There are plenty of data and analytics consultants in the market. The choice of a data and analytics consultant can be critical to the success of your project, so how you can be sure you have chosen the best team to partner with?

At The Knowlton Group, we specialize in working with financial institutions. We know your challenges, the technology you utilize, and can provide solutions backed by expertise and experience.

But so can a handful of other qualified consultancies.

What the other consultancies don’t have is what we call “The Knowlton Group Advantage”.

The Knowlton Group Advantage and Why It Matters

“The Knowlton Group Advantage” is the closest thing to a full service data and analytics solution that can be provided to financial institutions. With our partner ecosystem and in-house expertise, we can help you manage and implement each component of a data and analytics program.

The Knowlton Group Advantage

There are several key steps involved with any data and analytics program.

Data and Analytics Strategy

You must have a plan of action and a strategic direction defined before commencing any data and analytics project. A data and analytics strategy is required to drive the strategic direction of the program for the next several years. Combining execution and strategy, this offering is critical to long-term success.

Data and Analytics Talent Acquisition and Staffing

Third party vendors and consultants can only do so much. Eventually, an organization must have the right internal talent to drive the strategic direction of the data and analytics program. The Knowlton Group can source, assess, and recommend the best data and analytics talent based on the cultural fit and skills required for the role.

Data Warehouse Implementation and Customization

Organization’s that have a data warehouse in place often require additional integrations or customizations after the initial implementation. Need to integrate your new loan origination system or other third-party application into your data warehouse? No problem.

If you don’t have a data warehouse currently implemented, we have existing relationships with vendors who have been handpicked based on their ability to provide the highest quality product.

Data-Driven Business Strategies

You have a data warehouse in place and a data and analytics team staffed. Now what?

Let our team and partners work with you to define how best to leverage the data and analytics platform in place. We have helped each and every business unit within the bank or credit union drive business decisions and strategies through the use of data and analytics.

We can help you put your data to use.

Statistical Modeling and Analytics

There are times when advanced statistical modeling and predictive analytics requires a highly specialized skill set and expertise. From segmentation to product propensity analysis to advanced machine learning applications, we can recommend the top experts in these areas (and more!) to ensure seamless delivery and execution.

Why it matters

There are many moving parts in a data and analytics program. This can present significant challenges to organizations without the in-house experience and expertise. Through our own internal capabilities and the strengths of our relationships with the best vendors in the market, we can fully support and manage the entire data and analytics lifecycle.

Take the stress out of your data and analytics program with The Knowlton Group Advantage.

Properly building a data and analytics solution is no easy task.

Properly deploying a data and analytics solution (so that it actually gets used!) is no easy task.

When execution is devoid of strategy, the solution will fail.

When strategy lacks execution, the solution will fail.

At this point, you are probably saying to yourself, “Ok, we get it; data and analytics is pretty tricky. So what?”. (Or, you might be saying how negative this guy writing the post must be!).

Data and analytics implementations require the right combination of strategy and execution. Rich Jones of Leading2Leadership and I have worked with several financial institutions to merge the tactical and the strategic. In fact, our involvement and experience in defining and executing on data and analytics strategies has enabled us to clearly define a data and analytics strategy framework that works.

The Knowlton Group Data and Analytics Strategy Framework

Data and Analytics strategy framework

The Knowlton Group Data and Analytics Strategy Framework

Simple and concise, “The Knowlton Group Data and Analytics Strategy Framework” brings clarity to what can be an intimidating process. What follows is a description of each step in the framework that can be used by every organization that hopes to become data-driven.

1. Discover

Every major project or implementation must start with discovery. We recommend organizations begin by creating a data inventory and a report inventory to understand the existing data environment.

Business and reporting processes are becoming increasingly data-driven. Be mindful of how data can be used to track, monitor, and improve key processes. With a greater understanding of the processes driving the bank or credit union, the requirements for the final data and analytics solution will become clearer.

2. Strategize

The strategize step is one of the most critical steps in the framework that is almost always overlooked. Remember what we said at the beginning of this article: “When execution is devoid of strategy, the solution will fail.”

Before beginning the development of the data and analytics solution, you must define an overarching strategy that will drive the development, deployment, and utilization of the solution for the next several years. You must ask question such as:

  1. Do we have the right data and analytics talent?
  2. How quickly can we implement each piece of the solution?
  3. Do we have a culture that embraces data?
  4. Does our budget support our timeline?
  5. Does our executive team believe in the power and value of data and analytics?

Without a clearly defined strategy, your team is like a contractor without a blueprint.

3. Develop

With discovery completed and a strategy in place, the solution can begin to be developed. What gets developed and the order in which development occurs is going to be entirely unique to each organization.

For most banks and credit unions, we tend to suggest that a data warehouse (or, at a minimum, some type of central data repository) be designed and implemented. We find that most banks and credit unions have somewhere between 30 and 50 third-party applications or data sources housing data. Because of this expanse of third-party applications, the data becomes very disparate. Getting a 360-degree view of the member or customer becomes nearly impossible and/or incredibly cumbersome. A data warehouse will resolve these issues and provide a centralized, single source of truth for your data.

