Financial businesses collect a vast amount of data and are, understandably, incredibly protective of its integrity and security. Because of this, data usage is generally tightly restricted to the department for which it is collected, and sharing across the larger organization is historically limited and painstakingly slow.
Banks and other financial institutions are further held back by the complexity of legacy IT infrastructures, often comprised of a series of incompatible systems siloed by lines-of-business or product, making it extremely difficult and time-consuming to extract data and, more importantly, glean insights. But to remain competitive in a customer-centric landscape, marketers at banks and credit unions must develop strategies to harness their data in a smart, secure way.
According to from , 26% of banking and financial markets have not begun big data activities. Of those using big data, only 55% are focusing their activities on customer-centric outcomes. To build loyalty, confidence and excellent customer care, banks and financial institutions must recognize the changing habits and preferences of the banking population and adjust their product and service mix to meet consumers’ needs.
So how can marketers within the financial sector create these valuable customer experiences? The answer lies in self-service data analytics, and it requires a revolution in the way banks and financial institutions use their data to inform decision making.
Self-Service Data Analytics
Banks and credit unions of all sizes realize the tremendous potential for data, but many struggle with turning that data into something they can take action upon, quickly enough for it to make a difference. Legacy approaches and tools for analytics have simply slowed organizations down even more.
In it was found that 62% of organizations require others within their firm to perform some steps in the analytics process, resulting in 69% not being satisfied with the quality of the output and 81% not being satisfied with the speed of the output. With line-of-business departments such as sales, marketing, and finance exhausting point solutions, they have grown tired of having to depend on data scientists and specialized staff for data prep, blending, analytics, and sharing of insights.
Self-service analytical tools provide departmental-level analysts with the unique ability to easily prep, blend and analyze all of their data using a repeatable workflow, then deploying and sharing analytics at scale for deeper insights in hours, not weeks. Empowering departments like marketing with data analytics, without writing any code, will help them gain a complete picture of all customer interactions and enable them to target and retain customers with total accuracy.
Precision when identifying customers and segments is especially critical due to their line of service. If a supermarket s a brand that is too expensive to a consumer, no harm is done. But if an inappropriate credit offer is proposed to a banking customer, it has more serious implications.
A self-service approach can help financial institutions break down siloes and connect data within. In the IBM report, only 53% of banking and financial markets set up their big data infrastructure to include information integration. As the use of analytics spread across organizations, particularly outside of IT, balancing governance and access becomes key for marketers to quickly acquire the trusted, accurate information they need for timely, personalized services and promotions.
Sophisticated, user-friendly, self-service data analytics capabilities can also deliver untold benefits for risk management and compliance, enabling banks and financial institutions to assess and optimize risk exposure across business units. By empowering business people to analyze their own data, more people within the organization will be able to uncover hidden insights without relying on IT specialists or programmers.
Big Data at Work
For example, using data effectively can help pinpoint market trends, predict demand, and assess risk that can translate into an improved offering and service. One large financial institution is leading the way by blending five billion rows of customer and transaction data across multiple product lines to identify and retain at-risk customers, improve center agent behavior and optimize its distribution network. This results in reduced costs, improved convenience and a better customer experience.
Another smaller, regional financial institution, , used in combination with their customer data to ensure the right coaching and training of sales reps takes place before and during a sales cycle. With the help of data preparation and analytics software, First Bank of Tennessee has reduced the data preparation time from three-to-four hours down to a few seconds … meaning faster reporting, a more agile sales organization and an improved consumer experience.
Likewise, a major financial institution that focuses on customer lifetime analytics is using ‘joined up’ predictive models on customer response, conversion and lapse to understand the most powerful predictors that drive customer activities across the pre- and post-sales cycle. The institution creates customer segments not just based on tendency to buy, but tendency to ‘pay and stay,’ which has helped achieve a 15-25% uplift in customer responses and resulted in customers being 3x more accepting of up-sell or cross-sell offers.
Banks and financial institutions already have the biggest piece to the puzzle for achieving this customer-centric approach: vast volumes of data. Adopting a self-service analytics strategy will help them unlock the potential of the data and realize that it is not there just to protect, but to use.
When organizations open their business to legitimate and safe data use, and expand their marketing consciousness, they can flex their marketing muscle and use a data-led approach to customer targeting and management. The results can offer bigger, better and more efficient campaigns, leading to higher sales, satisfied customers and empowered employees.