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Personalization at scale is a key strategy for fintech companies to offer highly relevant products and services. In this VB spotlight of his, learn how leading fintech companies are delighting their customers and building powerful relationships with AI-enabled platforms and data sources.
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“There is a direct correlation between product-loving customers and revenue,” said Bala Chandrasekharan, Vice President of Product Management at Chime. “Customers are much more engaged and more likely to recommend your product to others. Referrals are an incredibly powerful viral marketing channel for him compared to paid marketing.”
And to do that, you need true personalization. Chandrasekharan, his COO at Tricolor Auto Group, David Goodgame, and Eric Jamison, head of banking and technology products at Envestnet, believe that personalization at scale will give fintech an ever-greater competitive advantage. and talked about how AI and analytics are changing the game. During his recent VB Spotlight event.
Personalization case study
For Tricolor Auto Group, an automotive retailer and direct financier in the used car sector, personalization means delving into the deep desires behind customer demands.
“All our efforts here, from marketing to the inventory we choose to put in our sales lots, are focused on what we call work to be done,” said Goodgame. Told. “We looked at our business as a whole and thought, ‘When a customer comes to us, what do they want from us? How can we ensure that this issue is addressed in our customer service center?”
The “job to be done” is a customer who is worried about their credit, a customer who wants a lifestyle like the American dream, a customer who is working on a big project, etc. Personalized advertising is worth it Sell your proposal in the form of a car or a loan.
Achieving that will require connecting consumer relationships and use cases, where good data is key, Jamison said. Envestnet, as his provider of B2B2C services, helps lenders when they need to fully understand a loan applicant outside of a credit report, or when they don’t have a credit report. That data may include cash flow information such as income and expenses from banks and other providers.
“It’s been very helpful in personalizing applications to their consumers, enabling providers to make more informed decisions, and connecting dots that might otherwise not be visible to their consumers. he explained. “It’s about connecting consumer needs with our ability to do something and making sure they match. That’s going to lead to the best results.”
How AI and Machine Learning Will Change the Game
“Our AI risk model is the secret sauce behind us,” said Goodman. “What we believe is that if a customer goes anywhere else in America, they’re all thrown into one bucket. That one bucket looks very predatory to that customer. It’s a set of conditions, it’s going to be a state cap on interest rates, it’s going to be a bastard, and being affordable to that customer is never part of the conversation.”
Goodman said about 90% of the applications the company receives have no information on any credit bureau. But the vast amount of data they collect from a wide range of fields can identify what he calls a more reliable scoring system than the FICO score, allowing them to offer lower rates to those without credit data. .
“Our risk model allows us to sell cars with very low losses,” he explained. “Then we can bring the price down, and we can bring in more borrowers. The more we do this, the more data we get, the more applicants we can get, and the more the model becomes. As we get smarter, the flywheel effect starts to happen.It gets tighter.We can even shorten the contract period.More and more we are able to take out the risk and offer better terms.Better terms. The more you present, the more customers you will win, and that flywheel effect is real.”
And in that way, they are contributing to an often-overlooked demographic boost, establishing their financial history and allowing them to start building credit.
Getting all the application data also helps move the risk model up the funnel. The higher you go in the funnel, the more personalized marketing you can achieve. When a customer comes through a particular channel, you can identify their interests, needs, and context to ensure the content they receive is relevant to them, so they They feel their own needs, which increases conversion rates. , etc. — being seen and met.
This also applies to chimes. Chime aims to provide accessible financial services to Americans who may have been denied traditional banking services.
“In this world, AI and ML play a big role when there is not much clear public information available,” says Chandrasekaran.
For example, when a customer’s record is marked negative, it is important to distinguish between irresponsible behavior and unfortunate circumstances. The question is to understand customer behavior patterns: how they have used the platform or product before, what the negative events look like, what value they might bring. It depends on how you read it.
“That’s where AI and ML play a big role in trying to figure out how to tell the good from the bad,” he said. “This actually enables the flywheel effect I mentioned earlier. In this case, if you know they are good customers, you can drive a great and enjoyable member experience. These are moments that matter to your customers, and if AI and ML can be used to respond appropriately, they are likely to ultimately turn into enjoyable experiences and long-term loyal customers. They may refer your product to others.”
According to Jamison, the power of data comes from identifying patterns, and that requires the largest possible pool of data. Envestnet operates on the basis of approximately 40 million consumers and their regular transactional activity, which allows the company’s data scientists to identify key behavioral similarities, he said.
It could be identifying ways to act in your financial portfolio to save money, or helping your financial advisor scale by providing wealth management advice to the masses. This avoids the danger of taking a one-size-fits-all approach: losing the majority of your customers.
“We are all individuals and unique, but our patterns usually match someone else’s,” Jamison said. “We will be able to start reconciling these intersections to identify the next best actions, which will help consumers achieve better financial outcomes. Our platform and how they apply AI and machine learning empower consumers throughout their lifecycle.We deliver the right solutions at the right time so our clients can help their customers.This is the power of data. , helps us understand consumers across these broad segments in a very targeted and specific way.”
If you want to learn more about driving nuanced hyper-personalization at scale, overcoming data and privacy challenges, and more, don’t miss this VB spotlight.
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agenda
- How FinTech is leveraging personalization at scale to gain a competitive advantage
- A range of AI-enabled technologies to securely collect, enrich, and analyze financial data
- How Advanced Analytics and Transactional Data Provide Valuable Insights for Customers
- How to identify customer acquisition, cross-sell, and up-sell opportunities
- How to create personalized experiences that are relevant and emotionally ‘sticky’
Presenter
- David Goodgame, COO of Tricolor
- Bala Chandrasekharan, Vice President of Product Management, Chime
- Eric Jamison, Head of D&A Products — Envestnet, Technology and Banking Products and Design
- Mark Korakowski, freelance writer and editor. Lecturer; Former Financial Services Professional (Moderator)
