Getting your Digital Growth Strategy Right from the Start


Digital growth is only as good as the metrics used to measure it.

Growth is one of an executives’ most important responsibilities, whether that comes from the branch, through mergers and acquisitions or digital channels. Digital growth can be a scalable and predictable way for a bank to grow, if executives can effectively and accurately measure and execute their efforts. By using Net Present Value as the lens to evaluate digital marketing, a bank’s leadership team can make informed decisions on the future of the organization.

Banks need a well-thought-out digital growth strategy because of the changing role of the branch and big bank competition. The branch used to spearhead an institution’s growth efforts, but that is changing as branch sales decline. At the same time, the three biggest banks in the country rang up 50% of the new deposit account openings last year (even though they have only 24% of branches) as they lure depositors away from community banks, given regulators’ prohibition on acquisition.

Physical Branch Decline chart.pngImage courtesy of Ron Shevlin of Cornerstone Advisors

Even in the face of these changes, many institutions are nervous about adopting an aggressive digital growth plan or falter in their execution.

A typical bank’s digital marketing efforts frequently rely on analytics that have been designed for another business altogether. They may want to place a series of ads on digital channels or social media sites, but how will they know if those work? They may use data points such as clicks or views to gauge the effectiveness of a campaign, even if those metrics don’t speak to the conversion process. They will also track metrics such as the number of new accounts opened after the start of a campaign or relate the number of clicks placed in new accounts.

But this approach assumes a direct link between the campaign and the new customers. In addition, acquisition and data teams will spend valuable time creating reports from disparate data sources to get the proper measurement, instead of analyzing generated reports to come up with better strategies.

Additionally, a bank’s CFO can’t really measure the effectiveness of an acquisition campaign if they aren’t able to see how the relationships with these new customers flourish and provides value to the institution. The conversion is not over with a click — it’s continuous.

This leads to another obstacle to measuring digital growth efforts: communication. Banks use three internal teams to generate growth: finance to fund the efforts; marketing to execute and measure it; and operations to provide the workflow to fulfill it.

Each team measures and expresses success differently, and each has its inherent shortcomings. Finance would like to know the cost and profitability of the new deposits generated, to assess the efficiency of the spend. Marketing might consider clicks or views. Operations will report on the number of accounts opened, but do not know definitively if existing workflows support the market segmentation that the bank seeks.

There is not a single group of metrics shared by the teams. However, the CEO will be most interested in cost of acquisition, the long-term profitability of the accounts and the return on investment of the total efforts.

But it’s now possible for banks to see the full measurement of their digital campaigns, from the disbursement of funds provided by the finance group to the success of these campaigns, in terms of deposits raised and net present value generated. These ads entice prospects into the account origination funnel, managed by operations, who open accounts and deposit initial funds. Those new customers then go through an onboarding process to switch their direct deposits and bill pay accounts. The new customer’s engagement can be measured six to 12 months later for value, and tied back to the original investment that brought them in the first place.

Bank leadership needs to be able to make decisions for the long-term health of their organizations. CEOs tell us they have a “data problem” when it comes to empowering their decisions. For this to work, the core system, the account origination funnel and the marketing channels all need to be tied together. This is true Integrated Value Measurement.

Sink or Swim in the Data Deep End


data-7-1-19.pngCommunity banks risk allowing big banks an opportunity to widen the competitive gap by not investing in their own data management.

It’s now-or-never for community banks, and a competitive edge could be the key to their survival. A financial institution’s lifeblood is its data and banks can access a veritable treasure trove of information. But data analytics poses a significant challenge to the future success of community banks. Banks should focus on the value, not volume, of their information when adopting an actionable, data-driven approach to decision-making. While many community banks acknowledge how critical data analytics are to their future success, most remain uncommitted.

