The rise of digital payments has changed how customers bank -- a transition further catalysed by COVID-19. Digital payment doesn’t just help in boosting online and offline sales, but the increasing number of digital transactions has resulted in generating an astronomical amount of data every day. This data comes in the form of both transactional and non-transactional customer data.
Banks and fintechs can rely on this big data to improve processes and deliver good product experiences to customers. It can be leveraged by employing technologies like big data analytics, artificial intelligence and machine learning.
In an increasingly digital world, customers expect banks to connect all the dots and deliver seamless, personalised experiences. It is essential that banks and fintechs are able to understand and anticipate their needs, and respond accordingly. Understanding the data that customers share, therefore becomes key to this responsiveness.
Big data offers valuable insights into user behavior and can help banks build and optimise their customer experience accordingly. Here’s how:
The more banks & fintechs know their customers, the better they can serve them. Customer segmentation is a common practice in the financial service industry. However, it lacks the granularity required to truly understand their customers’ wants and needs. Big data in banking can help take this segmentation to the next level by building detailed customer profiles using insights into:
Banks can use these profiles to proactively engage with their customers, target the right markets and offer relevant products at the right time. Therefore, generating new revenue streams and increasing the value of every customer.
Consumers today interact with banks through multiple channels, whether for information or transactions. Analytical insights from mobile and web application usage can be used to determine if customers are facing difficulties in their interactions on any channel.
From identifying drop offs during payments, to ensuring seamless interaction across multiple devices -- banks can leverage big data to fine tune processes and create smooth digital customer experiences. This will help improve customer satisfaction and therefore, impact retention rates positively.
A study by Oracle on “The Era I Enterprise: Ready for Anything”, found that 84% of its respondents noted a trend toward customers wanting more individualised and tailored experiences. The report also found that offering users what they need, can bring up the annual revenue by 18%.
While the number of online payments is growing, the number of frauds is also on the rise. Banks and fintechs need to focus on providing additional security on transactions to safeguard sensitive customer data.
Advanced payment security features like 3DS 2.0, second factor authentication and risk-based authentication are examples of how big data can be harnessed to secure transactions. They make use of multiple data points such as payment history, device, channel, location etc. to identify unusual purchases and flag them for review before completing transactions. Secure payments can help banks reduce the risk of frauds and ensure safety of customer data. Therefore, building trust and nurturing relationships with their customers.
There are a lot of financial institutions in the market with multiple products, catering to the different customer needs such as hassle-free loans, online payments and more. The customer is spoilt for choice. Good customer experience, thus becomes a critical factor in determining who gets the business. Financial institutions that employ big data technologies will be able to understand customers more intimately and proactively meet their needs in real time. They can target and communicate with their customers efficiently on channels that are relevant to them, and eliminate redundancies.
If customer centricity is a key differentiator, data is the backbone. Banks leveraging insights from user data to deliver customised experiences will be able to better serve their customers and therefore improve their own scalability in the long run.