Be sure that both technical and business resources are members of your development team. A data and analytics solution is neither completely business-driven nor completely technology-driven. Rather, it requires a blend of the knowledge and skills of both areas of the organization to design the optimal solution.

4. Deploy

Data and analytics solutions are not a “if you build it, they will come” solution.

Let me repeat that. Data and analytics solutions are not a “if you build it, they will come” solution.

When we hear of data and analytics programs that fail, one of the common reasons is that the team responsible never deployed the solution to the organization effectively.

Would you implement a new core without training users throughout the organization? Of course, not! But we find that BI teams do not provide the appropriate amount of education and training to the rest of the company.

Your team could build the greatest data and analytics platform in the world, but if no one uses it – who cares? The “Deploy” phase requires your team to interface with the consumers of data throughout the bank or credit union to help them understand how they can use the solution that has been developed. Show them how they can use data to improve processes, make better business decisions, or visualize data and reports quickly and more efficiently.

5. Analyze

You’ve completed discovery, defined a strategy, developed the solution and deployed the solution. You’re done, right?


Your data and analytics solution is a living, breathing organism. The needs of the business users will constantly change, technology constantly changes, and new ways to use data to drive business growth are always being explored. Your data and analytics team must constantly be analyzing the effectiveness of the solution and determining new and improved ways to use data throughout the organization.

Constantly questioning how you can make the solution better is critical to any sustainable success.

6. Iterate

After the analyze phase, you may find that some key data is missing or a new application is being deployed that must be brought into the solution. At this point, you must cycle back to the “Discover” phase of the framework.

This doesn’t mean that you are completely redesigning the solution, but you need to figure out how to integrate this new application into the solution. This requires discovery, strategy, development, deployment, and analysis just as the framework describes. In fact, we have a whole post dedicated to why data and analytics is an iterative process because it is that critical to long-term success.

Like a small child, your solution requires constant nurturing and guidance. Iterating through the entire framework ensures that your data and analytics solution remains a success for years to come.

The Knowlton Group’s Data and Analytics Strategy Framework is designed to be simple, concise, and help every organization to become data-driven. By following this framework, we strongly believe (and have witnessed first-hand) that your bank or credit union can become data-driven in as little as eighteen months.

If you feel like you need a little bit of help getting started or guidance during any step in the process, ask how we can help by calling 860-593-7842 or emailing Brewster at

The terms business intelligence, data and analytics, and big data strike a combination of excitement and fear into business leaders’ minds. My goal for this post is to alleviate some of the fears associated with business intelligence and analytics (big data is a whole different world). Community banks and credit unions, regardless of size, can start using business intelligence (BI) today to yield tangible benefits. All you need to do is start with reporting.

Reporting: All You Need is Excel

While a data warehouse is often the foundation of a robust business intelligence platform, as long as you have a single SQL database (like Microsoft SQL Server, Oracle, MySQL, DB2, etc.) you can begin to use business intelligence.

Let’s assume that your core’s database is a SQL database (your IT team should be able to answer this question for you very quickly). Even if you don’t have a program like iDashboards, SSRS, Tableau, PowerBI, or some other data visualization and reporting application, you can still begin to create reports. Microsoft Excel 2010 and above have a fantastic feature, PowerPivot, that allows you to create and monitor KPIs right in Excel. You can create SQL queries, return data, create charts and graphs – all without leaving Excel.

Let me reiterate this: to get started with reporting and business intelligence, all you need is Excel.

Is this the best long term solution? Probably not. But it is important to start small when thinking about how to expand business intelligence in your community bank or credit union.

SSRS – You Probably Already Own It

SSRS, SQL Server Reporting Services, is a reporting tool built right into Microsoft SQL Server (one of the most common types of databases deployed at community banks and credit unions). This browser-based reporting application allows you to easily create, manage, and deploy reports to users throughout your organization.

Did we mention you probably already own it?

The theme here is to start small and use the tools already available to you. If you have SSRS, start to experiment with the program. Find someone in your company who wants a report built and design a small proof-of-concept. Once interest is piqued, one report becomes ten and then ten becomes one hundred. Soon, the use of business intelligence will spread like wildfire throughout your community bank or credit union.

Start Learning SQL

Learning SQL seems much harder than it really is. We’ve provided some great free SQL lessons throughout our site to help you get started. Looking for something a little more comprehensive and hands-on? Take a look at our top-rated online SQL course; it’s a great value for SQL beginners.

Once you have learned SQL, you can start writing queries to retrieve data from a database. You can use SQL to pull data into Excel to then build reports, charts, and other visuals like we mentioned before. If you want to start using SSRS, start by creating some very simple SQL queries. Once you write the query, you will find that SSRS is very intuitive for new users.

Don’t feel like you need to become a SQL expert. Learn enough to start making an impact, and you can learn more complicated features as you go.

To Begin, Begin!

