AI, automation, and data are the future of the business world. But, it will take time for many organizations to determine how to make the most out of them. Tech companies such as Green Dot are taking the lead, and they are an example of how machine learning and data drive business growth.
In this episode of Data Point of View, our host Laurie Hood welcomes Simran Singh. He is the Vice President, Banking as a Service at Green Dot Financial. Simran shares details from his incredible career. Although he did his undergrad in computer science and mathematics, he didn't become a techie. Instead, he turned to business and entrepreneurship.
Key Insights
Our goal is to be a partner rather than a vendor
- The Green Dot team is on a mission to provide an exceptional customer experience. Also, they want to ensure their customers, such as Uber, understand their employees' financial perspectives and needs. "Nobody knows the driver's driving preferences or timing preferences better than Uber. But helping them understand the financial services aspect and back that up using data we've gathered either through clustered analysis or some other behavioral patterns that we've recognized. That is such a powerful way of building a partnership, not a vendor relationship. They then understand, 'Okay, if we work with Green Dot, not only are we getting access to the best-in-class financial services, but also a partner that will work together with us to understand what is the best experience we can provide to our drivers.' And that's what matters the most to them."
Quality outweighs quantity
- As Simran explains, their vision of long-term success doesn't revolve around building a one-size-fits-all product. Instead, they focus on identifying the population genuinely interested in their offer because, at the end of the day, they want to make people's lives easier and bring value. ''When it comes to handling people's money, the population that we're serving is mostly unbanked or underbanked. So when I say you're going to get quality over quantity, we don't want to sign up 500 million users and then offer one thing that works for them and make money from that. We would rather identify the population that will benefit from our services and offer them better quality support. So better quality features, better quality support, better quality experience, everything included. So that's what I mean by quality over quantity. It's not, 'Hey, we will unleash every feature we can build.’ No, we want to understand what it is that works for a specific population.'''
We don't do data analysis just for the sake of doing data analysis
- Although every tech company relies heavily on data, you still need the 'why' behind any data analysis. ''There always has to be either a business problem that you're looking to solve or some form of additional insight that this data analysis would provide. So I don't want to know that 75% of our transactions happened between 6:00 PM and 9:00 PM. So what? What if we were able to incentivize users to spend more from 6:00 PM to 9:00 PM because 75% of our transactions are happening at that stage? Those are the insights that the data business insights team is supposed to bring to the leadership team to be able to drive our business both in terms of getting new users and making sure that existing users get more value out of it.''
