The likes of Netflix and Spotify are no longer disruptors, these companies have become the heartbeat and influencers of their industries. Through innovative pricing structures and an obsession with optimising the user experience, some would argue they have kept film and music from the clutches of piracy.
This customer-obsessed approach means that every user enjoys a personalised interface, uniquely moulded around their likes, dislikes and ongoing behaviour. It is not just in the entertainment sector that this model can work however. There are lessons from Netflix’s and Spotify’s success that can be applied to many industries, and none more so than in the world of financial services.
As a part of its continued commitment to technological innovation, UBS has taken digital-led personalisation to the world of trading. In its early stages of development, the wealth management firm is applying recommendation algorithms to suggest trades to its asset management and hedge fund clients, an unprecedented move in the space.
This is a huge step in digitising the trading process and opens up a lot of questions for the future of the sector. It is also indicative of a wider shift in the way technology is being applied in the finance industry, and that this bold move from UBS can have a groundbreaking impact on investment banking as a whole.
For UBS, innovation and digitalisation are key strategic priorities, and the company appears to be investing heavily into those areas with innovation labs at L39 in London and across the globe. Dirk Klee, Chief Operating Officer at UBS Wealth Management has made only the threat from disruptors in its space but the importance of customer experience, claiming that “the client experience is being increasingly driven by what clients see in companies like Apple or Amazon.”
In the same way that we take recommendations for the latest movies and albums from friends or magazines, clients in the banking sector commonly take their trading recommendations from trained consultants and salespeople.
Through implementing Netflix-esque algorithms, UBS is taking huge leaps in automating the trading process. By analysing a client’s trading behaviour and preferences, they are able to provide bespoke recommendations tailored on an individual basis.
When it comes to the rest of the industry, despite the incredible technological advances in algorithmic trading and trading platforms, investment banks still service their largest clients through intensive “high touch” relationships. It is apparent to most investment banks that long-term this white glove treatment will lose to ease of use and higher returns.
Today’s way of doing business is simply too slow in a world of algorithmic trading, too expensive, and too dependent on the skills of individual employees. However, if clients take recommendations from an algorithm, UBS can ensure that they receive the highest quality advice faster and at lower cost. Building on the initial success hinges on finding the customers who will become long-term champions of this new way of doing business. Banks must then rigorously experiment with the way they are improving their experience and returns, and changing one of the most traditional cultures in finance. Why stop here? Traditional financial institutions are quickly learning that unless they embrace the new wave of experimental technology, their growth will suffer. Whilst risk will always be a factor inhibiting these moves, taking a test and learn approach in the same way UBS is will ultimately reap rewards. Should UBS succeed in this vision, the company has the opportunity to rewrite the rules of investment banking
About Hazjier Pourkhalkhali :
Hazjier Pourkhalkhali is the Global Head of Strategy, Optimizely—a US-based company that makes customer optimisation software. Pourkhalkhali, as global head of strategy, leads pricing and packaging for Optimizely’s key products, and works on new pricing metrics for optimal customer uptake.
He also leads global initiatives on customer retention strategies and methods. He has worked as a management consultant at McKinsey and was the COO/cofounder of Cloud Games, a frontunner in development, distribution and monetisation of HTML5 games. He has earned a degree from UC Berkeley, California.