Community banks have always relied on data to make decisions. And making use of that data has always been challenging. With rapidly evolving technologies, ironically, the challenge of making use of this data has grown even greater. And, unfortunately, data doesn't fit neatly into a spreadsheet.
It wasn’t that long ago that a bank's customer data came, for the most part, from account records, loan files, and transaction histories. That is no longer the case.
Today, community banks interact with customers through dozens of channels, from mobile apps, online presence, chatbots, and email, to SMS alerts, digital account onboarding, credit applications, call centers, and social media. And every one of those interactions generates information. The result is an explosion of data, much of it unstructured. This unstructured information, known as big data, contains valuable insights into consumer behaviors. But the data is often in overwhelming volumes and siloed across the enterprise, making it difficult to organize, search, and analyze.
Community banks have become data-rich organizations. The challenge is turning all that information into actionable intelligence.
What makes “big data” big? Look for the four V’s
- Volume: size of the data set.
- Variety: types of data. This could include anything from business transactions, emails, photos, and activity logs to social media postings.
- Velocity: speed at which data are accumulated. In 1998, Google received 10,000 searches per day. That number has skyrocketed to over 3.5 billion.
- Variability: change in velocity or structure. This shows the need for data analysis to be dynamically scalable to efficiently handle additional processing loads.1
More touchpoints. More data
As consumers increasingly embrace digital banking and customer engagement levels reach new heights, banks are presented with both an opportunity and a challenge. On the one hand, more data can provide a clearer picture of customer behavior, preferences, and financial needs. On the other hand, managing and making sense of that information requires tools, processes, and expertise that many smaller institutions are still developing.
The issue isn't simply the amount of data being generated. It's now the variety. Traditional structured data, in the form of account balances, transaction histories, and payment activity, remains important. But increasingly, valuable customer insights are hidden inside documents, conversations, and communications that don't fit neatly into a database.
What next?
Community banks will never outspend the largest national banks on technology, data science teams, or AI platforms. The good news is that they don't have to.
1. Start with the data you already own: Most industry experts argue that community banks should focus on combining their greatest competitive advantage, i.e., customer relationships, with targeted, practical uses of data rather than attempting to build massive analytics infrastructures. Most community banks already possess enormous amounts of first-party customer data through account openings, loan applications, website activity, transaction histories, online banking sessions, and customer interactions. The challenge is organizing and using it effectively. ICBA, in Market your community bank with first-party data, notes that community banks own an "enviable amount of data" but many are not yet leveraging it to its full potential. Instead of pursuing expensive enterprise-wide initiatives, ICBA suggests, community banks should analyze existing customer information to identify opportunities for cross-selling with more personalized offers, customer retention, and targeted marketing campaigns.
2. Focus on customer intelligence, not data science: Large banks often employ teams of analysts and data scientists. Community banks typically cannot. However, community banks already possess something many larger institutions struggle to create: deep customer relationships. The most effective strategy, then, is to combine relationship banking with insights gleaned from those relationships, rather than try to become a technology company.
3. Use third-parties: Community banks don’t need to build everything themselves. Cloud-based CRM systems, customer analytics platforms, and marketing automation tools such as Bank Marketing Center’s web-based content development and management platform, allow smaller institutions to access sophisticated capabilities without staffing a large IT department.
4. Prioritize a few high-impact use cases: Many community banks make the mistake of viewing data initiatives as massive transformation projects. Industry experts increasingly recommend focusing on a handful of practical applications that produce measurable results. Examples include:
- Identifying customers likely to need a mortgage or home equity loan.
- Detecting deposit attrition risks.
- Improving small business relationship management.
- Enhancing fraud monitoring.
- Creating more targeted marketing campaigns.
- Personalizing digital banking experiences.
ICBA's 2025 Data Advantage" report specifically argues that community banks can turn transaction data into meaningful growth without large technical investments by focusing on strategic, targeted uses of customer information.
5. Invest in data quality before advanced analytics: Financial institutions, both large and small, have customer information scattered across core systems, lending platforms, CRM systems, spreadsheets, and digital channels. Before implementing sophisticated analytics, community banks should ensure that data is accurate, accessible, and consistent. A bank gains little value from artificial intelligence if the underlying data is incomplete, unreliable, or “artificial”.
6. Use AI to augment employees, not replace them: For community banks with limited resources, AI represents an opportunity to access capabilities that were once available only to large institutions with substantial technology budgets. There are affordable AI tools out there; tools that can automate routine analysis, summarize customer information, identify trends, and surface actionable insights that previously required dedicated analytics teams.
7. Turn data into a relationship-banking advantage: The real opportunity isn't becoming the next national banking giant. It's becoming a smarter version of a community bank. As ICBA notes, community banks can leverage transaction and customer data to strengthen relationships, increase revenue, and differentiate themselves in increasingly competitive markets.
In the end…
The banks most likely to succeed won't necessarily be the ones with the largest datasets or the most sophisticated algorithms. They'll be the institutions that combine local knowledge, personal relationships, and targeted data insights to deliver a more personalized customer experience than larger competitors can provide.
Bank Marketing Center
We’re Bank Marketing Center, the leading, subscription-based provider of automated marketing services to community banks. Our goal is to help bank marketers with topical, compelling communication with customers that builds trust, relationships, and revenue. And we do this through automating critical bank marketing functions, such as content creation, social media management, digital asset management and, of course, content routing. All of which contribute to a community bank’s ability to create and distribute content that drives business, without fear of fines, brand damage, or fleeing customers.
We also want to share what we know—and learn along the way—with all our community banking friends. Whether it’s content focused on the latest on AI technology, suggestions on how to attract and retain top talent, or the importance of data protection, we’re here to make bank marketing the best that it can be..
Want to learn more about what we can do for your community bank and your marketing efforts? You can start by visiting bankmarketingcenter.com. Then, feel free to contact me directly by phone at 678-528-6688 or via email at nreynolds@bankmarketingcenter.com. As always, I welcome your thoughts.
1CommunityBankingConnections.org. Big Data in Small Banks — Maintaining Effective Data Management in Community Banks. January 2, 2023.