Transforming data into a strategic asset means empowering your teams to have fast access to accurate data that they can put to action.  Data and analytics are the key accelerant of an organization’s digitization and transformation efforts. Yet today, fewer than 50% of documented corporate strategies mention data and analytics as fundamental components for delivering enterprise value. 1

Organizations who want to compete in the emerging digital economy need to be strategic on how they go about analyzing and managing data. Companies know their data is a strategic asset and they want to utilize it to make smarter decisions; but the problem is it takes dedication, a cultural shift, empowered people, tight processes, and robust technology.  Those who follow through however, will be the success stories of tomorrow.

Let us uncomplicate the process.  These 5 steps will get you on a well-chartered course to turn your data into a strategic asset.

 

Step 1: Build a Data Strategy & Roadmap

Strategy without execution will fail just as execution without strategy will also fail. Moving towards a more strategic approach to capitalize on data is certainly attainable, and it starts with a Data Strategy. A Data Strategy addresses more than the data; it is a roadmap that defines PeopleProcess, and Technology. Creating your strategy begins by addressing some critical questions and filling gaps quickly.

  • What are your objectives and use cases that can turn data into an asset? Knowing your strategic business goals now (i.e. deposit growth, opening new branches, new products, digital channel alignment, etc.) and in the coming years will help your organization devise a living, breathing roadmap that aligns analytics with key business objectives.
  • How can the credit union empower employees to use the data? If you empower employees through a high-performing culture, a vision of the future, open communication and access to data and analytics assets, you can accelerate delivery, improve quality, and drive user adoption and future success with data.
  • Do you have the right processes and tools that ensure data is accessible and of high quality? Having a robust process and high-quality control disciplines that governs the overall management, usage, storage, monitoring, and protection of the credit union’s data is a critical step in the data strategy.
  • Do you have the right technology that will enable the storage, sharing and analysis of data? As data becomes an even greater asset for the credit union, the ability to store large amounts of complex data in a unified, central database, known as a data warehouse


Step 2:  Create Reliable, High-Quality Data Sets for Rapid Analysis

A vital step when transforming data into an asset is having a proper data collection system. The credit union can amass enormous volumes of data from several sources in a short period of time, but not all of that data is relevant for analysis. Start by defining the types of data that are important and which use cases will drive the greatest return. Meticulous data organization is pertinent for analysis, and it will enable you to remain in control of data quality while improving the efficiency of analysis.  Data cleansing is imperative and will help to ensure data analysis is centered around the highest quality, most current, complete, and relevant data.

If your data is clean, well-organized, and free of silos, the next step is to segment your data for a more detailed and focused analysis. Going back to step number one, define your business objectives and use cases. Consider and plan for how the data will help you achieve this goal and segment the data accordingly. You can sort data into relevant groupings to analyze trends within the various data subsets.

Step 3: Use Information as a Competitive Edge to Drive Growth


In his book, Infonomics, Doug Laney explores countless examples of how organizations can assert economic significance from data. He dives into how organizations should measure, manage, and monetize information as a real asset.   These 3 steps include:

Monetizing information starts with generating economic benefits from available assets to drive measurable business value.

Managing information is to apply asset management principles and practices to information.

Measuring Information is to practice  gauging and improving information’s economic characteristics.

As Doug puts it, “most organizations have a better inventory of their office furniture than their information assets!”  Clearly, the future of data is all about moving from volume, velocity and variety to monetize, manage and measure.  2

 

Step 4: Hire The Right Talent

Data is your organization’s second most valuable asset. The first is your people. Ensuring you have hired data savvy talent that understands how to mobilize data across the entire business ecosystem to serve customers and create valuable data products is the cornerstone of transforming data. As many data analysts and CIO roles evolve to focus less on system uptime and more on using data to drive the credit union forward, having the right data-savvy talent is key.

Data expertise must remain at the heart of a data-centric approach. Investment in core data skills is required to get maximum value out of data and technology, and to ensure that the right processes are in place to translate insight into financial gains.

