Almost three out of four community banks and credit unions admit they do not have a formal data analytics strategy, but for those that can get over the organizational hurdles of implementing a data strategy, the competitive advantages are significant.
Smaller banks and credit unions know they must leverage the large amount of customer information they have on-hand, including product purchases, transaction histories, service delivery preferences, demographics and geographic information. Additional demographic and financial information from third parties can also be augmented on these data sets — income, accumulated wealth, family composition and characteristics, and much more.
So why aren’t more community-based institutions jumping on the marketing analytics bandwagon? Part of the answer is that it is not that easy to make the changeover. Many marketing teams are being pushed into using big data analytics by senior management when the organization simply isn’t ready for it.
Data analytics promises the ability to create marketing that offers the right product to the right person at the right time on the right device. This transformation will have a profound impact on a small institution’s organization and culture. Given that community banks and credit unions have typically not been as progressive in adapting to market and technology advances, there are definitely a few areas where implementing a big data analytics program might cause some “growing pains.”
The first critical step is getting all the internal stakeholders at the table, then getting them to agree on a strategy. This will require everyone within the organization to recognize the business need for analytics in their own respective wheelhouse. For example, human resources has just as much to gain as marketing in terms of analytics insights.
Many community banks and credit unions use gut instinct to define their “best customers.” It’s not uncommon for a CEO to unabashedly declare their bank is “attractive to older, less affluent customers,” believing their institution draws a disproportionate number of these from the communities it serves. But assessing the customer file and analyzing the data, it turned out this long held belief was actually not true. In fact, the bank drew heavily from younger, more affluent consumers. The CEO was shocked — utter disbelief. Data — and the corresponding insight that comes from it — can rock the status quo, particularly in smaller, community-based institutions.
With even a simple marketing analytics function, a marketing department can evolve from a “promotions department” into a true sales support role. With data analytics, the marketing department can match customer behaviors with demographic markers and build a segmentation framework. This means the entire organization looks to the marketing department to answer questions like:
- What is the institution’s most profitable customer segment?
- What segment(s) should we be cross-selling? With what product(s)?
- What are the best new customer segment(s) to go after? What product(s) should we sell them?
Armed with this information, marketing strategies become more fact-based, where marketing is driving certain aspects of the sales function instead of vice versa. The marketing department identifies new prospects, cross-sell segments, as well as the product(s), pricing and messaging. Marketing can also accurately forecast goals for sales campaigns, so branch personnel are accountable for results.
With the need to more effectively compete with larger banks, neo-banks, fintechs and other outside disruptors, how can a community institution possibly hope to develop their marketing analytics capabilities? There is a basic template — a Data Anlaytics Roadmap — with the following steps:
The process starts out with identifying all sources of data throughout the bank. Once these sources have been identified and categorized, a relative value or ranking is assigned to each one. The value is dependent on how much each data source aligns with the overall goals of the bank. The next step is to determine a suitable warehouse for the data.
As the data is analyzed and reviewed by stakeholders. Possible campaigns are discussed, and a list of projects is created. This project list is then reviewed and prioritized according to revenue and non-revenue factors. The highest priority campaigns and projects will be researched, then have expenses and potential revenues attached to them. The next step will be to incorporated these activities into the annual marketing plan and ultimately into departmental budgets.
One of the most critical aspects to the success of any data analytics initiative is to incorporate all campaign results back into your data warehouse. Correlating successful campaign elements with the data will help marketers identify what is working. This reintegration of results also drives the process of continuous marketing improvement. At this point, the process has completed one cycle and is ready to begin again. A best practice is to conduct quarterly meetings to review and share campaign results throughout the bank in order to include all stakeholders in formulating strategy adjustments and to increase organizational knowledge.
It is possible for community banks and credit unions to take the plunge into more sophisticated data-driven marketing. To be successful it just takes a strong commitment by senior management, a willingness to evolve the organization and a sense of humor — because completing a project of this magnitude is a lot of fun.
Frank Koechlein is the Managing Director at Velocity Marketing Analytics and coauthor of the marketing resource book “The New Marketing Analytics”. Frank has 40 years of marketing experience in the financial services industry. He has held several senior marketing positions including SVP, Marketing with the Dreyfus Corp and Director of Marketing with Prudential Direct, Prudential’s in-house, direct response agency.