Pub. 4 2022 Issue 1

Advanced-Data Analytics with Side-by-Side Success Model Empowers Midmarket Banks to Drive Revenue and Smarten Operations

Customers increasingly demand digital banking experiences, immediate results and responses to sales and service inquiries, and easy-to-use online platforms. It is common for banks to invest in building mobile and online banking platforms. Industry trailblazers harness the power of data and analytics to drive revenue and smarten operations by gleaning a better understanding of their customers for timely targeted cross-selling opportunities, lower risk lending, and reduced operational costs caused by ineffective duplicative customer outreach for sales and service.

So, where to start? The data analytics landscape has exploded over the past decade with an ever-growing list of products and services. Literally, thousands of tools exist to help businesses deploy and manage data lakes, ETL and ELT, machine learning, and business intelligence. With so many tools to piece together, how do business leaders find the best ones? How do you determine the best combination of tools and use them to get business outcomes?

The truth is that many tools are built for data scientists, data engineers and other users with technical expertise. With most tools, if you do not have a data science department, your company is at risk of buying technologies your team does not have the expertise to use and maintain. This turns digital transformation into a failed project without business outcomes instead of sparking data-driven revenue growth.

Yet most midsize financial institutions do not have data scientists or data engineers teams, and it does not make business sense to add these FTEs. Midmarket banking is stuck between a rock and a hard place, trying to compete with big banks and not get left in the dust of industry giants taking more market share by using data analytics to sweeten customer experiences. So how does the midmarket compete?

Side-by-Side Solution

A side-by-side service model provides value beyond most tools and platforms on the market by providing a data platform with built-in data management and analytics and access to human intelligence in data engineering, machine learning, and business analytics. While many companies offer tools – and many consulting firms can provide guidance in choosing and implementing the tools – integrating all tools and expertise in one end-to-end solution built for non-technical business users is key for digital transformation success for midmarket businesses.

The side-by-side model begins with a client success team working with the bank to drive digital transformation initiatives with meaningful business goals. The success of the digital transformation project should not be measured by hitting implementation milestones. Rather, the success of a digital transformation initiative should be measured by achieving business outcomes for the client. This means using data and analytics to reveal insights and tangible actions the business can take to meet its goals. Data mining can show when a customer is ready for a specific financial product based upon changes in account balances, mortgage pay-offs, credit scores, transactions between your financial institution by the customer and a third party investment account, and other customer behavior.

Accordingly, the client success onboarding plan begins with business goals and objectives of the bank. Goals, OKRs, and pain points of each client stakeholder are identified and prioritized to create an overall plan for how data and analytics will provide meaningful, tangible action for the bank. The prioritized business outcomes lead the direction and focus of the digital transformation initiative. This serves as a guidepost for which data sources to use and integrate into a data model for analytics and prioritizes pressing business questions the analytics target.

Onboarding outcomes include determining milestones, priorities, and measures of success. Service challenges are identified, such as possible gaps, roadblocks or delays in finding, accessing and wrangling the data into a form ready to answer the questions posed or building the analytic models to meet the bank’s business objectives. The onboarding plan creates a vision for success with clear timelines and expectations, and then documents how to deliver value to the client. The result is that digital transformation is directly tied to client business outcomes.

Case Study: Midmarket Bank Client Success Plan

A midmarket bank sought to add data analytics to boost sales and marketing strategy. Like many in the industry, customer data resided in various core and other systems, and the bank lacked a customer 360 view of data. In order to better understand its customers, customer behaviors, identify the most valuable customers to the bank, and use data to inform marketing and sales strategies, the bank wanted to implement analytics based on data from multiple lines of business, including multiple banking channels.

Goal #1: Trust the Data

The bank set the first goal – establishing trust in the data used for analytics. This involved bringing the data together from multiple disparate sources, some of which were internal, and some included third-party sources. Data sources included on-premise and cloud databases, transactional data, customer account master data for various products, including loans, mortgages, HELOC products, investment accounts and checking and savings accounts.

Customers sometimes had both personal banking and commercial banking accounts. Different people of a residence individually were account holders of varied products and services that a “family” or domicile collectively held together. Information between commercial and personal accounts and aggregated household was not readily available. This led to duplicate mailings, inadvertently offering a product to a household that already had the product, and marketing that appeared generalized instead of personalized. The data also needed to be cleansed for accuracy to eliminate errors, discrepancies, and duplicates. Account records needed to be matched by the customer (sometimes from multiple branches based upon where the customer opened an account) to create a customer 360 of trusted information.

