The rise of the hyper-personalisation is changing the game when it comes to customer centricity and the adapting to customers' individual preferences.
For a long time, the market agreed to accept pre-defined individual profiles, called “personas”, to determine someone’s risk tolerance. People were viewed on a spectrum of conservative to aggressive according to their investment options or risk tolerance. Businesses then created a strategy for each persona. In other words, we group people into big clusters where we believe people would have similar behaviours. The problem with this is that each client is shoehorned into a persona, rather than seen as someone with personal needs. In other words, strategies are shaped around an imaginary and static persona and the inconsistencies that make each client unique are ignored.
Most financial institutions and vendors claim that using personas enables a business to personalise offers for clients and scale-up in a cost-controlled way. For this reason, many companies invest heavily into this strategy as a data-driven marketing effort. Even with the evolution of artificial intelligence and machine learning, the most advanced technique in banks I have seen recently, is based on personas, but the number of demographic identifiers has increased. For instance, instead of conservative, today you can be called a “father of a family” or a “female executive”. More effort is spent capturing new personas than understand how people in existing personas react to client offers. And by effort, I mean millions of dollars.
In reality, preferences are different due to individuality. It means that a persona can only be perfectly accurate if there is one for each individual client, which obviously eliminates the scalability of a persona-based strategy. It’s always better to think about each client individually, with their own lifestyle, behaviour, and reaction to stimuli. It’s also important to recognise that people change over time.
Let’s take two examples: think about a young boy in his 20s with a low-income job and no savings. Now imagine a man in his 70s, also with a low income, and equally no savings. Both would be classed conservative regarding their financial state. The common conservative persona would put both into the same profile. But it’s totally different when we step away from the static point of view and consider what they want to do about the future of their personal finances. In other words, if your decision system doesn’t know why they are equal but will respond to different nudges, it could make your marketing offers irrelevant. So, how do you create more efficient marketing campaigns?
To stay with the current situation, the COVID pandemic, and the lockdown and restrictions that this disease imposes, many are considering a dramatic change in people's behaviour. It is expected that behaviours on working, studying, spending, investing, buying, traveling, and so many others will change, if not dramatically, they will change significantly. It is still not easy to understand how new habits will be. It is an important question that I believe all companies are trying to answer right now.
More than that. As the customer behaviour changes, products remain static, loosing its importance over time. It makes clients to move to another providers as products are better designed in your competitor. Phone plans are a good example of this effect. Products should also adapt as we can understand changes in customers' behaviour, remaining meaningful, improving loyalty.
The answer lays in hyper-personalisation, the ability to understand customers on an individual level and make the experience unique by adapting the products to them. This novel approach, based on machine learning, does not pre-define an expected average behaviour of a user (persona). Instead, it follows the individual. It is fed not only by past data, but also current transactional data and becomes a live indicator, linking the past with present and anticipating the future behaviour and needs of a consumer. Ranking a customer’s risk tolerance can then show not only how they will use their money, but why. The models adapt automatically, not only with data generated daily by the transactions of an individual, but also with the imputes created by the customer experience.
Another reason to adopt these models is that you, as a bank, can save significant budget by not having to track and maintain the perfect personas, or check how they are resonating with the real clients in real life, which require constant updates and a high-skilled team. Indeed, it’s a huge effort required from teams of data scientists. Needless to say, these technology will be relevant in different regions and countries as opposed to the personas-approach. Hyper-personalisation do it automatically, self-adapt, learn with its own mistakes, and all in real-time in a much more efficient way.
Your customers will thank you for treating them individually, especially if you can understand, anticipate and adapt to their new habits.
Pablo Morales | CEO of London Analytics.