Many financial marketers are successfully leveraging big data to gain insights into how they can improve customer interactions, deliver more relevant product offers and create a more meaningful, personalized experience. But that’s the exception — big data is still befuddling many retail banking providers. Some institutions lack the technology tools, others haven’t embraced a data-driven culture, and for many, the stumbling blocks are more basic: marketers don’t even know what data they should be analyzing.
Even the most astute and progressive financial marketing professionals need to be asking the right questions of the right data sets before they can begin to extract any value from it. They need to understand that it’s not just about data inside their institution’s walls that should be analyzed. In fact, what can be learned from unstructured data that is publicly available can deliver real insights that help lead consumers down the purchase funnel.
Here are three common marketing challenges facing retail banks and credit unions, and how analytics can help address them.
1. Millennials Don’t Want Traditional Banking
The Millennial generation is a paradox to many institutions. Some market studies report that Millennials are very careful with how and where they spend their money, while other surveys reveal that they’re fickle and nomadic. The truth is that Millennials are not much different than other demographics when it comes to their needs and expectation of financial services. They want personalized service, and want to feel like a valued customer — with every interaction and at their channel of choice.
If marketers aren’t using analytics to understand what resonates with Millennials (and other customers) and further cultivate them, they are missing a golden opportunity. A survey from Segmint found one in two Americans are open to receiving marketing communications and personalized advertising from their financial institutions, and that proportion increases among Millennials. The survey also found nearly two-thirds of Millennials are open to marketing communications that may help them reach their personal finance goals. Financial marketers aren’t going to get the full picture of this from data within their own network, especially if they’re trying to reach new customers. This means using analytics to gain information from outside, unstructured sources such as social media, where Millennials are more than likely commenting about finance goals such as trying to pay off student loans, saving money, or moving out on their own.
Read More:
- Five Big Data Trends Impacting Financial Institutions
- 5 Critical Big Data Capabilities Financial Marketers Must Master
- Data Analytics Critical to Success in Banking
- The Billion Dollar Big Data Question
2. Customer Loyalty is Dead
Consumer preferences are the predominant force in digital transformation. In a report by renowned digital analyst and futurist Brian Solis, 71% of executives said their number one challenge is understanding the behavior and impact of new customers. Staying on top of consumer behavior is possible with greater use of analytics technologies combined with critical decision-making. Marketers at financial services firms can achieve better results through data analysis, but a good first step is figuring out which business problems are a priority, and where the data exists to analyze and strategize around those problems from a marketing perspective.
Financial services providers are facing a steep uphill battle: 1) it’s easier than ever for consumers to switch brands; and 2) their expectations of customer service and the banking experience are high. For example, a FICO survey found that Millennials are two times more likely this year than last year to close all accounts and change banks. The same survey found 16% of those ages 25 to 34 years are considering opening a banking account with an online-only bank in the next year. Another industry survey on consumer banking preferences found that 42% of consumers don’t believe that any service provider understands them, creating an opportunity for banks to distinguish themselves moving forward.
Many banks and financial firms closely tie their customer relationships with loyalty programs, and while this strategy is successful in retaining valued current customers, it’s inwardly focused. Banks and credit unions need analytics that can help them evaluate unstructured, external data that can provide insight into other key metrics like brand awareness, brand consideration and brand perception. This is data than can include both current customers and others — like customers of competitors — that can help guide loyalty programs and marketing campaigns.
3. Struggling to Define the Customer Experience
Institutions are collecting huge volumes of data to stay agile, refine and adapt their marketing and sales opportunities, and ultimately provide the best customer experience possible. Data from each customer touchpoint, social mentions, and publicly available digital channels is all part of the picture of what needs to be analyzed to gain a holistic view of the customer experience.
But what is customer experience? It’s a buzzword that gets associated with a lot of elements pertaining to how a company interacts or approaches customers. The report from Brian Solis also found that “customer experience” is the top driver of digital transformation, supported by ongoing investments in IT and marketing technology.
Rather than getting tripped up by the different meanings and interpretations of customer experience, a simpler way to think about it is for marketing executives to ask themselves how their current customers feel about the organization. Attracting new customers is an obvious marketing goal, but keeping current customers satisfied is equally important. Marketing should apply analytics around metrics such as “buying experience” and “current customer engagement” to help them understand their customers. Analyzing all available data is the fastest way to get closer to customers, and the closer an organization is, the better experience it can deliver. This can help lead them through the decision funnel to purchase.
Venkat Viswanathan is founder and chairman of LatentView Analytics, one of the largest and fastest growing data analytics firms globally.