This whitepaper is the result of AIMixer roundtable that happened on 25th Feb in Mumbai, in association with Comsense consulting & Acoustic. The roundtable was attended by more than 20 analytics heads from Indian forms whose inputs helped us curate this thought paper.
Banking, financial services, and insurance (BFSI) institutions are servicing their customers across multiple channels. Customers expect the experience across all the touchpoints, including online, app, IVR banking, in-person banking, and personal banking among others, to be not just seamless but also highly personalized. In India, despite the introduction of digital services, millions of customers still visit bank branches to carry out different types of transactions that include withdrawing or transferring funds, updating their passbooks, opening a fixed deposit or trading account or taking a loan.
On the other hand, digital transactions through online and banking apps are gaining significant traction, with millions of consumers from diverse demographics purchasing goods and groceries through digital wallets across physical stores, e-commerce sites, and other shopping apps. Many more customers are buying or selling securities and bonds online, and even paying utility bills through the banking apps. Interacting with these hundreds of millions of diverse customers is a difficult proposition. Banks often find it difficult to understand and anticipate the unique needs of their customers, who are becoming all the more selective and demanding when it comes to the level of service they expect and the satisfaction they derive from their banking experience.
On another level, it is important for BFSIs to utilize analytics to provide financial advice to their customers over and above the leveraging of analytics for customer insights. Moreover, it is important for BFSIs to understand not just existing customers but also under-developed communities. These prospective customers of basic banking services are not aware or do not have access to legitimate credit and borrowing rates – analytics could transform their experience of borrowing or taking loans.
UNDERSTAND THE CUSTOMER DATA AND CHALLENGES
Understanding customer data is crucial to reach a level of responsiveness and satisfaction that ensures the customer does not switch his or her financial firm or bank. When BFSI companies harness data in appropriate clusters, they are able to offer relevant services at the right time to the consumers. The banks can then drive new opportunities, generate revenue streams, and increase the value for every customer. This is all the more important in an environment in which banking executives are mandated to lower costs and improve processes while maintaining, if not exceeding, customer expectations.
According to a report from Forbes, data-driven enterprises (including BFSI organizations) are 23 times more likely to acquire customers, 6 times as likely to retain those customers, and 19 times as likely to be profitable as a result.
Customer data from every service channel, including websites, mobile apps, branches, and call centres, help develop insights to improve customer experience. Data is generated with every transaction, and analyzing that data enables the evolution of decisions that lead to greater customer experience and transactions with the same bank. BFSIs are utilizing real-time data analysis to determine the customer segments that are most likely to buy a certain product and what time – the right time in the customer journey when cross-selling could take place. These insights help prevent customer churn.
UNDERSTAND THE CUSTOMER’S PREFERENCES
One of the key points that emerged from the BFSI roundtable was the leveraging of analytics to identify the channels or ecosystems the customer is most comfortable with or most frequently uses.
Once identified, interactions with the customer at the next opportunity, stage or level should be carried out mainly / only through those few channels. Other channels of interaction that the customer usually does not use or interact with should be avoided in terms of contacting the customer.
One example provided for this was the payment of insurance premiums. If the customer primarily uses his banking app or the website for the payment of the premium, it is evident that the customer is tech-savvy. Hence, the bank or the insurance agency, rather than calling the customer through IVR or the contact centre, must send a push notification and link via the app or an SMS, which the customer could click to directly make the payment. This ensures that the contact with the customer is through a channel that the customer is comfortable with – the banks need to leverage analytics to identify this preferred channel of service on the basis of past trends of payments and interactions with the bank.
MOVE BEYOND SILOED SYSTEMS
To enhance the customer experience, it is important that the data related to customer interactions across the various channels are accessible in one single place.
This enables near real-time data analysis. This requires removing the data from siloed systems, enabling data analysis both holistically and granularly – and understanding the issues faced by the customer, predict customer behaviour and outcomes, analyse patterns and improve processes. Traditional data analysis typically involves a cycle of pulling data on a predetermined schedule and applying algorithms to produce predictive and prescriptive analytics. None of these analytics is integrated—data resides in siloed data warehouses, where separate, unrelated reports require separate analyses. BFSIs must move towards real-time analysis across a variety of data types, no matter where the data resides.
As the next step of action to the point above, BFSIs must build a single view of the customer from all the siloed systems – to do so the analytics’ function must leverage the cloud to create a data lake that houses all the relevant information of the customer from the required siloed systems. As and when information on the customer is required, such as his or her preference for payment of utility bills, the information can be retrieved from the data lake.
As a first step towards this, it is crucial to understand why data consolidation and granularity is desired. For this, specific use cases should be defined for the data – what is the objective of the single view of the customer transactions and data. These use cases could cover cross-selling products to the customer or providing the latest term deposit details on a regular basis. If cross-selling is the objective, then only the relevant data related to the history of investments or preferred channel of payments (credit card, debit card, or prepaid card) should be collected from the siloed systems, stored on the data lake, and subsequently analysed. A relevant financial product can then be identified for the customer. These use cases should be well defined so the analytics engine can run the required algorithms on the customer data to generate the relevant results.
To facilitate effective data gathering and analysis it is essential that an overhaul of legacy systems and processes at BFSI be carried out as most of the systems/processes were adopted as far back as the 1980s.
Moving to a digital platform, where a single point of view of customer preferences and analytics, requires a mindset change to move from legacy systems and processes to a digital, data, and analytics-driven customer-centric process.
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