In the digital era, many companies are still struggling to transform their business to meet customers’ new expectations. In this scenario, the COVID pandemic has accelerated a transformational process, leaving many institutions with only one possible way: adopting digital solutions in their everyday operations. Early adopters of the digital transformation process and digitally native companies are steps ahead in this journey with innovative banking data solutions but the truth is, whether we look at a traditional bank that is transforming, or at a fully-digital company, both of them need to think about how to improve their digital interactions in order to play a relevant role in customers’ lives.
For that, in a world where digital transformation is the only path and where competition is increasing every day, the key to market differentiation is data. Nowadays, the perception of data has changed. Instead of being seen as a resource to control a business, data is now a strategic asset to guide decisions and, for that reason, banks (and any organization) should be aware of how powerful and valuable data is.
Banks have valuable information about their customers. They know how much their income is, what their spending patterns are (how and where they spend their money) and they also can take advantage of their high volume of customers to find common patterns and try to predict their behavior.
But the main problem is that in many cases, the information is dispersed across the organization, separated in silos per departments or areas, where each department is the owner of that data and the only one with access. For example, the digital channels department knows when a customer simulates a loan application in the mobile app, but the commercial representative who receives the customer in a branch isn’t aware of it. The marketing department knows when a customer clicks on an email offer for insurance, but the telemarketer who receives the call from the customer does not. And those are common situations that happen every day.
Because of this, to start building a data-driven strategy, banks need to develop their data model and for this to happen, it is necessary to keep an eye on the quality of the data. The first thing to do is integrate it, stop thinking about separated silos, and start thinking about linked datasets. Easy to say, but not easy to do, since the bigger the bank, the more silos will be found. But, linking and unifying data could also generate other problems related to consistency and accuracy which are critical. Problems like finding the same customer named differently in two departments or figuring out which is a customer’s most up-to-date phone number are likely to happen daily during the process.
Having a healthy banking data model is necessary to get a better understanding of the business and customers. A better understanding of the business means finding banks’ pain points in their operations and services. These could be finding opportunities for improving internal processes, identifying bottlenecks in customers’ attention channels, and other kinds of problems related to the core business. When these are solved it could generate major efficiencies in the business.
A better understanding of customers means thinking about what banks can do to be more relevant to customers. For example, banks could provide tools to help them better manage their finances as well as make smart and contextualized interactions with them. Providing tools to help customers means thinking about how to incorporate digital banking services into their daily lives, and leverage them to know which is and will be the state of their financial situation. To do that, banks need to help customers answer simple questions like “can I afford a new tv?”, “which are the services that are due next week?”, “in which type of products or services am I spending my money?”. It consists of looking at customers’ spending patterns and warning them about recurring payments that the customers might be paying without their knowledge, or alerting a customer when they are spending too much on their mobile phone bill, as compared with customers in similar financial situations.
Making smart and contextualized interactions with customers is all about giving the right information to the right customer at the right time. It implies a deep knowledge of a customer’s behavior and also their preferences and goals, and doing this will give clients the feeling that their bank really knows them and understands their needs. For instance, by leveraging artificial intelligence and using historical transaction data to forecast or predict future events, a bank can let a customer know when their credit card expenses could lead to a liquidity problem in the near future. By doing so, banks can position themselves as trusted financial advisors. The value delivered in this simple interaction is huge and the main reason why it is so important to contextualize each touchpoint with customers.
These are the pillars, based on data, that deliver a truly personalized experience to customers and play a relevant role in their lives. Thus,banking data is truly important and banks need to consider it an asset going forward.
The banking market is currently being challenged by plenty of companies providing financial services with new customer-centric business models. As such, banking data is probably the biggest advantage banks have to maintain and grow their position. After all, banks have been providing payment, loans, payroll, insurance, and lots of other services for years, and by doing so they possess an invaluable data source of customer insights waiting to be discovered and utilized.
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