Real-time Hyper-personalization for consumer banking
Tags: Real Time Marketing, Hyper Personalization, Artificial Intelligence, Data Science, Banking

In 1991, India opened its markets for the rest of the world’s big brands to come in. From then onward, we have been riding a tremendous consumption wave in the retail market, so much so that the domestic consumption market is one of the key backbones of the economy. Along with this liberalisation of markets, we have also been witness to liberalisation of tastes and aspirations, especially as the economic middle class grows larger. Fast forward to the digital era of today, where we have more options but not enough time, more interests but not enough deep bonds, more brands at our disposal but not enough discerning ability to choose well, and more importantly, a disposable income higher than previous generations.

Such rapid change has made many in the population eligible for a new best friend, “the credit card”. He loans us money when we need it and makes us feel powerful in the age of aspirations. In return, we only have to make sure that we pay him back. Well, thankfully, in India, Banks give out credit cards to people who are capable of paying their bills.

Banks have been pushing credit cards to eligible individuals based on metrics around income levels and credit scores obtained from the bureau. We have also been subjected to the often untimely telecallers or accosted on the way to the train station or to the movie theater by eager marketing agents. Even for “pre-approved” offers, the process of getting a credit card itself is itself filled with various friction points, including delays in intimation, frequent asks for more and more documentation, and no adherence to any kind of TAT whatsoever. Is the pre-approval then a case of mere lipservice then?

Once we sign up as a customer, regular usage brings in a new wealth of information to the card-issuing bank. We then get subjected to offers of a different kind — that of partner merchants with whom the bank has made new agreements, irrespective of whether the partner offerings is relevant to us or not. Take the case of that “Travel voucher worth ….” offer we have all got when it is fully clear from earlier spends that we have blown our non-essential budget for that year on an earlier trip. Does the bank really understand its customers’ preferences? Is it the case of the business measuring success of a partner relationship only based on impressions, and not conversions? Are HNIs the only worthy class of customers for whom targeted outreach programs are conducted?

We believe AI has the potential to solve these problems, and more.

First, with behaviour mining, what seemed like an impossible task of knowing our wishes has become possible and accessible.Banks have been generating massive data from debit and credit card transactions, net-banking, product & services consumption data in addition to the core banking data. These data points hold the representation of our choices, preferences and wishes. They tell stories about us, playing a transformative role in the way a customer is seen. By juxtaposing these data sets, AI can help banks understand each customer’s journey and personalize in nuanced ways the next interaction that happens. We might not choose that Uber ride today, but would it not be nice if somebody were to anticipate that a ride home is probably what we need after a hard day’s work?

Second, understanding behaviour of all customers was a costly proposition a few years back. Big data and cloud-scale technologies were still evolving, while legacy solutions were exorbitantly expensive and lethargic in its agility. It used to be cool if a marketer did something special for birthdays of HNIs and wealth management clients.However, in this digital age, a social media user fully expects even strangers who have become “friends” only recently to wish them on their birthdays. Banks that owe a lot to the customer need to do a lot more, that too for every user. This brings us to the automation possibilities of AI. By being able to deploy personalization automatically at scale, no customer is ever left behind.

It is this power of AI that we at TheDataTeam have packed into Cadenz, our powerhouse customer intelligence platform (CIP). At the base of Cadenz is an internet-scale customer data platform (CDP) that gathers and integrates the information from all customer interactions, usage journeys, channel and partner touchpoints. It then delivers real-time, relevant and contextual interactions for 100% of users, guaranteeing excellent customer experiences. At its core, the value proposition that drives this innovation is a suite of algorithms that are able to profile customers and understanding their behaviors, interests, preferences, financial aspects almost instantaneously. By leveraging the scale of big data technologies and the elasticity and agility of cloud, Cadenz is deployed in any customer environment to deliver value in a matter of weeks.

Our recent Cadenz deployment for a leading multi-national bank reached customers with real-time offers on products the customers wanted to buy in the location they were in within moments of perceived interest. The relevance behind the offers ensured the customers kept using the bank’s credit card at more places eventually resulting in 20% uptick in transactions by volume. Moreover, due to increased relevance, actual follow-on purchases at partner merchants increased too resulting in a direct contribution to the partner relationship topline of another 30%. Wouldn’t you agree that these are far more impactful success metrics?

About the author:

Sunil Saini, Product Head, seasoned professional with experience in creating data science driven solutions and products for Financial services sector.