Using business intelligence is just as much a capability to the $100 million community bank as it is to the $5 billion credit union. Keep things simple and start working with Excel. Once you have made some basic reports with Excel, start to experiment with SSRS. Learn enough SQL to get started, and make incremental progress. Business intelligence can be as simple as reporting – something every bank and credit union can get better at.

Need help getting started? Give us a call at 860-593-7842 or send an email to!

Over the past few weeks, we have released posts describing how different members of the C-suite can make use of data and analytics. We started out discussing five ways CMOs can use data and then talked about the CLO and lending analytics. In this post, we want to highlight the ways that a CFO (Chief Financial Officer) can make use of data and analytics.

How Can a CFO Use Data?

CFOs can benefit in several ways from data and analytics. From near real-time access of key data to addressing data quality issues, CFOs – especially in community banks and credit unions – have great opportunities to leverage data and analytics.

1. Lose the Overly Complex Excel Workbooks

As a CFO, does your finance team have incredibly complex Excel workbooks? When data and/or requirements change, how time-consuming is it for your team to quickly deliver you updated results?

With the growth of data and analytics and the increasing use of data warehouses in combination with visualization tools, CFOs and their teams can lose the overly complex Excel workbooks. Using industry-standard (and affordable!) visualization tools, ad-hoc reporting can eliminate complexity and manual effort to get even some of the more complicated data points. Working with your internal business intelligence team, processes can be automated to allow your financial analysts to spend less time fussing with Excel files and more time actually analyzing the data.

2. Repeatable, Automated Processes

Similar to some of the concerns mentioned in the first point, CFOs and their teams tend to have manual, multi-step processes built to create reports and retrieve information daily, weekly, monthly, and quarterly. Now imagine giving that time back to you and your team to increase productivity.

With a data and analytics platform, a CFO can enable his team to automate what tend to be very time-consuming and cumbersome processes. If a team member is out for some reason, the automated process still gets completed. This eliminates concerns that may arise if there is turnover in the CFO’s team as processes are removed from any one individual. Processes to gather information and produce reports are created more quickly, more efficiently, more accurately, and in an automated fashion.

3. Integration of Data

With a data warehouse, CFOs can begin to understand their organization’s operations more holistically. With an integrated data and analytics platform, a CFO can examine financials while “drilling across” to view loan, deposit, or channel usage details. As more community banks and credit unions look to stay competitive in such an evolving industry, the insights provided by an integrated data platform will become critical for CFOs.

4. Data Quality

How many CFOs trust the data they get from their MCIF? Probably not a ton of you, right?

But many community banks and credit unions use an MCIF as their primary and sole tool for business intelligence. This naturally presents a challenge for CFOs because an MCIF (Marketing Customer Information File) is primarily designed for marketing. Sure they may have some financial capabilities built into them, but MCIFs are not designed to be a financial reporting tool.

With a enterprise data and analytics program built, a data warehouse or central data repository of some kind will be created. This platform will enable CFOs to look at data without the MCIF’s scope and filters affecting data quality and accuracy.

5. Compliance and Regulatory Reporting

It’s no secret that compliance and regulatory pressures will continue to impact community banks and credit unions. As a CFO, these pressures will become increasingly important to monitor, audit, and track.

With a well-defined data and analytics platform, CFOs can help their teams do a better job of tracking and ensuring compliance for key policies. For example, dashboards can be built to automatically track Risk-Based Capital ratios and other financial details auditors will undoubtedly be focused on. As CECL implementation and standards timelines inch closer, data and analytics will enable CFOs to monitor, track, and conform to the appropriate standards.

6. Data-Driven Pricing Models

Companies like Deep Future Analytics are starting to bring this idea to many banks and credit unions. By leveraging your historical data, statistical models can be applied to optimize pricing while maintaining (or even reducing) your organization’s credit risk. Based on past loan performance, your bank or credit union can identify whether or not you can offer better rates on C or D quality loans while ensuring adequate ALLL.

We anticipate a sharp increase in the number of community banks and credit unions performing this type of price modeling as data and analytics become more commonly leveraged throughout the industry. Large financial institutions have been doing this type of work for years; as data and analytics technology becomes more practical for smaller organizations, these advanced models should become more frequently adopted. Increased collaboration between the CFO and CLO can maximize the power and effectiveness of these pricing models.


CFOs, like all members of the executive team, can benefit from data and analytics in several ways. We believe that all community banks and credit unions can and should become data-driven. By understanding some of the ways the CFO can use data and analytics, our hope is that your organization takes a more focused approach to building out your business intelligence program.

An increasing number of organizations are making data and analytics an integral part of their business operations. In Thomas Davenport and Jeanne Harris’ book Competing on Analytics: The New Science of Winning, they define the most analytically advanced businesses as “Analytical Competitors”. These organizations embed data and analytics into their operations and are a fundamental aspect of their business model. Data and analytics, then, is not promoted as a technology initiative but rather as a business initiative. This is a bit of a paradigm shift for most organizations with a less advanced analytic capability. However, data and analytics projects must focus on the business first to be implemented and adopted successfully.

