Understanding customers is the foundation to a sustainable competitive advantage in banking. Therefore, financial marketers can no longer wait to embrace the power of advanced analytics to gain insights and evaluate opportunities that will improve cross-selling,
up-selling and enhance share of wallet.
Financial marketers also need to extract more value from internal and external data sources, guiding product development, customer communication, innovation and growth.
In a recent report from Celent entitled, "Customer Analytics in Banking: Why Here, Why Now?", senior analyst, Bob Meara writes that now is the time for banks and credit unions to leverage the advances in processing, memory, database design and analytic methods
to improve performance and reduce costs. While the Celent analyst notes that some institutions are already on the path of using advanced analytics for decisioning and optimization, other organizations have only limited experience (this correlates with several
other studies).
The following are the primary reasons why banks need to step up their customer analytics game:
- The New Normal: The banking industry is expected to remain revenue challenged for the foreseeable future as a result of low interest rates, moderate fee revenue, onerous regulation and a less than robust economy. As a result, it will be more important than
ever for banks and credit unions to focus on all possible strategies to reduce costs and increase revenues.
- The Imperative for Customer Centricity: With customer delivery and communication channels expanding, and more customers interacting with their financial provider using online and mobile channels, always-on, real-time sales and service become imperative.
- Technology Advancement: Customer analytic applications are no longer the sole domain of highly skilled specialists. Today's solutions can be accessed and used by marketers and other business users to answer complex inquiries.
Customer Analytic Applications
As the Celent study makes clear, there is no shortage of analytic applications for banks and credit unions. While some are more general in nature, some are highly specific outsourced solutions, supporting a buy vs. build decision. Obviously, with a focus
on containing costs, the ability to utilize outsourced solutions is good news.
"Key retail banking priorities - specifically, using self-service channels to drive branch foot traffic, improving branch channel efficiency and effectiveness, and learning how to sell and service through digital channels - all require customer analytics,"
says Meara from Celent. "The good news is that there has never been such a variety of specialized customer analytics solutions."
According to the Celent report, there are six key well-established business drivers for predictive analytics in financial services. Each of these are important as a bank or credit union builds an analytic strategy for the future.
Of special interest to most financial marketers is the ability to gain a better insight on current customers. While demographics and current product ownership are at the foundation of customer insight, behavioral and attitudinal insights are gaining in importance
as channel selection and product use become more differentiated. Sentiment analysis and social media analysis are two additional examples.
Another predictive analytic model is the FICO score. Scoring models such as FICO analyze consumers’ credit history, loan or credit applications, and other data to assess whether the consumer will make their payments on time in the future.
The foundation of traditional banking business intelligence (BI), customer analytics are often used for product and channel development as well as economic forecasting, business improvements, risk analysis, and financial modeling.
- Customer Experience Management
According to the Celent study, the key to using customer analytics for customer experience management (CEM) is about delivering personalized, contextual interactions that will assist customers with their daily financial needs. In addition, if done correctly,
customer analytics in the context of CEM enables the real-time delivery of product or service offerings at the right time. It can also allow for highly sophisticated relationship pricing never before available.
One of the more common uses of 'big data' today is in the area of risk and fraud management. Data mining today has expanded well beyond internal purchase and balance insights to include transaction patterns and even social media interactions that can provide
a leading indicator to potential losses or fraud.
This type of integration of structured and unstructured data can also be leveraged for traditional risk management uses such as for pricing decisions.
BI tools have helped banks understand channel effectiveness for some time. More recently, analytics capabilities have boosted the usefulness of these tools. Capabilities include providing comprehensive views of channel performance based on both customer
behavior and transaction mix. Solutions help banks understand channel profitability and customer satisfaction and tailor retail operating models to improve retail delivery.
As more banks and credit unions work harder at migrating customers to digital channels, analysis of engagement and shifts in channel use become important indicators of satisfaction and re-pricing opportunities.
Another traditional use of customer analytics is the ability to increase the effectiveness and efficiency of sales and marketing in financial services. The ability to derive the likelihood of purchase based on available information about individual customers
has ushered in a seismic shift in marketing from product centricity to customer centricity.
Rather than offering products and services based on what the financial institution would like to sell (campaigns), banks and credit unions are now able to make unique, timely, and relevant offers based on available customer insight. Doing this form of analysis
across multiple channels allows financial marketers to significantly improve the efficiency of marketing spending and the close rate of sales leads.
For each of the applications shown above, the power is not just in the analytics themselves, but in the ability to do so in real time. With more challenges than ever in banking, analytics is at the center of it all.
Implementing a Successful Data Analytics Process
The Celent research emphasizes that while there are a growing array of use cases for data analytics, the process is definitely not a 'one and done' proposition. The move from a product/campaign based approach to a customer centric approach is huge and involves
many moving parts.
Successful implementations always involve a series of steps and a test and learn process with a different amount of time and effort applied to each step based on the specific project being undertaken.
According to Bob Meara from Celent, "Most organizations (banks included) get good at specific analytics use cases and broaden their use once parts of the organization gain confidence and prove the business case. Only then is the approach used more broadly
and extensively." He recommended that banks:
- Start small. Invest a little and wear out the application. See what it can do.
- Experiment – early and often. This requires a willingness to fail (in small and low-risk ways).
- Embrace analytics as a journey, not a destination. Keep learning and keep looking for ways to apply analytics for fun and profit.
In response to a question from me around whether banks should 'boil an ocean' in their analytics endeavors, Meara stated, "Of course, banks should walk before they run. By that, I mean banks should fully leverage in-house transactional data before investing
heavily in external sources of information and insight."
He adds, "Social data is particularly compelling, but runs a big risk of being unrepresentative. SAS, for example, does a great job integrating social media data with internal data to arrive at more well informed models and more highly predictive outcomes.
Either way, start with the treasure trove of data already onboard."
The reality is that, in the digital banking model of the future, data is a financial institution's most important asset. Banks and credit unions that are able to combine their internal and external data sources to create value will find themselves well placed
to thrive in what some have called 'Banking 3.0'.
Those who are unable or unwilling do so at their own peril.