world of machine learning, it is generally the case that if given the choice between more data and more sophisticated models, it is the data that will have greater impact. Ideally, if you are looking to build a powerful personalization system, you have access to large quantities of unique data, and its accumulation yields an improved experience for all your customers.
Personalization at massive scale is only possible with the diligent application of technology. The mere existence and possession of data is not the same as having that data in the right structure to enable its most valuable use. Too often, companies that excel at data consumption end up with data indigestion, as information gets fragmented throughout the enterprise, and it becomes harder to construct a unified view of all data pertaining to any particular customer. While some of this entropy is unavoidable in organizations of rapid scale, there are ways to mitigate the complexity. Investing in data engineers who focus on data pipelines, data integrity, and data structure is of utmost importance and will multiply the productivity realized by a data science or modeling team. At the same time, building the learning systems – in terms of both process and technology – to rapidly iterate and deploy new innovations is crucial.
Brand Point of View
In building experiences that appreciate the uniqueness of each of its customers, a company must not forget to do so in its own unique voice. Few companies do particularly well on this dimension, and there is some risk that all computational personalization will sound alike, even if the message itself is tailored. While creating personalized experiences at scale with a unique editorial voice is a significant challenge, brands that are able to do this are likely to enjoy greater success.
The historical opposition of scale and personalization is deeply embedded in human psychology, and overcoming this legacy requires a clear and compelling use case. Offering customers real value from personalization will offset the inherent privacy concerns and provide a lasting incentive to continue making available the data that enables personalization. Moreover, as the core technologies for delivering bespoke experiences become commoditized, the differentiating factor will be the ability to pair technical capability with compelling customer value.
Many of the financial product structures being offered by today’s banks and non-bank financial technology firms are relatively well-established – checking accounts, loans, credit cards, investments – even if the packaging has changed. The most interesting developments are at the intersection of technology, information, and humanity. For financial services, personalization at scale offers incredible opportunity, and the most exciting developments are yet to come.