Goal #2: Determine Use of Banking Channels 

Second, the bank set a goal of establishing a foundation for understanding customer banking interactions by channel. The intent was to focus on brick and mortar and move it to banking channels of how customers transacted and interacted with the bank.

The bank sought data showing the banking channels that each customer was using, such as ITM, ATM, branches, mobile banking, online banking, and self-identified customer contact preferences. From this data, the bank could better determine how its channels were performing, revealing under-utilization and customer preference indicated by behavior rather than survey results. It would also provide a clearer picture of allocation where customers were transacting business, instead of which branch a customer opened an account, typically the bank’s metric used for branch performance reporting.

The channel data would also reveal the best ways to contact and service customers based upon customer use of the channels, for fewer ineffective communication attempts, resulting in more efficient banking operations. There was a secondary goal of increasing customer engagement with digital banking to provide further operational cost reduction.

Goal #3: Build Enhanced Customer Profiles

The next priority was to create detailed customer profiles for targeted marketing. This was achieved by combining the customer and channel data to determine customer behaviors to allow the bank to discover opportunities for encouraging customers who were under-using a channel and understand the customer-specific preferences for banking.

  • Segmented customer lists would be created by product, location, channel, and householding families to get a better view of the complete listing of products and services used by a residence to pinpoint the next best products to offer the household.
  • Churn could be predicted by leading indicators so that bank personnel could reach out to engage customers before losing accounts. The data models needed to be updated based upon transactional data created each day.
  • Targeted campaigns for products and services could be built based upon actual customer activity and customer behavior.
  • “Ideal” Customer’ profiles could be informed by data and behavior.
  • A 360 overview of each customer across lines of business could be achieved to better understand lifetime customer value.

This included a secondary goal of decreasing ad hoc reporting requests made by the bank to the IT Department, which typically produced dated results when the IT department responded. The bank’s business analysts needed self-service data availability from a ‘single source of truth’ to answer pressing business questions.

Goal #4: Create Targeted Lead Generation Strategy

Finally, the fourth goal was to create a new targeted lead generation strategy for new and existing customers to expand product saturation based on data. The bank wanted targeted marketing lists available on-demand and automatically updated daily.

The new strategy included identifying specific products and services for cross-selling opportunities based on profiles, channels, distribution, behavior and referral sources. Rather than marketing a product to all customers, or those in a particular location, the new campaign would provide the next best products to market to individual customers, such as Wealth, Trust and Lending, based upon propensity models created by data and analytics. Leads and referrals would be tracked by source and type to establish a lead quality scale. Leads would be earlier identified, such as identifying high-wealth customers earlier in their wealth cycle. For commercial customers, factors such as collateral changes, industry trends, and NAISC changes would be included in the profiles. The analytics would be based upon data in motion to provide timely insights that the bank could act upon proactively instead of missing sales opportunities by reactive actions taken only after the window of opportunity expired.

Future priorities were identified to include developing product-centric data models to better evaluate product saturation and longevity to underwrite dynamic pricing, operational data models to provide insights to eliminate manual processes, and reduce expense through system consolidation and retirement (particularly for systems only being kept live due to data being held hostage to an old application).

The end result would enable the bank to use its data analytics for decision-making. Annual budgeting would be collaborative across multiple departments based on the trusted consolidated golden record of data created by the analytics solution. Data analytics would provide a better picture of client relationships and profitability.

Side-By-Side Success Measurement 

The progress toward meeting the business objectives was driven by the client success team to keep implementation success from stalling. Progress was reviewed biweekly. This provided a status update and opportunity to remove roadblocks, reveal early and incremental analytic insights that could add to or change business outcome objectives, and ensure alignment on the priority of the goals.

Success in meeting business objectives was reviewed quarterly to discuss outcomes from the previous quarter’s progress (what went well and what can be improved), update the objectives and key results for the next quarter’s focus, and determine owners of the tasks and project for the next quarter.

Additionally, the side-by-side service model included regular executive reviews to ensure value delivery and the next steps in meeting business objectives with data analytics.

As such, goals of implementing data analytics were achieved in a targeted path rather than the bank installing a new analytics platform and then figuring out how to use it, maintain it, and tie it to business outcomes to achieve ROI and value. Without the side-by-side access to data scientists, engineers, and business analysts specializing in the financial services industry, the bank’s digital transformation project would have stagnated in the gap between buying tools and making meaningful use of them for business outcomes. It would have been another case counted in the reported industry metric of 88% of digital transformation projects started that either fail or have not yet succeeded in meeting any business outcomes.