In the current digital era, customers expect personalized experiences from banks, and those that are not able to provide such experiences may risk losing customers. According to Salesforce research, 66% of customers expect companies to understand their unique needs and expectations, and 52% of customers expect offers to always be personalized. Banks have started investing heavily to focus on hyper-personalization which acts as a key to unlocking the potential gains and providing customized services and products to their customers.

Hyper-Personalization – An Introduction

Earlier banks used to follow a “one-size-fits-all” approach, where they offered the same products and services to their clients irrespective of their needs. This means the customers within the bank would get the home loan at the same interest rate, debit card, or credit card with the same features.

In the last few decades, we saw that banks were able to provide specific products and services to certain customer groups based on basic demographic information like name, gender, and location. In such cases, banks configure bulk personalization rules across the segments.

In recent years, due to technological advancements and with the help of real-time data, banks can now target each customer with personalized offerings. Hyper-personalization is the process of delivering personalized experiences to individual customers using behavioral science, data science, and artificial intelligence. It delivers customized products and services and, hence provides added value to the customer. Banks could differentiate themselves by adopting hyper-personalization, which in turn will also help to create loyalty and trust among their customers.

How to Enable Hyper-Personalization

To cater to the ever-changing expectations and needs of customers, banks must adopt a customer-centric approach and deliver products and services accordingly. Enabling hyper-personalization would help deliver services based on customer behavior, and data is a medium to drive hyper-personalization. Banks typically get access to an enormous amount of customer data, such as transaction data, customer basic information, account details, products/services availed, etc. In addition, banks could collect complementary data from third parties to create a comprehensive view of the customer and use all this information to provide personalized offerings and experiences. Customer Relationship Management systems or Customer Data platforms help store and manage customer data with various attributes.

With the help of data analytics and AI, the collected data is analyzed, and prediction models or insights are created for the customer. The real-time data of customers gathered from channels such as a website, mobile app, or any other platform will be linked to these models so that the system can predict and take decisions based on customer behavior accordingly.

Also, the adoption of cloud technologies enables the ease of data collection, storage, and retrieval as and when needed. The bank needs to ensure that the suggested offerings are tracked in terms of customer acceptance and that any improvements that are seen are iteratively built to provide a better version of the offering.

Use Cases of Hyper-Personalization in Banking

Some hyper-personalization use cases that are getting adopted with faster momentum:

• Personalized loans: Banks can provide personalized and pre-approved loans to their customers based on the data collected from various sources, such as credit ratings from credit rating agencies, customer risk ratings, and data related to spending/payment details available within the bank or from account aggregators.
• Customized Product or Service recommendations: A bank could provide customized product or service recommendations to customers by using customer details. For example, Banks could recommend a credit card with reward points to a customer if his or her shopping frequency is high.
• Targeted Promotional Offers: Banks target customers with promotional offers based on spending and transaction analysis. For example, if a customer is a frequent client of a restaurant chain, banks will generate discount offers for the customer when the purchase is done through their credit or debit card.
• Predict customer needs: Customer data analytics, along with recent bank transactions, provide an idea of the customer’s needs. As a simple case, when the customer changes from minor to major, banks could identify whether he or she will be looking for an education loan and, later in life if the person is looking for a vehicle and home loan accordingly.

The Way Forward for the Banks

Currently, many customers do not perceive the products or services offered by banks as personalized to meet their needs. Banks must thus move from a product-centric to a customer-centric model. A comprehensive view of customers, as well as data analytics, behavioral science, and AI, will be critical for the adoption of hyper-personalization.

Even though many banks have all the necessary data, some banks struggle to obtain a comprehensive view of their customers because the data is spread across multiple lines of business within the organization, which operate in silos. Business, Marketing, and Technology teams should work together to get a comprehensive view of the customer and thereby create a unique user experience. Some of the established banks still fall behind in terms of their capabilities to analyze vast amounts of data and thus need to adopt these technologies faster.

The use of customer data for hyper-personalization leads to questions regarding data privacy and customer consent. Banks must ensure that they collect these data ethically and are utilizing them with respect to compliance regulations. With hyper-personalization in banking, every customer can get the right product or service at the right time through the right channel. Banks that deliver personalized services will gain a significant advantage in the future, and thus most banks are focusing on developing capabilities around hyper-personalization.