Hyper-Personalization: A Key Enabler in the Digital Evolution of Banks

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Why Hyper-personalization?

In recent times, customers are looking for a customized solution for their specific requirements and utilizing the digital features of banking as per their needs. Moreover, banks have realized the economic advantage of personalization which can bring in considerable cost reduction by providing diverse cutting-edge products with AI, ML, and big data capabilities. Banks are now trying to cater to customer personalization while meeting the larger organization goal.

Also, it is crucial that the banks meet customer’s requirements and make them feel delighted and important. This is the key area to succeeding in business.

Real-time data combines with AI processing to control how you deliver content, suggest next steps, and respond to events, thereby controlling every touchpoint in your process map based on customer data. While it cannot be used to design strategies or replace multivariate testing, it can be used to control how you deliver digital experiences to users.

Hyper-personalization requires you to model different behaviors and use algorithms with real-time data to decide when the optimal time is to deliver a communication, alert, or offer. Every touchpoint in your process map can benefit from hyper-personalization by engaging with users directly and anticipating their needs accurately.

Hyper-personalization Benefits:

High customer satisfaction is one of the key achievements with the hyper-personalization improvements. The more ease of usage, the less effort and time is invested in the application by the customer. Customers are also moved away from unwanted regular business suggestions, which saves a lot of customers’ time.

Customer Segmentation: With the help of hyper-personalization, companies can segment customers based on the data collected and analyzed. The data tells us how the customers are engaged with our brand and what they are looking for. It will help the companies deliver truly customized ways to help the customers. Customers can be divided into subsets based on history, demographics, locations, and buying behavior.

Business outcomes: Businesses can also see a spike in revenue due to the implementation of hyper-personalization as it is always proven that customers are tied to the companies that provide personalized digital experiences. The hyper-personalization strategies applied to various customer applications will also improve the revenue and cross-sell of banking products, which is why agile methods are important for creating the blueprint with short cycles to investigate the engineering part if there are any ROI corrections.

Default transaction channels: Digital channels are now the dominant banking channels worldwide. As a result, customers are visiting the branches less and using the digital platforms for their banking needs. Over the years, it has been observed that internet banking and mobile banking have become key banking platforms with a huge increase due to portability and quick access to features in banking transactions. Banking institutions provide all the features in online and mobile banking for the customer, and we can use these channels through hyper-personalization based on customer usage and provide a better and more customized solution.

 Customer first

With Hyper-personalization it is proven that customer’s individual needs and requirements are more important to the banks than the bank’s solutions, which are based on the customer’s interests. In the past, the banks used to function in the mono process by providing the same options to all their customers. Now, the business model has been upgraded to meet the minute needs of the customers and tailor the policies and systems as per their requirements.

According to a recent study, the only way that banks can achieve hyper-personalization is by utilizing digital technologies like cloud-based applications, platforms, and infrastructure, mobile devices, and data analytics. This can closely gather the data of each customer, which can be used for different customer’s solutions accordingly.

The significance of data in hyper-personalization

It goes without saying that data is the heart of all personalization. To build a successful hyper-personalization system, we need to collect the right data to be able to tell what the customers truly want. The greatest challenge businesses face today is making the right assumptions out of the data that is being flooded with huge amounts of data. There are various channels through which organizations can collect the data from their customers, like browsers, mobiles, apps, and OTT platforms.

With customer data gathered, the next critical component is developing actionable intelligence. The level of complexity associated with unpacking customer data and using it usually has to do with the number of different customers we serve. More data about customers with various attributes means more opportunities to create better customer experiences.

As we explore our customer data, consider segmenting your audience by age, location, gender, satisfaction, brand interaction history, average purchase value, purchase categories, content consumed, traffic source, and exit points. The idea is to spot as many attributes as possible, which will be applied to spot trends and help deliver data-backed decisions.

IoT in Hyper-personalization

IoT and personalization really belong together in the same breath. One facilitates data and the other represents opportunities for the use of that data. IoT devices like smart watches, TVs, streaming devices, and Internet-enabled home controls are the key IoT devices to gather customer data, which are useful, convenient, and innovative ways of reaching customers with personalized content and recommendations. Also, IoT devices play a key role for business brands in customizing product details and services for individual customers with AI and ML processes.