This comes as the multi-national institutions expand their data science teams exponentially, create chatbots for their websites, use artificial intelligence to customize user interactions and apply machine learning to complete back-office tasks more efficiently. The advantage that a regional bank manager has when working next door to a community bank is growing too large. And the argument that the human touch and customer experience of a community bank will make up for the technological gap has become less convincing as younger customers forgo the branch in favor of their phone.

Small and medium institutions are dealing with a number of obstacles, including compressed margins and a shortage of talent, in an attempt to move past basic data analytics and canned ad hoc reports. If an institution can find a qualified candidate to lead their data management project, the candidate usually lacks banking experience and tends to have a science and mathematics backgrounds. A real concern for bankers is the hiring managers’ ability to ask the right questions and fully discern candidates’ qualifications. And once hired, is there a qualified leader to drive projects and their results?

Despite these obstacles, banks have only one option: Jump into the data deep end, head first. To compete in this data-driven world, community banks must deploy advanced data analytics capabilities to maximize the value of information. More insight can mean better decisions, better service to customers and a better bottom line for banks. The only question is how community banks can make up their lost ground.

The first step in building your organization’s data analytic proficiency is planning. It is crucial to understand your current processes and outputs, as well as your current staff’s capabilities, in order to improve your analysis. Once you know your bank’s capabilities, you can determine your goal posts.

A decision you will need to make during this planning stage will be the efficacy of building out staff to meet the project goals, or outsourcing the efforts to a consultant group or third-party software. A community bank’s ability to attract, manage and retain data specialist could be an obstacle. Data specialists tasked with managing more-complex diagnostic and predictive analytics should be part of the executive team, to give them a complete understanding of the institution’s strategic position and the current operating environment.

Another option community banks have is to buy third-party software to supplement current resources and capabilities. Software can allow a bank to limit the staffing resources required to meet their data analytical goals. But bankers need to understand the challenges.

A third-party provider needs to understand your organization and its strategic goals to tailor a solution that fits your circumstances and environment. Management should also weigh potential trade-offs between complexity and accessibility. More-complex software may require additional resources and staff to deploy and fully use it. And an institution shouldn’t solely rely on any third-party software in lieu of internal champions and subject-matter experts needed to fully use the solutions.

Whatever the approach, community bank executives can no longer remain on the sidelines. As the volume, velocity and variety of data grows daily, the tools needed to manage and master the data require more time and investment. Proper planning can help executives move their organizations forward, so they can better utilize the vast amount of data available to them.

Mining for Gold in Bank Data


data-5-9-19.pngCommunity banks are drowning in customer data.

Every debit card swipe, every ACH and every online bill pay produces data that provides insight into their customers’ relationship with the institution, as well as their lifestyle and potential needs. Banks should prioritize using their proliferation of customer data to open up new service and revenue opportunities. The potential to identify untapped opportunities is enormous.

The amount of data generated by the digitization of services and customer interactions has grown exponentially in recent years. By 2020, about 1.7 megabytes of new information will be created every second for every person on the planet, according to a 2017 McKinsey & Co. report. This figure is only expected to increase: By 2021, half of adults worldwide will use a smartphone, tablet, PC or smartwatch to access financial services. The mindboggling amount of data comes at a time when companies must “fundamentally rethink how the analysis of data can create value for themselves and their customers,” according to a Harvard Business Review article by Thomas Davenport, a professor at Babson College and a fellow at the MIT Initiative on the Digital Economy.

Amazon is often cited as the model for capitalizing on data to increase sales and improve consumers’ experience. The company tracks each customer interaction—from site searches and purchases, from Alexa commands to song or movie downloads—to develop a holistic view of that consumer’s preferences and buying habits. For instance, if a consumer purchases prenatal vitamins from Amazon, she will soon see pop-up ads for other pregnancy and baby-related items. Amazon will also send her offers and reminders to repurchase the vitamins at the exact time they run out.

Banks should try to emulate Amazon’s ability to highly personalize a consumer’s experience. Organizations that leverage customer behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin, according to Gallup. And personalization based on customer data can deliver 5 to 8 times the return on investment on marketing expenses and increase sales by 10 percent or more, according to McKinsey.