What Happens if Data and Analytics Projects Focus on Technology First?

A common mistake for organizations just starting to build out a data and analytics program is to focus on the technology first. These organizations tend to first ask questions like:

  • What data do we have available in our database?
  • What data visualization tool should we buy?
  • How can we get as much data as possible into our data warehouse/repository?

Each question lacks any insight into how business users will leverage data and analytics.

Focusing on what data is available currently potentially ignores a wealth of information not currently available to your business. External data, like US Census Data or social media information, is already ignored with this mindset. Especially in community banks and credit unions, their tends to be a large number of third-party applications. A majority of these applications may not be hosted on-site and, therefore, access to data stored in these systems could be currently limited. By focusing only on what data is currently accessible, potentially valuable data could be ignored.

Focusing on data visualization tools or data warehouse products should not be one of the first questions asked by your data and analytics project team. Last year, we posted an article titled “Having Business Intelligence Software is Not Equal to Having Business Intelligence”. In this article, we discussed why the tools alone are not enough. A business intelligence strategy needs to address how those tools will be leveraged. Training and development, cultural changes, and proper analysis of the business processes and needs are required for any actionable insights to be gained.

A data warehouse, which, for this post, we assume will be the foundation of your data and analytics program, is not meant to be a repository of ALL data. It is designed to to integrate data from various applications for the purposes of reporting and analysis. Simply copying operational databases with all available data doesn’t create a simpler, analysis-driven design. Organizations that don’t take an iterative approach to building their data warehouse often find themselves buried in an overly-complex project six months down the road.

So, Why Focus on the Business First?

Focusing on the business first creates some significant advantages:

  • You understand the data needs of business users
  • Better understanding of the processes that drive the business
  • Complete picture of the application environment with data integration priorities applied to each source

Since data and analytics projects are designed to provide data driven insights for the business, focusing on the business needs of the business is the best starting point for these projects. With a better understanding of what your business needs, a technological solution can be developed. Without a clear picture of the data needs and goals of the business, there is no guarantee that the needs of the business will be met by the solution. Understanding the business and, specifically, their KPI and metric requirements should be a foundational part of the data and analytics project. Especially with project teams that are mainly supported and staffed by IT, these teams lack the organizational clarity and understanding needed to define high-quality requirements for the data and analytics project.

An understanding of critical business processes is essential to successful data and analytics projects. Processes drive how customers interact with the business and how staff interact with the applications. Each process creates data and each data point gives us a potential opportunity to create insights into operations. By understanding these processes, “quick wins” can often be uncovered to simplify processes or to reduce the amount of manual effort and time involved with them. These quick wins help justify a BI investment and improve the data and analytics project’s ROI.

If you focus on the business first, you will understand what applications are used throughout the organization. We find that our bank and credit union clients typically have well over 30, 40, and even 50 applications. Each application has a purpose; figuring out where that application fits into the data and analytics project is critically important to your strategic roadmap. With an understanding of all applications used in the organization, you will be able to accurately prioritize which applications should be integrated into your data and analytics platform and in what order. Coupling this with the understanding of key business processes, the data and analytics project team will have a great amount of business knowledge with which to design and support the initiative.


The summarization of this post is simple: focus on identifying the business needs for data and analytics FIRST. Only after addressing those needs should the data and analytics project team start to explore which technologies to employ and how best to design the solution. Data and analytics projects that focus too heavily on the technology inevitably fall short of their long term goals.

In one of our last posts, we talked about how marketers can use data. As part of the growing usage of data and analytics, we want to highlight the different ways key executives can leverage data to drive their business. In this post, we discuss five ways that lending analytics can be used in any bank or credit union.

A CLO (Chief Lending Officer) often has a wealth of information at their fingertips. Modern loan origination platforms have mountains of information. Unfortunately, much of the information contained in the LOS systems goes unused by lending teams. With the growth of non-traditional lenders, like LendingClub, credit unions and community banks must become more analytically driven to remain competitive lenders.

Five Ways CLOs Can Use Lending Analytics

Below we identify five ways that CLOs and their lending teams can use data and analytics:

1. Concentration Risk

Would a wise investor put all of his funds in one sector? One company? Of course not. Just as investors must ensure their portfolio has adequate diversity, lenders must ensure their risk is not overly concentrated. Concentration risk might involve stratifying your loan portfolio by geography, product type or credit tier to ensure risk is appropriately spread across a variety of variables.

For example, if your lending portfolio is heavily concentrated in a particular county where business is booming, what happens if business collapses in that county? What might have looked like high credit quality loans could change dramatically and quickly if significant layoffs or business bankruptcy occurs. Similarly, CLOs want to strike a proper balance amongst their credit tiers. Buying A and A+ paper might reduce charge-offs, but this will negatively impact net interest margin by reducing the average yield for your portfolio.

Developing KPIs to constantly monitor concentration risk is something all lenders can and should be doing today. The reporting process should be automated to ensure accuracy and timeliness of information.

