Personalized loans changing the way banks market their loan products
Experience is the new product in the financial industry that is going through hyper-personalization. How many times have you responded to a call offering a Personal Loan or an Auto Loan where the call center agent seems to be selling loans like vegetables, with knowledge only of the product and none about the customer?
With years of direct unsolicited e-mails and mobile push campaigns, consumers turn disinterested even if they do need a loan.
But why would anyone be disinterested if he’s getting what he’s looking for? The answer is experience. What starts as a useful conversation turns into a futile argument when the customer realizes the amount of documentation and amount required for availing those loan products. But what if the bank calls you only when they are sure that you’re a qualified consumer to avail the loan? That’ll mean you wouldn’t have to go through a long process of documentation only to later realize that you can only qualify for the fraction of amount you were promised or worse still, not qualified for the loan at all!
Consumers therefore are seeking meaningful insight and want to be understood as individuals, and they expect service based on their unique needs. Unfortunately, those needs are not being met by traditional financial marketing campaign methods.
A relentless push for “Do you want this product of the month?” messaging does not build a relationship. Banks must strive to match the needs of a household by understanding a consumer’s journey at every specific touch point in order to improve trust.
The rise of AI
Technology has slowly started to resolve these challenges to bridge the gap between the customer expectations and the bank’s personalized offering.
With the help of quality customer data that can typically be sourced from their employers, banks can create hyper-personalized loan offerings for each and every employee of an organization.
For instance, YES BANK offers the Project BlueSky, an analytics driven process for pre-qualifying customers for loan products basis third party/alternate data. This is a dynamic analytical model wherein the pre-qualification model depends on the richness of the data quality.
Some key data points which help in building a robust model include Profile-related information of the customer (salaried/self-employed etc.), Income related data (directly available or through imputation basis surrogate detail), Document detail (PAN/Aadhar etc), Address detail (pin code etc.).
Basis the data available, the customer can be pre-qualified for single or multiple products along with the commercials. This model also helps in identifying the customer population and their propensity of consumption for a product and thus making any customer campaign more targeted. An Artificial Intelligence based approach gives every customer a personalized experience with exclusive loan offers catering to multiple needs across customer life cycle.
For the consumer, these offerings come as a delight as these require minimal documentation and assured outcomes. Successfully interacting with consumers after all isn’t only about product push – it’s about understanding needs, building trust, and reinforcing relationships.
This new customer-centric approach is a big shift from a product-centric one and allows banks to know when to sell and when to serve or even to decide when doing nothing at all is the best decision.
The use of analytics thus allows banks to proactively initiate retention or sales conversations at the moment an issue may arise. Quicker the banks understand this approach, quicker consumers will be able to get rid of those pesky calls that lead to nowhere and leave everyone frustrated.