Consumers’ expectations for simplicity and convenience is at an all time high. To meet these growing demands, financial institutions must embrace AI-driven messaging applications and voice-activated devices. And it all starts with clean data. Without clean data, chatbots can’t answer basic questions and AI is anything but intelligent. Leverage the learnings from Mercantile Bank of Michigan to find out how your financial institution can use AI-driven technology to successfully drive customer engagement and loyalty.
Join Keith Armstrong and John Schulte to find out how they use AI technology to increase engagement and reach new customers.
In this 60-minute webinar, you’ll gain 3 main insights:
- How to evaluate your organizational readiness
- how to select the right digital channel to start with
- How to drive adoption through product marketing
In a changing world where 50% of home loans and 38% of auto loans are provided by a non-FI, how do you compete? The answer lies in how you collect, analyze and act on data. James will walk you through how to lay the foundation for an AI-driven approach to big data.
What You’ll Learn:
- Why data-driven financial institutions get better results
- How to build a foundation for a data-first approach
- How and when to use AI and Machine Learning in your financial institution
About the presenter:
James Dotter, Chief Financial Officer at MX, brings more than 16 years of management experience and financial expertise with industry-leading, billion-dollar technology companies. Previous to joining the MX team, Dotter managed finance and operations at InsideSales.com, one of the fastest growing tech companies in the US. He led InsideSales.com through five years of more than 100 percent revenue growth, directed talent acquisition of more than 100 employees per quarter, and raised more than $140 million in private equity financing, while building and maintaining strategic partnerships.
Jim Marous from Financial Brand and Don MacDonald from MX
Come join the conversation with Don MacDonald and Jim Marous as they discuss the Digital Banking Report and the rich information it provides on how to survive in the data age.
It’s all but a foregone conclusion that financial institutions need to make the transformation to digital. In fact, many have already made huge strides and have become digital-first. But, once you’ve made the transformation, there’s a huge difference between having and using data.
Many analysts across various industries have said that data is the new oil. And yet financial institutions are sitting on billions and billions of datapoints and often don’t know how to put them to use.
Join James Dotter, CFO at MX, to see how to get real revenue gains from the data you already have on hand as well as from data that’s easily within reach.
- How to stitch disparate data sources together
- How to get started with data structuring and architecture
- How to define the right metrics for the projects you’re working on
Erin Caldwell, Director of Client Education at MX and John Schulte, CIO at Mercantile Bank
“Banks are at a tipping point. Their historically stable customer base could erode steadily if banks cannot deliver the service proposition that customers demand.” — Accenture Research
How do you use data to exceed user demands?
Join Erin Caldwell, Director of Client Education at MX, and John Schulte, CIO at Mercantile Bank, to see what it takes to better leverage your data and exceed user demands.
- How Mercantile Bank uses AI and data analytics to improve the user experience.
- How to maximize every touchpoint with relevant, targeted marketing campaigns.
- How to use data to win long-term user loyalty and drive up ROI in the process.
When users see dirty transaction data, they don’t get upset with the vendor or the card provider. They get upset with you. They dial up your call center and drain your employee’s time, trying to figure out what the transaction refers to.
How do you take garbage data and turn it into something users can actually make sense of?
In this webinar, you’ll learn how to:
- Clean dirty data descriptions such as “CSTxx 04ROTER XXX726” so users just see the company name. (In this case, “Costco.”)
- Categorize all transactions with the greatest accuracy on the market — a full 30 percentage points more accurate than the closest competitor
- Classify transaction data by type (such as fee, bill pay, remote deposit, and more)
You’re sitting on a goldmine of data, but if the data isn’t clean, categorized, and classified, it’s no good to you or your users.