2. Segmentation Analysis

Segmentation analysis is another great way for CLOs to get started with lending analytics. When integrated with profitability data, CLOs can identify which customer segments are driving the most revenue and profits.

Segmentation analysis can manifest in a variety of forms: lenders can segment customers by profitability, by geography, origination channel, or combinations of these and other factors. Proper communication with your bank or credit union’s marketing team then enables your organization to develop strategies to effectively target and lend to key segments.

For organizations with a mature data and analytics program, integrating lending data with other data sources can prove invaluable and open up additional segmentation opportunities. Overlaying loan application data with third-party data (like US Census Data) can identify new opportunities to expand and grow your loan portfolio.

3. Loan Performance Analysis

Your lending team probably spends a significant portion of their time generating new loans. But how are you monitoring loans that have already been funded? How are these loans performing over time?

Proper monitoring of a loan’s performance throughout its life is critical to successful data-driven lending. By monitoring loan performance over time, you will start to uncover key insights like how many loans are being charged off, are in default, have been paid off and other outcomes. The results may highlight common characteristics of high-performing loans and of low-performing loans. This information can then be used to manage your underwriting criteria along with your loan growth strategy.

4. Data-Driven Pricing Models

Companies like Deep Future Analytics are starting to bring this idea to many banks and credit unions. By leveraging your historical lending data, statistical models can be applied to optimize pricing while maintaining (or even reducing) your organization’s credit risk. Based on past loan performance, your bank or credit union can identify whether or not you can offer better rates on C or D quality loans while ensuring adequate ALLL. By understanding your past charge-off history and integrating with loan performance, this form of lending analytics can keep your CFO and your CRO (Chief Risk Officer) happy.

We anticipate a sharp increase in the number of community banks and credit unions performing this type of price modeling as data and analytics become more commonly leveraged throughout the industry. Large financial institutions have been doing this type of work for years; as data and analytics technology becomes more practical for smaller organizations, these advanced models should become more frequently adopted.

5. Non-Traditional Risk Metrics

FICO score have been the dominant number used in credit decisions for the majority of financial institutions. But, as 2008 showed, credit scores are not as a flawless as they are often believed to be.

Modern banks, like SOFI, are using non-traditional metrics to fund and refinance the massive amount of student loan debt outstanding. Their lending analytics models rely on factors like where you want to college and your current employment and income status. As more and more millenials “say no to credit cards”, traditional banks and credit unions should consider how to integrate these non-traditional metrics into their underwriting criteria.

While we don’t expect smaller organizations to completely overhaul underwriting criteria using non-traditional risk factors, coupling these risk factors with traditional credit score-based underwriting models could generate additional lending opportunities.


Lending analytics are becoming more common with community banks and credit unions. For banks and credit unions to remain competitive, a greater adoption of these data-driven techniques must become a part of any strategic plan. While we understand that jumping right into, for example, social media mining for credit decisions is a big leap for most organizations, laying a foundation to continuously improve lending analytics is critical, if not necessary, to remain competitive in the next decade.

Data and analytics is exploding.

Whether you call it big data, business intelligence, or analytics, it is clear that the use of data is going to be critical to any company’s success. Especially in the bank and credit union industry, data and analytics is a relatively new concept. Sure, data has been used in the past but never has it been so critical to sustainable operations.

With the growing adoption of data and analytics, the bank and credit union industry is reaching a critical juncture. In a previous post, we discussed how data and analytics will become a competitive necessity – not a competitive advantage – in the very near future. If you believe this to be true, two questions immediately arise:

1. In what ways can we use data and analytics?

2. Once we know what we want to do, how do we actually do it?

Over the next few weeks, we will be releasing several posts that primarily address the first question. Specifically, we will be addressing question 1 from several different executive perspectives. The ways that a CMO (Chief Marketing Officer) would use data and analytics might differ from how the CLO (Chief Lending Officer) or CFO (Chief Financial Officer) would use data and analytics.

Five Ways CMOs Can Use Data

In this post, we identify five ways that CMOs can use data.

1. A/B Testing

With the analytics now available from sites like Google Analytics, HubSpot, and MailChimp, data is readily available for website traffic, conversion rates, click-through rates, and a variety of other data points that are a gold mine for marketers.

A/B testing allows marketers to compare two versions of something (a site or page design, email content, offer, etc.) and measure the results of the user experience and interaction. For example, you could use A/B testing to determine which email subject line generated more opens or which content led to more conversions.

The ability to tweak content to users while using data to support your decisions allows marketers to gain valuable insights and deliver more successful messaging to consumers.

2. Customer/Member Segmentation

We have already discussed member segmentation in a previous post, but its value to marketers cannot be stated enough. Through data and analytics, marketers can understand the channel usage, product propensities, and a variety of other details about specific segments of their customer base/membership.

Does the millennial want the same user experience as the retired baby boomer? Certainly not; it is important to understand these differences and create an experience that is most beneficial to that particular segment.

With the growth of non-traditional financial institutions like Ally Bank, a 100% digital bank, or Lending Club, a peer-to-peer lending platform, CMOs in the banking industry will need to use data and analytics to deliver the proper experience and messaging to the appropriate segments.

