AI automation is driving hyper-personalization in finance and insurance. Learn to deliver exceptional digital experiences that rival in-person interactions.
Modern applications in artificial intelligence (AI), like AI automation, AI Agents, and Generative AI, together with big data, allow the banking industry to deliver hyper-personalized digital experiences that customers can appreciate. A successful experience should be personal and personalized in today's fast-paced global environment.
A customer opens their phone and sees alerts from their favorite streaming service. They have recommendations, updates for their daily commute to the offices, and recommendations about products based on their past purchases. These highly tailored recommendations fit their wants, needs, and interests across the board. Modern consumers now expect highly personalized digital experiences, just like they would in person, a key driver for the increasing demand for AI automation and Generative AI. The financial industry is no exception when it comes to these expectations.
Specifically, the last decade has seen financial institutions, like banks, insurance companies, and even treasury, migrate from traditional to digital, offering consumers 24/7 access to their accounts, balances, bank statements, and more, wherever they are and whenever they need it.
But what if these consumers had access to even more information? Would their banking provider see increased engagement if customers had customized recommendations and insights based on consumer behaviors, cash flow or balances, searches, account or app usage, location, or demographics?
An excellent place to start is defining automation-based hyper-personalization and the different types of financial services.
Automation-based hyper-personalization within the banking industry can be defined by the unique approaches used to offer products or services to clients. It is driven by AI (more specifically, intelligent automation and Generative AI) and various behavioral sciences that collaborate with the data collected regarding an individual's transaction history, shopping or browsing habits, location, etc.
AI-powered agents use this consumer data to create personalized, comprehensive profiles that financial institutions can later utilize to craft specific product offers that cater to an individual's changing wants and needs.
The future success of financial institutions will be largely dependent on the balance between integrating technology into service while also maintaining the integrity of data protection.
Below we dive deeper into the pillars of personalization, what guidelines you should follow to ensure best practices in implementation, and how to be successful in the financial sector.
Building trust is one of the most critical pillars to follow. With trust, your suggestions will be strongly considered; without trust, it doesn't matter how unique or customized communication efforts are; they will be seen as spam and most likely be ignored or deleted immediately.
Craft product marketing approaches to follow a solutions-based approach. Determine what your customers are searching for, what they need, and why. With this data in hand, you can offer more educational content and solutions when the need is in view.
Unification of data is essential. Typically, banks and other financial institutions have data in different locations throughout the organization. This incomplete data can place limits and sometimes even undermine your personalization techniques.
Choosing the best automation platform provider can make a world of difference. Druid's AI Agents can be tailored to fit your organization's needs and solve your specific problems. Druid's Agentic AI platform creates a unique, seamless experience for your customers while using essential data.
Want to Learn More About the Benefits of Using Conversational AI for Financial Services?
In the last few years, the financial sector has become far more transactional than ever before. The relationship between client and business is no longer just about the relationships with bank tellers and other administrative leads. Instead, modern consumers are much more in tune and concerned with choosing the right partner, one who doesn't charge maintenance fees and who offers the best rewards for partnering with them.
Often, financial institutions tried to cut costs and corners and lacked personalization. In doing so, they missed a huge opportunity. Today's clients seek valuable insight, advice, and guidance from their banks. However, it is difficult to offer any financial advice when there is no data about a customer's goals, priorities, or history, which many banks previously could not allow.
When banks invested in hyper-personalization efforts, they saw improvements in revenue and other benefits, including:
An excellent example of these benefits would be credit card invitations from companies. Most financial companies offer credit cards and many partners with major retailers. These companies compete for customers with competent incomes, good credit, payment histories, and revolving credit usage. This has built up a saturated market where many banks are forced to become even more competitive, tighten margins, and gamble with customers with lower credit scores.
Due to this increasingly competitive market, financial institutions have built value by competing based on customer satisfaction and their experience alone. Creating personalized content, enhancing relevancy, and marketing personalized products, crafts a more customer-centric focus and builds brand value and loyalty.
There are generally three types of hyper-personalization that banks and other financial institutions routinely utilize:
Prescriptive Personalization: Prescriptive personalization aims to predict a customer's wants and needs based on historical data. Financial institutions employ the business's overall goals, allowing marketers to use this method to create new rules and workflows that enable them to manage users simply.
Real-Time Personalization: Real-time personalization depends on current, live data and a customer's historical data to create a personalized customer experience as it occurs. For instance, marketing teams can utilize this type of personalization to make suggestions for customers as they are shopping on the website in real-time. This helps to enhance both customer engagement and communications.
Machine-Learning Personalization: This type of hyper-personalization utilizes intelligent machine-learning algorithms. Using AI-driven automation, companies can make smarter decisions regarding how best to communicate with customers based on their unique behaviors.
By using the functionality of AI, machine learning, and other data applications, you will be taking advantage of a massive opportunity within the financial industry. You can gain insights about your client base by using existing data and channeling it into tailored, hyper-personalized experiences based on your customer's interests and cycles. By successfully executing hyper-personalization, you automatically add value to your business and strengthen customer relationships and brand loyalty.
What does hyper-personalization mean for your business? Staying ahead of any learning curves as you familiarize yourself with your customers and use those insights, trends, and data into a highly-customized digital experience that contributes to overall activity and trust.
What does hyper-personalization mean for your customers? While some level of customization has been expected previously, a hyper-personalized experience in financial services can lead to increased satisfaction and customer engagement, fraud prevention, improved decision-making, and a feeling of human understanding from their banking provider.
Modern consumers believe that financial organizations should offer necessary services that may affect their future financial position as individuals. Financial services marketing teams must identify the consumer's perspective and offer them unique, customized communication tactics. By implementing the right technologies, marketing strategies, and insights, marketers in the financial services sector will see increased brand loyalty from customers and enhanced growth in market share.
Hyper-personalization strategies should be implemented correctly to work effectively. Acquisition costs are reduced, conversions are boosted, and revenue is increased. We hope we have offered a better understanding of successful hyper-personalization strategies and how best to implement them. As you begin to move forward in exploration and assessing ideas, keep this article in mind and incorporate it into your hyper-personalization strategies.
If you are a bank or financial institution interested in learning more about DRUID and how we can assist you in hyper-personalization, we are here to help. Click below to learn more.
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