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The key to success for the financial industry

We live in an era of digital transformation, where customers want companies to not only meet their needs, but go beyond them.

It’s no longer enough to just have a consistent customer experience or a robust marketing strategy. Customers experience information overload, and the mass communication of products and offers is most likely perceived as noise or creates friction in the minds of customers. There is a need for an effective mechanism to break through this noise. This is where hyper personalization comes into play. It’s a cutting-edge way for businesses to deliver a personalized experience based on customer segments.

In marketing, brands need to go the extra mile to enable real-time adaptive content, features and interactions. With that in mind, let’s dive a little deeper to understand how products and brands are using technology to create hyper-personalization and what this means for the financial sector.

How is technology driving hyper-personalization?

Hyper-personalization captures real-time data and delivers a seamless experience by looking at customer data, identifying their pain points and providing tailored solutions. This can be accomplished by developing personalized and targeted experiences through the use of real-time data, predictive analytics, AI and automation.

What does hyper-personalization mean for the financial sector?

For financial institutions, personalization is a key enabler to drive customer centricity throughout the value chain. Traditional financial institutions are increasingly looking at strategies to retain their current customer base and attract new customers as they face increasing competition from technology-driven companies. As today’s customers seek personalized products and services that address their specific interests and needs, finance organizations must focus on smooth product design, tailored investment advice and proposals, advanced tools for faster decision-making, and strategic communication and customer service plans. All of this helps to ensure customer focus while future-proofing the organization.

Financial institutions are quickly finding ways to make better decisions about the types of products to offer.

They also identify potential touchpoints in customers’ financial journeys, leveraging the vast amounts of data they have on consumers, right down to the transaction level.

Natural language processing and language analysis are some examples of transformative technologies. These methods have been implemented by financial institutions to capture their customers’ profiles and views during telephone, in-person, and online interactions. This has enabled them to become more data-driven to better understand and anticipate changing customer needs.

What could that look like for the brokerage industry?

Financial brokers can significantly improve customer satisfaction through hyper-personalization. They facilitate transactions in the financial markets through their mobile or web-based trading applications. Data as a catalyst for hyper-personalization comes in the form of market data and customer data. Market data includes:

  • Trading information – stock prices, volumes, open interest, order histories, etc.
  • Company fundamentals – earnings, dividends, financial metrics (e.g. price-to-book), margins, etc.
  • News and Insights – Business and company news, analyst research reports, etc.

Even for the experienced investor, the amount of information available on a daily basis can quickly become overwhelming. Individual investors will be looking for educational content and distilled, actionable insights because managing their capital is not their full-time job. In addition, investors do not want to get insights into thousands of stocks and funds. They only want to see what is relevant to them. This leads us to customer data.

Trading apps offered by brokerage firms have extensive records of their customers. This may include demographics, trading history, and in-app activity history. For example, based on this information, the client base could be segmented by skill level (e.g., client new to investing vs. client with years of proprietary trading experience) and activity level (e.g., do-it-for-me investor) to be segmented or practical trader).

Once users are associated with one of these segments, the experience is completely tailored to them. For a novice investor, the app offers a lot of guides and educational material to learn the trading section. The UI is dynamically changed to show the stock details much more easily without using much technical jargon. The app highlights the low-risk, low-return stocks to help users gain confidence and continue their journey.

For an intermediate investor, the trading app would offer curated insights into their existing portfolio. Based on customers’ current holdings, personal in-app history, and similar customers, potential investments could be highlighted along with business cases for and against those opportunities. The intention is not to give advice but to enable the client to break through the clutter of thousands of potential investments.

For a more advanced trader, hyper-personalization extends to advanced tools with real-time data analysis from exchanges to help them make timely trading decisions. The intention is to allow these advanced users to trade as many markets as they like to invest as quickly as possible.

The final result

As technology-enabled consumer data grows, customer expectations evolve, and the market becomes more competitive, hyper-personalized experiences will become a critical part of any successful strategy. Data, analytics and AI are essential technologies to create a hyper-personalized experience and strategy that can help financial firms like brokers build closer relationships with their customers and grow in the marketplace.



The views expressed above are the author’s own.


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