3. Channel Analysis

There is some overlap between channel analysis and customer/member segmentation, but, with the digital growth of the financial industry, channel analysis is deserving of its own section.

Who is contacting your call center? Who hasn’t completed an in-branch transaction throughout their entire customer/member lifetime with your FI? These are the types of questions CMOs must ask and data and analytics can answer. Asking these questions enables the CMO to define the proper growth strategies based on the underlying data.

4. Measuring Consumer Sentiment with Social Media

Facebook, Twitter, Instagram, LinkedIn and the multitude of other social media networks can offer a wealth of information about consumer sentiment. With each new post about your bank or credit union, you can gain a more complete picture of the consumer’s feelings towards your FI. CMOs can use data from social media to gain insights that would ordinarily require surveys or focus groups to gather. These insights can be gained more quickly and efficiently with new technology.

Several companies now specialize in analyzing social media posts to determine consumer sentiment towards a branch or product. These insights, when combined with data from previous marketing efforts and internal operations, allow a CMO to identify and address flaws in product, services, or messaging quickly and efficiently.

Analyzing social media posts and the associated text is considered “unstructured data”. The key difference between standard business intelligence and big data lies in big data’s integration of unstructured and structured data sources. Organizations that can leverage social media information can accurately state that they are using big data in their marketing efforts.

5. Tracking and Measuring ROI

Have you ever watched Shark Tank? How often do you hear Mark Cuban ask an entrepreneur “what is your cost of customer acquisition”? Can your bank/credit union get this metric? Can you get it quickly?

CMOs can become their CFO’s best friend by combining financial data with their marketing information. Tracking average conversion on marketing campaigns combined with product and service profitability data, you can create realistic ROI models for finance to evaluate marketing activities. Overlay this data with the strategic growth objectives of the bank/credit union, and your models and forecasts will improve significantly.

The ability to measures and track success with your campaigns enables you to learn from each marketing effort. Even some of the other techniques mentioned in this posts, like A/B testing and monitoring social media sentiment, can be combined with campaign tracking to improve the likelihood of success for future efforts.

What does this all mean?

The US Navy SEALs have a saying, “slow is smooth; smooth is fast”. For data and analytics in organizations, it can be said like this: “small steps lead to small wins; small wins lead to big wins”.

CMOs can use data to drastically improve marketing efforts. In their positions, they have the ability to drive data and analytics initiatives by applying some of the way to use data we have suggested in this post. Marketers should start by taking small steps with data and analytics. These small steps will lead to small wins. But, after a few small wins, some big wins will emerge. Like a flywheel, its challenging, at first, for data and analytics initiatives to gain momentum. But with some small wins and a good data strategy, your data and analytics initiatives will gain momentum. Once it gains momentum, those small wins will have led to big wins and your organization will be on its way to becoming data-driven.

Most people don’t know it by name, but most recognize the unique design of the Sierpinski Triangle. The fractal is designed by recursively breaking down equilateral triangles into increasingly smaller triangles. Mathematicians have a field day with this type of object, but most people don’t equate it to any type of practical business visualization. As we talk about organizational KPIs, you will see that the Sierpinski Triangle is the perfect visualization of how we would like to create clear line of sight from department operational metrics to strategic KPIs.

What are KPIs?

KPIs (Key Performance Indicators) are defined well by Investopedia who define them as “a set of quantifiable measures that a company or industry uses to gauge or compare performance in terms of meeting their strategic and operational goals.” For example, achieving a return on assets (ROA) of 70 basis points, 5% loan growth, or 3.5% share growth are all examples of KPIs that a credit union might define as their strategic metrics for the year.

KPIs are not designed to be tactical or operational in nature. Achieving an application to funding time of under thirty days for a new mortgage is more operational in nature and therefore wouldn’t be considered strategic enough to be an organizational KPI. That is not to say that the metric isn’t important – it simply is better suited as an operational, departmental metric.

The Relationship Between KPIs and Departmental Metrics

Departmental metrics are more operational in nature. In the last paragraph, we gave an example of reducing the average time to funding for mortgage applications. This would be a great example of an important metric for the mortgage lending department of a bank or credit union. A consumer lending department might be interested in the month-to-date origination volume as a departmental metric. These metrics, unless of the utmost importance to the strategic direction of the financial institution, are best suited to be measured and managed at the departmental/business unit level.

KPIs and departmental metrics should have a direct relationship. Let’s assume, for example, that return on assets (ROA) is a KPI for your financial institution. ROA is defined as the ratio between net operating income (net operating earnings) and average total assets. For those organizations with well-defined KPIs, this ratio is usually very closely monitored. The CFO, however, might care about average total assets and net operating earnings to date as two separate metrics for the finance business unit. The organization measures and manages to the ROA; the finance department might measure and manage the two individual metrics that comprise ROA.