But in order for banks to use the data produced by their internal systems, they need to create a structure and plan around it. Institutions need to direct information to one location, figure out how to analyze it and—most importantly—develop an actionable plan. This is a challenge because many banks partner with a myriad of vendors to provide the different consumer services such as debit and credit card processing, online banking and bill pay vendors. To consumers, these disparate systems may appear to work together reasonably well; behind the scenes, they may not communicate with each other.

This is an overwhelming imperative for many community banks. Fifty-seven percent of financial institutions say their biggest impediment to capitalizing on their data is that it is siloed and not pooled for the benefit of the entire organization, according to a July 2016 report from The Financial Brand. Other impediments include the time it takes to analyze large data sets and a lack of skilled data analysts.

But banks can remove these impediments with an “intelligent” data management technology platform that aggregates information from unlimited sources and makes it available enterprise-wide, from frontline staff to marketing to management. Platforms analyze data from sources like the core processor, online banking and lending systems, as well as peer and demographic data, and develop automated revenue- and service-enhancing strategies that capitalize on the findings.

The results are better, automated and even instantaneous decisions that generate greater sales opportunities and improve customer experience.

Banks can use the data to generate personalized, targeted marketing and communications campaigns that are triggered by an increase or decrease in customer transactional activity. Reduced activity can indicate an account might leave the institution; proactive communication can reengage the customer and retain the account.

This data can improve cross-selling objectives, generate sales opportunities and track onboarding activities to facilitate the customer’s experience. The data could identify customers who use payday or other non-bank lenders, and generate omni-channel offers for in-house products. It could also flag follow-up communications on products or services that consumers expressed interest in, but did not open.

Centralizing institutional data into one platform also creates efficiencies by automating manual processes like new account onboarding, loan origination and underwriting—even customer complaint resolution. It can also introduce additional customer services provided by third-party vendors by requiring them to integrate with only one data source, instead of many.

Banks need to leverage their customer data in order to create highly personalized and meaningful offers that improve engagement and overall performance. With the assistance of a comprehensive data management platform, community banks can overcome the hurdles of unlocking the value of their data and achieve Amazon-like success.

The Key To Creating A Profitable Deposit Strategy


deposit-5-6-19.pngSmall and mid-size banks can leverage technology to retain and grow their retail relationships in the face of fierce competition for deposits.

Big banks like JPMorgan Chase & Co., Bank of America Corp. and Wells Fargo & Co. continue to lead the battle for deposits. They grew their domestic deposits by more than 180 percent, or $2.4 trillion, over the past 10 years, according to an analysis of regulatory data by The Wall Street Journal. To survive and thrive, smaller institutions will need to craft sustainable, profitable strategies to grow deposits. They should invest in technology to become more efficient, develop effective marketing strategies and leverage data and analytics to personalize products and customer experiences.

Banks can use technology to achieve efficiencies such as differentiating net new money from transfers of existing funds. This is key to growing deposits. Traditionally, banks and their legacy core systems were unable to distinguish between new deposits and existing ones. This meant that banks paid out promotional interest and rewards to customers who simply shifted money between accounts rather than made new deposits. Identifying net new money allows banks to offer promotions on qualified funds, govern it more effectively, incentivize new termed deposits and operate more efficiently.

To remain competitive, small and mid-sized banks should leverage technology to create experiences that strengthen customer retention and loyalty. One way they can do this is through micro-segmentation, which uses data to identify the interests of specific consumers to influence their behavior. Banks can use it to develop marketing campaigns that maximize the effectiveness of customer touchpoints.

Banks can then use personalization to execute on these micro-segmentation strategies. Personalized client offerings require data, a resource readily available to banks. Institutions can use data to develop a deeper understanding of consumer behaviors and personalize product offers that drive customer engagement and loyalty.