 

Step 5: Build A Data-Oriented Culture


If your company’s culture does not inspire excitement about leveraging data in new ways to propel the credit union forward, then you will have a hard time achieving your business goals. Building a team who is dedicated to the organization’s success and focusing on new and innovative ways to use the data strategically will make all the difference.  To build a data-centric organization, ensure you are cultivating an organizational culture around data. Treat data as an asset and give employees tools and empowerment to make the transformation.  A few other ways to inspire a data-driven culture include:

Ensure data analysis is a key part of the leadership decision-making. Your management and leadership team will set an example that will trickle down throughout each tier of management and among employees, leading to lasting transformation.

Remove silos and make the data readily available throughout the credit union. Provide every employee access to the data, so they can perform their duties more effectively. Also be sure to educate your teams on the importance of data security, privacy and governance with proper protocols that all must follow.

View data as a key focal point of every decision and strategy. Encourage employees to routinely analyze data and then develop questions and observations from it—making it a rewarding part of their role.

Promote data literacy across the organization.  When employees have the ability to read, understand, and communicate through the use of data they are more engaged in the process and vision.

Share data successes. Celebrating the individuals and teams behind successful outcomes is essential to promoting a healthy data culture.

If you can encourage and drive this cultural shift, invest in the right people, processes and technology, there is every chance that your data will be treated as the asset it truly is—and you and your organization will be well-positioned to reap the rewards that investing and nurturing your data can bring.

What about your organization? Do you have systems in place to effectively and consistently transform data into an asset that can dramatically benefit your organization and members? If not, The Knowlton Group would like to help. Learn More, www.theknowltongroup.com

Resources:
1. https://www.gartner.com/smarterwithgartner/why-data-and-analytics-are-key-to-digital-transformation/

2. Infonomics, https://www.gartner.com/en/publications/infonomics

When used properly, data can be the fuel that propels organizations towards growth and success.  Most credit unions understand that the data that has been collected over the years can be one of their most valuable assets to improve member experiences, boost efficiency and revenue, and make faster, more accurate business decisions.

The problem we all face is having too much data that may not be properly compiled, cleansed, and organized. In other words, it isn’t information — it’s just data and raw numbers.  So, how do you remove the data clutter and get better at data analytics that transforms unorganized data into insightful, actionable information?

These tips will help you and your team transform the mountains of data into steps to improve your data analytics strategy:

Clearly Define Your Key Performance Indicators (KPIs)

Having clearly defined organizational KPIs gives your analytics efforts a guiding direction.  There are thousands of different analytic opportunities your team could explore.  But, if they don’t align with the organization’s strategic objectives (which the KPIs should directly measure), then is that effort really worth it?

If a KPI is to lower the average cost of funds, then your analytics initiatives shouldn’t focus on how to target more CD deposits but gaining greater engagement in core deposits.  This strategic-to-operational alignment needs to exist in your analytics team’s efforts to maintain clarity and focus.

Define Business Objectives

The first step in the process of turning data into information is asking questions and defining your business objectives. Data for the sake of data is meaningless. Start by outlining some clearly defined business goals, use-cases and objectives around your data analytics and analysis. Figure out the questions you would like the data to answer.

For example, how many members have obtained an auto loan or mortgage in the last 24 months but do not have an active checking account? Why are so many loan applications falling off before being approved?

To get the best results out of the data, a question (or series of questions) needs to guide your starting point.

Avoid Confirmation Bias

Many organizations use data today to add support to a belief they already hold.  When viewed from a biased perspective, it is easy to manipulate an interpretation to match a previously held belief.

Successfully embedding analytics in the decision-making process requires you to eliminate confirmation bias at the outset.  Treat each analytics use case with the scientific method approach – establish a baseline hypothesis and objectively measure whether that hypothesis is true or false.  Much organizational learning will come from this rigorous and honest approach to data.

Don’t Let Perfect Get in the Way of Good

Many organizations that are more immature in the analytics journey fixate on superficial data quality issues that arise.  Things like bad social security numbers or inconsistent addresses drive most executives crazy.  But if that data is only 90% or 95% accurate, will your decision change substantially with 100% accurate data?  The answer is almost always, no.

Strike the right balance between data quality and data integrity without chasing some ideal perfection that will never be attained.  Sometimes good enough is good enough.