KPIs and Sierpinski

What does the Sierpinski Triangle have to do with KPIs? Consider the outer triangle as the organizational KPIs. These KPIs encompass the entire bank/credit union and are integral to the organization’s strategic direction. Then consider the next level (the four next biggest triangles within the outer triangle) as the major business unit metrics. For example, in an organization with a COO, a CFO, a CLO, and a CTO, the four inner triangles would represent the metrics managed by each of those four C-level individuals. The next level of smaller triangles within the second level of triangles (or metrics in our analogy) might represent the departments managed by the C-level staff. For example, the CLO might have three departments under his or her guidance: consumer lending, mortgage lending, and commercial lending. These increasingly smaller triangles might represent the metrics for each of those departments.

So, the Sierpinski Triangle is the perfect representation of developing a clear line-of-sight between the departmental, operational metrics and the strategic KPIs. The loan originator should know how his or her daily work supports the strategic KPIs and goals set forth by the CEO. This line-of-sight and transparency that proliferates top-down throughout the organization is one of the greatest strengths of a successful data-driven organization.

Ask yourself this: Is there a clear line-of-sight between the daily activities from those in front-line operations and back-office administrative support roles to the KPIs and goals of the organization? Does branch staff understand how their day-to-day actions affect the overall growth of the bank or credit union? Defining and nurturing this line-of-sight is a major factor in the success of any business intelligence and analytics program. This also helps support the growth of a data-driven culture that is necessary for sustained success in the areas of analytics and BI.

For assistance in defining your organization’s data strategy or if you simply have questions about business intelligence and/or analytics, please contact Brewster Knowlton at or call 860-593-7842.

In our last post, we discussed why analytics will become a necessity to compete in an evolving banking industry. In that article, we promised to follow up with a series of posts that contain specific examples of how analytics can be applied. This first post will discuss how analytics and business intelligence can be used for member segmentation analysis and developing member/customer insights.

Using Analytics for Member Segmentation and Insights

If a member calls into a contact center asking about a fee they were assessed, what would your staff see about this person? If your organization is like most, the contact center staff would see this member’s basic accounts, current balances and maybe recent interactions with the credit union that were tracked in a CRM system. But would they see that this member also had $2 million with your wealth management unit? Or that this individual has a $500,000 mortgage with the credit union on a home worth over a million dollars?

Most banks and credit unions do not possess a complete 360-degree profile of a member’s products and services. This poses several issues that can negatively affect the organization.

Member Segmentation and Insights by Overlaying Profitability

Without a complete picture of the costs and profitability associated with all products and services, member segmentation is at worst impossible and at best incomplete. Overlaying product and service profitability with all products and services owned and used by a member allows you to segment your membership based on net profitability.

What is more profitable – the member with a share draft, a primary share, and a new auto loan who transacts in branch or the member with a primary share, a home equity loan, a debit card, a credit card who transacts using only digital channels?

By segmenting based on profitability, we gain greater insights into our membership while also furthering our understanding of the product and service offering.

How can we leverage this information?

Take one of Progressive’s best features – the competitor price comparison. If you go to Progressive’s website, you can get a quote from them and their competitors all at once.

Why would Progressive want to show you the price of competitors when Progressive might be higher?

They are so confident in their ability to calculate risk-based pricing that they believe if a competitor can offer you a lower rate, then you don’t fit into their desired risk profile. By not fitting into their desired risk profile, Progressive is creating a natural filtering of customers that might end up costing them more in claims payout than in premiums received. They are, in essence, saying “if someone can offer you a lower rate, then we feel you are too risky to be our preferred customer”.

What does this have to do with member profitability segmentation?

Not every member/customer is going to be profitable. But wouldn’t we want to maximize the number of profitable members in our membership? By identifying unprofitable members, we can target market to entice them into more profitable products and services. If your primary marketing effort to acquire new members involves a free checking account with other free benefits, is that really the best strategy? You are most likely acquiring members that tends to be predominantly unprofitable. Consider offering free checking and other benefits if they sign up for a more profitable product or service. Instead of gaining unprofitable members, your marketing now acquires profitable members which your CFO will most certainly be excited about.

These relatively simple business strategies can only be accurately designed with profitability data that allows for member segmentation.

Member Segmentation by Channel Usage

What about channel usage?

How does a primarily digital member, like myself, differ from a member who does all of their banking in a branch? What about one who frequently contacts the call center? By understanding which of our members use digital channels – and how frequently – it allows us to design marketing and member experience strategies to leverage this information.

If you want to convince me, a digital member, to get a new credit card, the best strategy is not to send me something in the mail. But, what if you could add a credit card offer to the header or a call-to-action in online/mobile banking next time I log on? Your conversion rate will be much higher using this approach than simply blanketing everyone with the same message. The ability to leverage this targeted marketing approach is only possible after segmenting your membership by channel usage.

Customer Lifecycles

What would the value be to your organization to identify a member who used to have $10,000 in savings and an average balance of $3,000 in checking but now has only $1,000 in savings and has closed their checking account? This would seem to indicate that this individual has shifted their primary banking operations to another financial institution.

