Over the past few years, consumers have become increasingly sensitive to the amount of personal data they generate in the course of daily life, and the value that companies attach to it. Consumers now accept that their behavior/purchase data will be collected, analyzed and used to enhance the quality of their service. In fact, they expect it.
The idea of banking providers analyzing consumers’ financial product and service patterns along with their demographic and lifestyle characteristics then using that to cross-sell appropriate products is not fundamentally different from any of the other services people use today. It’s central to the business model used by most major online businesses today.
Consumers are subconsciously assessing the value of their data every day based on what insights it provides — in other words, how their data is being used to benefit them and not just the company they are giving their data to. After all, the rationale offered by most marketers is that data collection “will improve the quality of service.”
One example where this rationale has actually manifested is music streaming. The service I subscribe to tracks every song I play. That data is analyzed along 40 different attributes for each song, which, along with other factors, is used to predict new music I might enjoy as part of a “daily mix” sent to me. This algorithm has changed the way I listen to music, and in ways I would not have thought possible just a few years ago, creating an incremental value to the service for me. It is the quality of this predictive functionality and its ability to create a more personalized service that increases its value and as a result retains me longer as a customer.
The trade-off in this example is straightforward: I get music I like suggested to me and the use of my data becomes a critical element of music company’s brand. How can banks and credit unions apply this premise to improve the quality of consumers’ experience?
Read More:
- The Massive Untapped Sales Opportunities of Mobile Banking Users
- Stop Cross-Selling! Help Customers Buy Instead
- 9 Steps to Improving Bank Cross-Sell Performance
Community Institutions Must Follow the Footsteps of Big Banks and Big Tech
Large banks with significant resources are beginning to use the same type of sophisticated data analytics my music streaming service relies on. In banking the data is derived from consumer transactions and used to construct predictive functionality that drives needs-based cross-selling programs. One example is “Erica,” Bank of America’s digital chatbot. In the same way as the music service continually evolves with my use, Erica will begin developing a more personalized service delivery for me.
This trend toward more personalized service, powered by collecting consumer data and developing predictive models is accelerating. Consumers are beginning to expect more personalized services from their banking institutions. These new data analytics capabilities are changing the baseline on how consumers evaluate service delivery and ultimately decide where to do their banking.
Adoption of data analytics, however, hasn’t been even across the industry. Smaller financial institutions have been very slow to adopt customer analytics. According to data reported by The Financial Brand, more than 50% of larger institutions are regular users of data analytics while for smaller institutions (less than $1 billion in assets) the figure is only 9%.
Needs-Based Cross-Selling is Not ‘Obnoxious’
Adapting data analytics to improve the customer experience will be a powerful marketing tool for community banks and credit unions. It would set them apart from big banks. But many smaller institutions have clearly not embraced a serious data strategy. There are at least two reasons for this.
First, in cases where management recognizes the value of harnessing the data they have, the cost of doing so can be prohibitive — especially when the number of retail relationships is relatively small. One credit union with 11,000 members was paying its technology provider more than $90,000 a year for analytics capabilities. The ROI just wasn’t there.
Second, in many cases managements of community financial institutions still resist the notion of proactively approaching consumers — in other words, “selling” (gasp!). They grew up in an age when banking companies were considered strictly service organizations. They remain leery of “annoying” people by offering someone a product or service if they haven’t specifically walked in and asked for them.
But needs-based selling is not obnoxious. On the contrary, consumers now judge the quality of their service by the degree of appropriateness of the new products/services offered to them. Proactive needs-based selling powered by customer data is becoming an expected component of good service by many consumers.
Another personal example demonstrates this. I do most of my banking with a community bank. I received an email offer for a home equity line of credit from a much larger out-of-state bank, however, with whom I have no relationship. I did not receive the offer by accident — I had been searching online for a home equity line. It was August and the tuition was due for my son’s upcoming year at college. I did not receive any such offer from my primary bank. So I called the out-of-state bank, and in less than a week I closed on a no-closing-costs line of credit.
Now that I have the line of credit, the remote bank communicates with me each month in my electronic statement and with standalone communications to cross-sell me additional products and services. I now have a high-yield savings account in addition to the HELOC.
This scenario is being carried out a million times over and it helps explain the decline of community focused banks and credit unions.
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Steps to Get Started
The current scenario for most community banking providers is that data collection and utilization is conducted at the individual level, between customer-facing associates and consumers. As branch associates get to know their customers, the quality of their needs-based selling increases. But those insights never become institutional knowledge that can be used across the organization. The value of the consumer data is never realized as an asset to drive growth and improve return on assets.
All is not all doom and gloom for community banks and credit unions, however. There are many forward thinking people in leadership positions in these institutions, who understand the requirements for successfully competing in the new “community.”
They would do well to consider that the process of integrating customer data into their marketing is much more than just collecting and analyzing data. If the objective is to develop an effective needs-based selling program, every activity must focus on improving the institution’s ability to offer the right product, to the right person, at the right time, on the right device, with the right price/offer.
The process for any organization to develop a data driven/needs-based marketing function involves the following seven steps:
- Data Collection — An audit of all data available.
- Data Definition — An understanding of the data collected.
- Data Format and Storage — A solution to storing this data.
- Data Analysis — Examination of data collected.
- Opportunity Assessment — Value determination of identified opportunities.
- Implementation — Execution of strategy and/or campaign.
- Results — Reaction of marketplace to marketing strategies/campaigns.
As you begin to pull your project plan together, the process will become much more personalized based on your objectives and the bank’s core competencies.
To effectively compete with new and traditional competitors, community banks and credit unions need to evolve their culture to one that tolerates calculated risk for growth as well as updating their understanding of what it means to serve their community. This change must start at the top, but the rewards can be substantial for financial institutions with vision.
Such a focus can arrest the decline of community financial institutions, because the combination of a strong local presence, positive brand, and the ability to drive needs-based selling is a combination the big banks should be worried about.