Whitepapers > 5 Essential Steps to a Robust Data Strategy for Financial Institutions
Gain deeper insights into your customers’ financial lives, create relevant marketing campaigns that convert at every touchpoint, and engage with customers on a much deeper level — turning transactional interactions into meaningful conversations.
Developing an effective data strategy is more than purchasing the latest software or signing up with new vendors. There’s a lot of deliberate thought and action that should go into every step of a data strategy to ensure it’s successful — and it all starts internally, by having the right structure in place to support all the things that data can make possible. It’s no secret that in today’s day and age, data is the driving factor behind successfully reaching customers. Companies like Amazon, Apple, and Netflix, to name a few, know this well. They’ve built entire empires on using data effectively to reach and delight customers.
No matter which way you splice it, really knowing your customers is imperative for today’s marketers, and it all starts by implementing and executing on a thoughtful and comprehensive data strategy. Unfortunately, most marketers at financial institutions are dealing with legacy systems, siloed data, and poorly planned processes that make it hard to get a full picture of who they’re marketing to, or the most effective way to do so.
With the right data strategy, marketers can get right to the source of the information, helping them create truly engaging and hyper-personalized experiences with every customer interaction. What’s more, with the right data strategy, marketers can create a full picture of their customers’ financial lives at every point of their journey, so they can offer products and advice that helps them make the right decision at the right time, every time. Making data easily accessible and actionable helps you always stay relevant in customers’ hearts and minds, positioning your institution as the primary FI. The following five steps will help you gain deeper insights into your customers’ financial lives, create relevant marketing campaigns that convert at every touchpoint, and engage with customers on a much deeper level — turning transactional interactions into meaningful conversations.
In today’s day and age, data is the driving factor behind successfully reaching customers. Companies like Amazon, Apple, and Netflix have built entire empires on using data effectively to reach and delight customers.
Before you can really begin to understand and use the full breadth and depth of your data, you need to outline how you’ll use the data and what success looks like. Set a few clear expectations that enable you to see if you’re really using your data to create value-add experiences for your customers. Measure what’s important to your financial institution: loan deposit increase, higher user engagement across channels and devices, greater product mix among customers, and so on. Once you know what you’re expecting to learn, you can start to work towards uncovering all the rich insights inside your data.
Otherwise, it’s easy to get lost in insights and data without a clear objective for what you intended to find. There’s a myriad of different types of insights you’ll come across, that’s why it’s critical to have a clear objective to keep you on point with what’s most likely to bring the highest success and deliver clear ROI. Overtime, as you develop your data strategy, you can add more specific levels of insights to create a richer and more multifaceted data strategy that will continue to add to your foundational goals.
Once you understand the insights you want to gain from [your] data and have tied those insights into clear defined goals for success, it’s time to dive into the types of data within your financial institution. The financial industry is sitting on a goldmine of data — more data than any other industry: from loan types and transactions to purchasing patterns and saving behavior. So when it comes down to it, it’s all about understanding the different types of data within your institution and knowing how to use it. One way to more easily understand your data is by “simplify[ing] your data architecture, minimize data fragmentation, and decommission redundant systems.” When you do this, you can “reduce...IT costs and investments by 20% to 30%.”
Another way to better understand and use your data is by hiring a data scientist, either as part of your internal team or as a consultant. At MX, we’ve seen that having the right people onboard is arguably the most important part for creating a successful, data-driven culture. Data scientists can ensure that your data is easily accessible and actionable at a moment’s notice. In fact, one of the “greatest [value-adds] unlocked when a [financial institution] uses its data... to transform its entire business model [is to] become a data-driven digital bank.” Once you make data-guided decisions, you’ll be able to see clearer, more trackable results.
Perhaps one of the most challenging issues that financial institutions face is siloed data. Although the depth and breadth of the data itself is powerful, it’s usually stored across various types of software systems and devices. This fragmentation makes it hard to access data and nearly impossible to act on. The longer it takes to decipher and understand what your customers need and what kind of products are relevant to them, the more opportunities you miss to reach them and add real value at the right time. With a clear understanding of what your customers need and when, you can help them make better financial decisions, ensuring that you become the trusted source in their minds when it comes to financial matters.
After you’ve brought all your data together from disparate locations, the next step is to make sure it’s actionable — so you must ensure it’s cleansed, classified, and categorized. When you think about it, the success of your digital and marketing initiatives are all reliant on the accuracy of your data. With dirty transactions and unclean data, it’s hard for both financial institutions and customers to make the best financial decisions possible. When it comes down to it, “if a user asks a chatbot how much they’ve spent on [medical bills] this month, and only two of the five ...transactions have been categorized as ]medical] transactions, what good is the chatbot? It will return the incorrect answer, even though it’s perfectly following its own logic.”
Once your data is fully actionable, you can use it to create products and messages that get to market faster — so you never miss an opportunity when customers are actively considering offers.
A critical factor for high data quality and a robust data strategy is having a clearly defined governance process. According to a study by Mckinsey, only 20% to 30% of data quality issues are attributed to systems faults. “The rest stemmed from human error, such as creating multiple different versions of the same data.” Although this is an issue that a lot of organizations face, it’s especially critical in the financial industry. The sensitive nature of customers’ financial information makes a solid governance process paramount for financial institutions to reduce liability.
A strong governance process can also help financial institutions reach their customers more effectively. Knowing that your data is always up to date and having a reliable place where it’s stored, allows you to access information faster and be more specific when it comes to promoting relevant products and offers. Furthermore, it can “create enormous efficiencies along the whole data life-cycle from sourcing and extraction to aggregation, reconciliation, and controls, yielding cost savings that can run as high as 30 to 40%.”
Once you’ve put a comprehensive and robust data strategy in place, it’s important to create a feedback loop to track its success and course correct where needed. The more consistent and diligent you are with each step, the higher the likelihood of continued success throughout your organization. Overtime, you’ll be able to look back and recognize trends and patterns in your data strategy process than can be improved and optimized. Ultimately, this ensures that your customers’ data is always secure, and you’re using it to its fullest ability when it comes to helping your customers become or remain financially strong.