Successful Analytics Programs are not Grassroots Efforts

Executive support is one of the leading reasons why some analytics succeed, and others fail.  Support for using data as decision-making tool needs to spread from the top of the org chart down.  Without this clear top-down buy-in, mid-level managers will not be required to provide data-driven justification for their recommendations.  This then proliferates further into the organization and minimizes the importance of using data.

Successful analytics initiatives requires executives to hold their teams and each other accountable for bringing data-driven discussions to the table instead of instinct-driven ideas.

 

Many credit unions are starting to see returns on their data and analytics initiatives while improving the decision-making process across the organization. But despite some standout success stories, I still witness far too many failing data initiatives. Begging the question…why do so many data analytics initiatives fail?

Consider these roadblocks to success, and how your credit union can avoid them!
1. Too big of an initial scope.
Very large goals have a downside. They can set us up for failure if we set too lofty of an initial scope to achieve, or if we don’t break the focus down and work towards each goal systematically. Start with a clear idea of exactly what you want to do with the data from a business perspective that will drive value and growth. Some common initial use cases include: attract new members, improve member onboarding, improve member engagement, reduce member attrition, core deposit growth, risk analytics and process improvement.

Analytics initiatives can quickly grow out of control since discovering value from data prompts wanting more data. Pick an initial goal and scale your efforts and focus around a refined list of clear objectives.

2. Heavy reliance on the software solution:
Industry data warehouse products and other off-the shelf solutions shouldn’t be viewed as the silver bullet that will magically solve all data issues.

For successful initiatives, data analytics needs to become a business-driven, not an IT-driven, journey. In many credit unions, the teams reviewing the technology and its capabilities may not be the same team using and deploying the technology. This leads to implementation, training, and process challenges later in the deployment process.
Technology is not the solution to business problems. Process, ownership, accountability and a defined strategy are the solution. Decisions around your analytics initiative should revolve around business goals and objectives rather than a technology or software solution.

3. Lack of internal talent:
Credit Union leaders know that if you want strategic execution, you need the right people and teams. To avoid this fixable problem, create a roadmap that gradually builds the skills, talent, and responsibilities that the credit union will need now and in the future. But before staffing up, you need to take a step back and look at what you want to do with your data, and then assign key roles and responsibilities for data collection, management, and analytics.

As the credit union grows and looks to remain competitive, there’s an obvious need to hire the right data talent who are skilled in analytics, who can interpret data, and make recommendations that offer tangible business value. In a previous article I broke down the responsibilities of data talent and key roles including: Chief Data Officer, Data Scientist, Data Analyst, ETL Developer more. Create a team that strikes the perfect balance between business and technology with the right blend of strategic thought and a tactical mindset.

4. Absence of executive support.
While data analytics has gone mainstream, the C-suite and senior leadership needs to drive the cultural changes that will empower utilization of analytics. Successful analytics initiatives require a shift in how the executive management embraces data. There are many steps you can take to foster a sustainable data-driven culture– one credit union employees will adopt and self-reinforce along the way. Becoming data-driven means that leadership should place data at the heart of the credit union.

Executive management should consistently foster the idea that the insights and opportunities born from the data can improve everything from operations and marketing to risk exposure and member loyalty. What gets measured gets done, and this can only be reinforced from the top down.

If your credit union has hit some obstacles along your analytics journey, it’s best to partner with experts who can develop a strategy and a roadmap. The Knowlton Group specializes in helping credit unions navigate through the analytics journey.

Let’s transform your organization into a data-driven credit union. If you are ready to become data-driven, send us an email or give us a call at 860-593-7842.

Data Analytics is becoming the main driver of innovation in the financial services industry. A recent report shows that data analytics investments in the banking sector totaled $20.8 billion dollars in 2017 and will certainly continue to rise as credit union executives leverage the wealth of potential that utilizing consumer data and developing successful, sustainable data strategies enables.

While more and more credit unions are realizing the value and future potential of data analytics, they are still grappling with some barriers, including: lack of in-house talent, how to appropriate their analytics budget, and formulating a data analytics strategy that sticks.

