Back in 2006, Time magazine named “You” as its Person of the Year, in recognition of the rise in user-generated content created by individuals, ultimately changing the commercial world.
But how are individuals treated? Did the supremacy of the individual gather momentum?
By definition, personalization involves tailoring products or services to accommodate specific individuals or segments. It is eventually divided into two main categories, prescriptive and adaptive personalization.
Prescriptive personalization is also known as segmenting. It is built on sets of rules and filters out possible user offerings based on interactions with a user, such as offering a product if a customer has already purchased one.
Adaptive, or predictive personalization uses collaborative filtering (a technique used by recommender systems), data analysis and user profiling tools to adapt offerings and content based on user characteristics and interactions. For instance, imagine if every time you walked into your favourite retailer, they presented you with options in your size based on items you indicated you liked.
Data fuels personalization. Where businesses used to shy away from personalization, customers are now willing to openly share data that powers experiences with brands they value and trust. They encounter personalized content everywhere – from getting personalized deals at their local supermarket to video streaming providers predicting which movies they might like. Personalized content makes customers feel special and unique, but it can be challenging for businesses to connect it all together to create truly personalized experiences.
So how is it done among the pros?
In the world of Amazon and Facebook, personalized content is a must have feature for all providers. These BigTechs have a head start because they have built their business models around collecting data and quickly responding to customer needs.
Amazon has made incredible personalization advances with its artificial intelligence, machine learning and predictive analytics. Amazon is essentially driven by customer actions. They collect the data to design recommendation systems to help you with purchasing decisions. The recommendations are based on a user’s purchase history, items they’ve looked at, items they’ve rated and liked, and what other buyers of those same items have viewed and purchased.
And customers seem to like it, with 56% of them being more likely to return to a website that has personal recommendations. 
At Spotify entire teams focus on understanding the listener through collaborative filtering, machine learning, DSP (Digital Streaming Platform – the variety of features tailored to consumers Spotify has to offer) and NLP (Natural Language Processing – by employing NLP models, Spotify can track online publications and conversations, such as articles, blogs or social media posts, to identify trends and fulfill the needs of their users) approaches. They model users’ taste through a cluster analysis based on their historical and real-time listening patterns. 
Clearly, brands benefit from providing personalization. The ones that create personalized experiences by integrating advanced digital technologies for customers are seeing revenue increase by 6% to 10%, according to BCG’s research.
Profiting from personalization in Financial Services
Despite possessing significant customer data, several banks have yet to unlock a meaningful level of personalization. 94% of banks surveyed in the recent Digital Banking Report say that they can currently only deliver basic levels or no personalization at all. 
Wealth managers have been providing personalized advice for a long time, but today’s world demands more than just a tailored portfolio constructed by a financial advisor. There is still a lot to learn from companies like Amazon or Spotify; wealth management companies could provide product recommendations based on previously viewed or purchased products and on products purchased by other users with similar investment habits.
Appeal for personalization is high and has the potential to positively impact businesses if offered the right way – only 32% of financial services executives surveyed confirmed exploring AI technologies such as predictive analytics and recommendation engines.
Well-executed personalization strategies have strong ties to high transaction volumes. In fact, high-value segments want more personalization.  Betterment introduced portfolio personalization to appeal to wealthier clients. 
The feature lets investors control asset class weights within the portfolio, enabling them to decide how their money is distributed. This is expected to encourage positive investing behavior.
After all, clients want their financial institution to know about their personal financial goals, lifestyle or major life events. Likewise, the majority of financial institutions want to leverage the data resources and technological background to give consumers the customized services they desire.
Start making use of your customer data
To meet higher customer expectations while maximizing advisor efficiency and productivity, wealth management providers must ensure that their web and mobile solutions are meeting expectations for ease of use and range of services.
The best solution is the introduction of an adaptive UX approach for wealth management providers, which can be brought to perfection by using a combination of two solutions.
The first element of the adaptive UX approach is tailoring the platform to a client group rather than individual users, which allows different pre-setting possibilities for each customer segment. These segments can be formed by an on-boarding process after the first login and by using existing data and statistics of extant clients. Using this preliminary categorization, users will feel that the application is tailored to them right after the first login.
Although the customer journey settings are based on the common habits of a group, clients will feel that the solution is specialized for their personal needs, if the right content is being delivered to the right profile.
The second element is the fine-tuning of the personalization, where the customization is being done seamlessly depending on user usage habits and data.
The application should follow and observe usage habits of customers, i.e. which features are being used the most. Based on these insights, the application should be able to establish real personalization by arranging functions tailored to specific personal needs. That does not mean that the rarely used features are lost, but that only the most important features should be reached extremely easily and quickly. If the customer’s behaviour changes at any point, further manual customization should be made available to the user.
The aim of the adaptive user experience is to deliver content and functionality that matches specific client needs on all screens without any effort and can be further customized by them anytime.
 Extractable, Digital Expereinces in Banking 2019