With share of wallet being such a critical metric (especially in highly competitive and saturated markets), being able to identify these individuals is critical. Unfortunately, credit unions and banks tend to struggle when it comes to understanding their member/customer lifecycle.

Ideally, we would want to define triggers that would initiate customer retention measures to re-engage this member/customer before they have completely switched their primary banking activity to another FI. Using analytics and the various techniques that can be employed, an organization can relatively simply identify these individuals and potentially regain a greater share of wallet.

Last Words

The shift from reactivity to proactivity is a major paradigm shift that analytic organizations embrace. By investing in business intelligence and analytics, banks and credit unions are able to make more effective strategic and marketing decisions through improved member segmentation analysis and insights. These are but a few of the many examples and applications of how you can employ analytics within your FI.

To learn how The Knowlton Group can help you define the proper business intelligence and analytics strategy or if you need help implementing an analytics solution, contact Brewster Knowlton at or call 860-593-7842.

Analytics in Credit Unions and Banks

The word analytics now tends to bring companies like Google, Amazon and Facebook to mind. What most people don’t realize is that analytics – and the benefits it brings – can be achieved by organizations of nearly every shape, size, and industry. In the banking industry, most would tend to equate analytics with huge financial institutions like Bank of America, JPMorgan Chase, or Citigroup. But the $500 million banks and credit unions as well as the $5 billion banks and credit unions can achieve wildly successful results through the adoption of analytics and business intelligence programs.

For a variety of reasons, analytics in the banking industry tends to be treated as a “nice-to-have” innovation – ripe with risk without yielding significant returns – as opposed to a competitive necessity. Increasing regulations that place a greater strain on fee income and operating costs along with a low interest rate environment means banks and credit unions must become more innovative to stay relevant and competitive. Factoring in the advent of non-traditional financial institutions and competitors like LendingClub (a peer-to-peer lending platform) and Ally Bank (a 100% digital bank with over $100 billion in assets) places even more competitive pressure on traditional banks and credit unions.

Want to know how to combat this changing environment? Invest in analytics and business intelligence.

Over the next few weeks, we will be releasing several posts that identify specific ways financial institutions can benefit from analytics. We will discuss several areas in which analytics can be applied including member segmentation insights, profitability and lifecycle analysis, rate risk modeling, internal operation efficiency gains, channel analysis and alignment, and strategic planning and decision making.

Our hope is that through this education on analytics and business intelligence, your organization can adopt a more analytical mindset and take the first steps towards becoming data-driven

Banking in the year 2030

It is now common knowledge that purchasing airline tickets at different times relative to your departure impacts the price you pay for the seat. Hotels use a similar technique to adjust room rates. This technique is known as “yield optimization” or “revenue management” to maximize revenue based on anticipating demand and supply (either airline seats or vacant rooms). This is a highly analytical technique and represents a fantastic use of data to increase profitability. Yield optimization was first introduced by American Airlines in the 1980s which led to bringing in $1.2 billion over three years and even eliminated some competitors. Prior to this, the airline industry had no such analytical technique.

For several years, American Airlines enjoyed this competitive advantage – but not forever. Soon, other airlines adopted this analytical pricing methodology making yield optimization a necessity for survival instead of a competitive advantage. A similar trend happened in the hotel industry after Marriott introduced its own revenue management techniques. What first existed as a competitive advantage soon turned into a requirement for remaining relevant in the industry. This is discussed in quite a bit of deal in Thomas Davenport and Jeanne Harris’ book Competing on Analytics: The New Science of Winning – a book I highly recommend.

We believe that the banking industry will follow the same path as the airline industry and hotel industry. As banks and credit unions become more analytical, the early adopters will yield significant benefits in the form of operational efficiency, profitability increases, cost reductions, reduction in risk profile and several other benefits of analytics that we will discuss in upcoming posts. Soon after, all banks and credit unions will be required to develop their own analytical capabilities if they wish to remain relevant and stay in business. Those who refuse to adopt these techniques will be eliminated by competitors or absorbed through acquisitions by more analytically-driven financial institutions.

What does it all mean?

It comes down to three choices for banks and credit unions over the next several years:

1. Embrace analytics and adopt early. Early adoption will lead to significant competitive advantages in their operating regions. Market share, profitability, and member experience will all rise as a result of this early competitive advantage.

2. Wait until analytics seems less risky and adopt later. This late adoption will mean that the organization will not yield the significant gains seen by early adopters, but it will at least ensure their survival in a new data-driven industry.

3. Hold on tight to the old way of doing business, fail to embrace analytics, and – in all likelihood – lose market share, lose profitability, lose members, and eventually be acquired by a more analytical organization who can revitalize and re-engage your remaining membership.

Every bank and credit union can build an analytically driven organization with the right help. Over the next weeks, we will release posts that will highlight some very specific examples in which analytics can help your bank or credit union.

To learn how The Knowlton Group can help you define the proper business intelligence and analytics strategy or if you need help implementing an analytics solution, contact Brewster Knowlton at or call 860-593-7842 to learn more!