Plotting the Best Course

In my years of consulting credit unions (and work with organizations in the healthcare, retail, distribution, and government sectors) to help maximize the value of their data, one common thread I see among many is the lack of a strategy and a road map that plots the organization’s best course.  What is a data analytics strategy?

The strategy allows your organization to establish goals from the starting point to provide direction, alignment and a clear path to success. I’ve reviewed several of the key benefits of creating a strategy in a past post. Simply put, your strategy will help you create a solid plan, determine your objectives, allow you to bridge the talent gap, communicate the data analytics objectives and mission to your team, set and measure benchmarks, assess technology capabilities and data quality, and so much more.

Additionally, having a data strategy ensures that data is managed and used as an asset and not simply as a byproduct of your organization’s processes. By establishing common methods, practices, and processes to manage, use and share data across the credit union in a consistent way, a data strategy ensures that the goals and objectives to use data effectively and efficiently are aligned.

Making a Data Strategist

Often, I see credit unions attempt to formulate a data strategy internally using two approaches.  The first approach is to assign the task of establishing the data strategy to a line-of-business head. Perhaps, a Chief Lending Officer or Chief Marketing Officer – someone in a strategic position that sees the big picture for the credit union.  The second approach is to pass it off to IT or another technologist.  This individual tends to build the strategy from the ground-up – focusing on the tactical and technical challenges without necessarily knowing the full strategic picture.

The problem with the first approach – using a line-of-business strategic leader – is that these individuals rarely understand the tactical and technical aspects of day-to-day operations that must be factored into any data strategy.  They might be quite adept in their LOB (i.e. marketing, lending, retail, etc.), but they will struggle to adequately understand how the daily use of data and processes must be addressed by a data strategy.  The outcome from these individuals tends to be a very strategic and conceptual deliverable for what data analytics could do for the credit union – rarely does it address the how.

The flaws of the second approach – using a technologist or IT resource to formulate the data strategy – is that these individuals do not have strategic insight of the credit union nor the enterprise-wide perspective that is necessary.  They may know certain systems or processes inside and out – valuable, no doubt – but lack the big picture for how integration prioritization, deployment and change management planning, and other more strategic decisions need to be factored into the establishment of the strategy.  These individuals tend to place too much focus and emphasis on the technology decisions and not enough focus on other critical aspects that a data strategy entails.

What’s The Best Resource/Team to Create a Data Strategy ?

The best individual or team to establish your credit union’s data strategy is one that balances the strategic business needs of the organization with the technical and tactical requirements that must be addressed.  The best team/individual understands that the technology choices you make are no more critical than how you handle cultural change management throughout the analytics solution implementation.  Striking the perfect balance between business and technology with the right blend of strategic thought and a tactical mindset are key.

The catch? I rarely see this work using only the credit union’s internal resources.  Every vendor has a “analytics solution” now and every company that does anything in consulting or IT has a “data strategy service”.  It’s difficult for individuals within an organization who lack the deep understanding of the analytics solution and service marketplace to formulate the right data strategy that isn’t heavily dependent on sales material alone.

Unfortunately, many data strategy service offerings are led by either line-of-business strategic leaders (approach one from above) or by pure technologists (approach two).  Just like this doesn’t work within a credit union, it certainly doesn’t work with your consultants.

Find a firm that has the right resources from both the technology/tactical realm as well as those with deep strategic line-of-business expertise.  Only with that blend of skills will you yield a data strategy that can guide your credit union to both short-term and long-term analytics success.

Looking for help with developing your data strategy and plan for analytics?  The Knowlton Group is staffed by resources with both extensive technical analytics skills and decades of a line-of-business strategic leadership knowledge.  We’ve worked with many credit unions in the past by helping them understand the full impact of data analytics to their business model, define a compelling data analytics strategy and ultimately provide results. From strategy, to conceptualization to full implementation, we are ready to make your credit union a data-driven organization. Contact us today.

Digital innovation is sweeping across the financial services industry and creating opportunities for banks and credit unions to leverage data as a source of competitive advantage.

Until recently, most credit unions were delegating data management and analytics to the IT department, which in turn created data silos that inhibited the enterprise use of data.

