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Seven More Big Takeaways From the Second Annual Raddon Conference

December 13, 2018
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It’s not a Raddon Conference unless data is on the agenda, and the keynote sessions of this conference highlighted the opportunity effective data management can provide. Here are seven key takeaways for financial institutions about big data, artificial intelligence (AI) and machine learning.

1. Know that computers are here not to replace us but to assist us.

Paul Zikopolous, vice president of Big Data & Cognitive Systems from IBM, challenged attendees to consider how they use big data in their lives already and where else it could help. Every action, reaction and process can be instantly digitized, but does digitizing it yield knowledge? After all, big data without insights is just a bunch of data. Adding to this already unmanageable data deluge are new technologies that will further muddle existing processes. For example, blockchain will bring new entrants to the marketplace and new workers to the economy; it also will put some organizations out of business. And what’s more, instead of a system in which people talk to other people, in the emerging system, things like devices, vehicles and appliances can talk to each other through the internet of things (IoT). Banks and credit unions that focus on using data to improve their processes by 1 percent can reap the additional benefit over time.

2. Consider big data for its applicability and culture rather than its size.

A panel of speakers from three data-driven organizations drove home the notion that data is only as good as its application by institutions. These organizations have taken the lead in exploiting the vast amounts of information that interactions with customers generate on a daily basis. They have identified ways to use data to effectively stem attrition, generate new growth and enhance customer profitability. In each case, they used data to answer an important business need, not for its own sake.

3. Use machine learning to help you better communicate with customers.

Devon Kinkead, founder and CEO of Micronotes, described a banking industry ready to embrace machine learning and AI. Machine learning can see patterns in data that human eyes can’t perceive. By implementing machine learning in customer outreach, institutions can narrow their message from a mass market to an audience of one. Regulatory requirements lag innovation, and with time, they will inevitably target AI. Investing in compliance will help financial institutions stay up to date.

4. Let the social networking of payments help you find hidden influencers in your customer base.

Each person belongs to overlapping human networks, often centered on home, work, school, worship or shared interests. Facebook and other social media platforms have provided a utility to plot these networks, opening the door to previously untapped marketing opportunities. However, as consumer privacy concerns regarding the use of this personal information increases, much of it may remain out of reach for marketers. By using payments data and machine learning, institutions can see the trendsetters in their base and target appropriately.

5. Use predictive analytics to aid in onboarding and reboarding.

Financial institutions must be able to systematically communicate with customers at the appropriate time through a very personalized message. Data will provide greater success when you truly understand your customers’ needs through key life indicators. First, consider what opportunities in your customer base are available because of your share of wallet. Now that the national deposit share of wallet is at 32 percent, organic growth is available to grow deposits within your current customer base. But is that true for your institution? Share of wallet is a key metric to measure the depth of your relationship with your customer households.

Next, mine your data from two perspectives: customers who are new in the past six months and those who have been with you for more than six months. As you drive deeper into your data, create segmentation by age, income and other key demographic elements to guide you to specific campaigns.

For a deeper targeting opportunity, combine information about customers’ use of your products through transactions and balance changes with the demographics. Predictive clues expose customers’ needs through key life indicators, leading to product offers that are individualized and properly timed. Marketers are challenged with speaking to the multitude of customers, but when they use segmentation and predictive analytics, these messages will become very targeted and personalized, increasing the chances for success.

6. Use data to understand whom to keep and whom to lose.

Not all customers provide the same level of profitability. As institutions seek to manage their attrition in hopes of maximizing growth, they try to allow unprofitable relationships to leave while keeping profitable customers happy. A case study of a West Coast institution showed that the way to retain profitable at-risk customers was through building transactions and increasing lending volume.

7. Organize data with an MCIF.

Many business intelligence tools are on the market, and FIs are confused as to what tools to use to tackle the data war…and win. This session was able to examine the various tools that organizations use and show the large part that MCIFs play in providing the insights as well as the actions FIs need to complete their business intelligence tools. The key points to remember are that it takes more than just one business intelligence tool to accomplish everything that an FI needs; many tools need to work together to get the job done. Consider all the areas that need to be covered, including target marketing, data sources, strategic planning, data governance and data visualization. FIs should look for tools that are simple, quick and easy to use. They should save time for not only marketing, but the organization as a whole. If you try to use one tool alone, you could drain your organization’s financial and personal resources. Build a team of people and tools to do battle, and win the data war!