Consumers deeply valued personalization, making it critical for banks trying to attract new customers and retain existing ones. A report by The Boston Consulting Group found that 54 percent of new bank customers said a personalized experience was “either the most important or a very important factor” in their decision to move to that bank. Sixty-eight percent of survey respondents added products or services because of a personalized approach. And “among customers who had left a bank, 41 percent said that insufficient personalized treatment was a factor in their decision,” the report read.

Banks can use data and analytics to better understand consumer behavior and act on it. They can also use personalization to shift from push marketing that promotes specific products to customers to pull marketing, which draws customers to product offerings. Institutions can leverage relationship data to build attractive product bundles and targeted incentives that appeal to specific customer interests. Banks can also use technology to evaluate the effectiveness of new products and promotions, and develop marketing campaigns to cross sell specific, recommended products. This translates to more-informed offers with greater response, leading to happier customers and improved bottom lines.

Small and mid-sized banks can use micro-segmentation and personalization to increase revenue, decrease costs and provide the kind of customer experience that wins customer deposits. Building and retaining relationships in the digital era is not easy. But banks can use technology to develop marketing campaigns and personalization strategies as a way to strengthen customer loyalty and engagement.

As the competition for deposits heats up, banks will need to control deposits costs, prevent attrition and grow deposits in a profitable and sustainable way. Small and mid-size banks will need to invest in technology to optimize marketing, personalization and operational strategies so they can defend and grow their deposit balances.

Drafting a Data Strategy


data-4-29-19.pngBanks need to be aware of trends in data analytics that are driving decision-making and customer experience so they can draft an effective data plan. Doing so will allow them to compete with the biggest banks and non-bank technology competitors that are already using internal customer data to predict behavior and prescribe actions to grow those relationships. These approaches leverage concepts like machine learning and artificial intelligence — buzzwords that may seem intimidating but are processes and approaches that can leverage existing information to grow and deepen customer relationship and profitability.


analytics-4-29-19-tb.png10 Data and Analytics Trends Banks Should Consider
Current trends in analytics include focusing on the customer’s experience, using artificial intelligence and machine learning in analysis, and storing and organizing information in ways that reduce risk. Banks also need to know about threats like cybersecurity, long-term developments like leveraging blockchain, and how to build a governance program around the process. Knowing the trends can help companies make educated choices when implementing a data strategy.

datat-trends-4-29-19-tb.pngHow Banks Can Make Use of Data-Driven Customer Insight
Banks can use machine learning and artificial intelligence to gain insights into customer behavior and inform their decisions. These data-driven approaches can efficiently analyze the likeliness of future events, as well as suggest actions that would increase or decrease that likeliness. Many institutions recognize the need for new technical capabilities to improve their customer insight, but a significant percentage struggle to embrace or prioritize the technology among other priorities at their bank. These institutions have an opportunity to establish a data strategy, map out their internal information and establish appropriate governance that surrounds the process.

The Transformative Impact Of Data & Voice



The biggest banks are spending billions on technology, but community banks can level the playing field by choosing technologies that personalize and enhance their interactions with customers, as Michael Carter, executive vice president at Strategic Resource Management, explains in this video. He shares how data and voice-enabled technologies could help community banks provide the digital experience that customers want.

  • Leveraging Data to Enhance the Customer Experience
  • Growing Use of Voice-Enabled Technologies
  • Opportunities for Community Banks

 

Strengthening Customer Engagement



Fintech companies are laser-focused on improving consumer engagement—but there is room for traditional banks to gain ground, according to Craig McLaughlin, president and CEO of Extractable. In this video, he shares three ways banks can strategically approach improving the customer experience at their own institutions.

  • The One Trait That Sets Fintechs Apart
  • Improving the Customer Experience
  • Understanding Digital Strategy

How Analytics and Automation Can Improve Shareholder Value


automation-2-8-19.pngAdvanced data science technologies like artificial intelligence (AI), machine learning and robotic process automation are delivering significant benefits to many banks.