AI/ML Implementation

With Robotic Process Automation and AI considered to be the key solutions for the future of banking, AI is believed to drive the wave of automation in banking by increasing capacity and helping employees focus on higher-value projects. In the finance industry, AI and ML are emerging as powerful methodologies. Banks are more customer-centric with hyper-personalization as a core component of any initiative that they undertake. They understand that everyone’s needs are unique, and they need to customize products accordingly.

Banks can easily handle a large pool of customers by adopting technologies such as chatbots to cater to the huge demands of customers, thereby driving hyper-personalization. The solution also drives operational and cost efficiencies, driving profitability with the help of artificial intelligence. Banks can partially or fully automate their digital processes, increasing operational and cost efficiency, which translates into greater profitability.

The AI and ML solutions are created by the data collected from the customers and provide a prediction model based on large amounts of data pumped into the machine learning algorithms. This model will then be used to help the customers based on their needs. The production systems are linked to the machine learning pipelines so that the machine will automatically predict and take the decision based on customer behavior on the website, mobile app, or any other platform. The outcome of this would be customer delight and accurate solutions to the customer’s needs according to their age, gender, and specific product requirements. AI/ML is emerging technology with high prediction and positive success ratio and most of the banks are already halfway through in implementing these solution engineering.

Banks used to develop campaigns around segmentation models that were marketed toward specific groups. They would try to drive conversions through the funnel regardless of where an individual was located, without any knowledge of whether their circumstances indicated they were ready for a new product.

However, the ideal way is to start with raising awareness and end with selling a product or service. Banks can target people in need of specific products at the appropriate time as per their requirement. Hence, the time has come through hyper-personalization solutions through which banks can keep up with individual customer requirements. The solution steps/process play a key role in achieving the right hyper-personalization.

Solution Process

  1. Data Collection

It is very important to arrive at a solution unless we have the data to make the prediction. Hence, it is critical in achieving hyper-personalization to data collection, organizing, and utilizing as much data as possible about our consumers. The data gives you insight into behavior patterns and helps you create an ideal customer profile. It also empowers you to create hyper-personalized messages, products, and services to draw in and retain your ideal audience. The precision of hyper-personalization is determined by how well we know our customers. So, it is very important that organizations collect the data using the latest technologies and make use of AI/ML techniques in utilizing important consumer data.

  1. Hyper-Personalized Content

Based on the data collected from the customers’ behavior on various platforms, we are ready to create hyper personalized content. It is very important to understand the hyper personalized content for the customers as we need to refine the solution continuously based on the customer profile and customer usage patterns. When we create the solution or suggestions for the customer, it also matters how we track the customer’s acceptance and then use what you’ve learned to iterate and create a good version of the thing.

  1. Data processing

The collected data is preprocessed and made into a list of errors in the data, which then removes the junk data. With this, we can clean the data and give it a proper structure before any AI algorithms are used. The algorithms work correctly on the clean data and make accurate predictions. The data is analyzed and worked on the missing values, and various graphs are applied to check the data distribution and then work on the skewed data to make it algorithms ready for processing. It is then tested on the data and then deployed to production for solutioning.

  1. Implementation

The system is then made ready for the customers and deployed to the applications. The banking systems will then send the notifications to customers based on their activity on the applications. The solution outcomes are involved through various channels like emails, SMS, social media, web notifications, and app notifications, to name a few of the current solution platforms.


Digital banks today need to keep up with the ever-changing technology landscape and the 24/7 demands of customers. Banks must provide complete functionality in the highly fragmented mobile and browser technology fragmentation, as well as end-to-end validation of both front and back-end systems and ensure a consistent multi-channel delivery experience with the best usability and compatibility.

Cigniti, with its experience of being the core banking digital transformation and testing partner for pioneering mobile-only banks in the US and UK, has vindicated its capabilities in areas such as omni-channel banking, retail banking, corporate banking, centralized banking, mortgages, cards, and payment gateways. Cigniti has in-depth experience in testing across diverse industry standard products such as T24, Finacle, Flexcube, Bancs24, and Vision Plus, and compliance with regulations such as BASEL, BCBS 239, SEPA, AML, FATCA, etc.

Schedule a discussion with our Banking domain experts to learn more about how hyper-personalization is becoming a key enabler in the digital evolution of banks.


  • Amar Sindol

    Amar Sindol has 17 years of testing experience in Digital Banking including cash management and digital image processing technologies. He holds good experience in quality assurance delivery and banking domain consulting, and he is part of Delivery as well as BFSI CoE with Cigniti Technologies.

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