As part of their mandate to protect shareholder value and improve financial performance, bank directors can play an important role in the adoption of these promising new technologies.

Technology’s expanding influence
With fintech companies generating new competitive pressures, most traditional banks have recognized the need to adopt some new techniques to meet changing customer habits and expectations. Declines in branch traffic and increased online and mobile banking are the most obvious of these trends.

Yet, as important as service delivery methods are, they are in a sense only the top layer of bigger changes that technology is bringing to the industry. New data-intensive tools such as AI, machine learning and robotic process automation can bring benefits to nearly all areas of a bank, from operations to sales and marketing to risk and compliance.

Advanced data analytics can also empower banks to develop deeper insights and make better, more informed strategic decisions about their customers, products and service offerings.

The power of advanced analytics
Historically, business data systems simply recorded and reported what happened regarding a customer, an account, or certain business metrics. The goal was to help managers understand what had happened and develop strategies for improving performance.

Today’s business intelligence systems advance this to predictive analysis – suggesting what is likely to happen in the future based on what has been observed so far. The most advanced systems go even further to prescriptive analysis – recommending or implementing actions that increase or decrease the likelihood of something happening.

For example, AI systems can be programmed to identify certain customer characteristics or transaction patterns, which can be used for customer segmentation. Based on these patterns, a bank can then build predictive models about those customer segments’ likely actions or behaviors – such as closing an account or paying off a loan early.

Machine learning employs algorithms to predict the significance of these customer patterns and prescribe an appropriate response. With accurate segmentation models, a bank can tailor marketing, sales, cross-selling and customer retention strategies more precisely aligned to each customer.

Automating these identification, prediction, and prescription functions frees up humans to perform other tasks. Moreover, today’s advanced analytics speed up the process and can recognize patterns and relationships that would go undetected by a human observer.

Industry leaders are using these tools to achieve benefits in a range of bank functions, such as improving the effectiveness of marketing and compliance functions. Many large banks already use predictive modeling to simplify stress testing and capital planning forecasts. AI and machine learning technology also can enhance branch operations, improve loan processing speeds and approval rates and other analytical functions.

Getting the data house in order
While most banks today are relatively mature in terms of their IT infrastructures and new software applications, the same levels of scrutiny and control often are not applied to data itself. This is where data governance becomes crucially important – and where bank directors can play an important role.

Data governance is not just an IT problem. Rather, it is an organization-wide issue – and the essential foundation for any advanced analytics capabilities. As they work to protect and build shareholder value, directors should stay current on data governance standards and best practices, and make sure effective data governance processes, systems and controls are in place.

AI, machine learning, and robotic process automation are no panacea, and banks must guard against potential pitfalls when implementing new technology. Nevertheless, the biggest risk most banks face today is not the risk of moving too quickly – it’s the risk of inertia. Getting started can seem overwhelming, but the first step toward automation can go a long way toward taking advantage of powerful competitive advantages this technology can deliver.

The Modern Roadmap To Gold



Today, data helps competitive banks identify key targets and make smarter—and quicker—loan decisions. In this video, Bill Phelan, president of PayNet, explains how data analysis is shifting loan decisioning, and how banks can survive and thrive through the next credit crisis. He also shares his outlook for business lending, and believes that Main Street America is still looking for capital to grow and improve their businesses.

  • Using Data to Make More Profitable Loan Decisions
  • How Credit Risks Analysis is Changing
  • Preparing for the Next Downturn
  • The Outlook for Business Lending

Maximizing the Power of Predictive Analytics



Data analytics affects all areas of the bank, from better understanding the customer to addressing regulatory issues like stress testing. However, organizations face several barriers that prevent unlocking the power of predictive analytics. John Sjaastad, a senior director at SAS, outlines these barriers, and shares how bank management teams and boards can address these issues in this video.

  • The Importance of Predictive Analytics
  • Barriers to Using Predictive Analytics
  • Considerations for Bank Leaders