Has your credit union made the business case for creating an analytics team to spearhead important data initiatives? If so, you now need to hire or train the right talent that can turn data into value and deliver on your organization’s data strategy

Chances are you have numerous questions whirling around about how to define the key data roles and responsibilities.  When venturing outside the credit union to evaluate data leadership, this list of tips breaks down key roles and how they should align with your needs.

Chief Data Officer

The CDO is a senior executive who bears responsibility for the credit union’s enterprise data and analytics strategy, data governance, data management, and data utilization.  The CDO’s role will combine accountability and responsibility for information protection and privacy, information governance, data quality and data life cycle management, along with using member data to create business value.

This last point is arguably the most crucial.  If your analytics team is not delivering business value then you’re not achieving the team’s full potential.  The CDO should focus on measurable outcomes for specific use cases to provide the necessary cultural and change management sparks to garner enterprise-wide buy-in.

Data Scientist

A data scientist masters a whole range of skills and tasks from being able to handle the raw data and analyzing that data with the help of statistical techniques, to delivering actionable recommendations based on the underlying data.

The title “Data Scientist” has become a bit of a buzzword as of late.  If your “Data Scientist” can query a database, but the extent of the statistical knowledge is mean, median, and mode…they aren’t a data scientist.

Real” data scientists have deep knowledge of statistical and probabilistic models and know how to leverage those models for specific analytic applications.

The Data Analyst

The data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions.  The insights that data analysts bring to the credit union can be valuable in identifying and even helping to predict the needs of the credit union’s members.  They help develop effective ways to collect the data and compile key findings into reports to share with other teams within the credit union.

Think of the data analyst as the individual who translates between the technical world and the business world.  This individual needs to have basic competencies from a technical perspective, but, most importantly, they need to be able to interpret technical knowledge into practical business terms and vice-a-versa.

A good data analyst doesn’t just produce charts, graphs, and other fancy visualizations.  They produce clearly articulated meaning to describe what the visualizations mean to the business.

ETL Developer/Data Engineer

The ETL Developer/Data Engineer is a critical member of the data analytics team as they are dedicated to the fundamental process of capturing, storing and processing your data.  If your CU leverages a data warehouse as your analytics platform, then the “ETL Developer” most aptly describes the job title.  If your organization is leveraging a data lake or hybrid platform, “Data Engineer” is a more appropriate title.

In the end, this role boils down to ingesting new data sources into the platform.  This may come from non-core third-party applications (i.e. consumer LOS, real estate LOS, online banking, etc.) to external data sources (i.e. demographic data, economic indicators, social media interactions, etc.).

Report/Visualization Developer

To effectively deploy self-service reporting and analytics through your BI portal (i.e. Tableau, Power BI, Information Builders, etc.), someone must be tasked with creating these reports and dashboards. This is the critical role of the Report/Visualization Developer.

If your credit union embraces a more decentralized approach to data analytics, then these resources may reside in the business areas instead of centrally managed.  Regardless of where they reside within the organization, this is an essential function for providing a front-end to your analytics platform.

The Right Role for Your Credit Union?

As credit unions grow and look to remain competitive, there’s an obvious need to hire the right data talent who are highly skilled in analytics, who can interpret data, and insight and tangible business value. Demand for data expertise is growing every day. Be sure to understand which roles are specifically needed by your organization.  Most credit unions don’t have the necessary budget to hire each of the resources discussed.  Determine where the greatest internal need exists and identify strategic partners who can assist with the rest of the functions.

The bottom-line, all organizations have the power to become data-driven by accessing data skills – and on almost any budget.  Ready to formulate a winning data analytics strategy?  Contact The Knowlton Group to get started.

Sources:

  1. Gartner Chief Data Officer Survey

 

It’s a harsh reality.  Many U.S. consumers are struggling financially.  According to the Federal Reserve Board, four in ten Americans can’t cover a $400 emergency expense.  As consumer debt rises and savings rates decline, credit unions are facing a growing issue and opportunity: how to quickly address financial wellness among members.

Consider these staggering stats: (2)

  • 70% of Americans live paycheck to paycheck
  • 24% of pay is spent on non-mortgage debt such as credit cards
  • 42% are living with no retirement savings

Data & Financial Health
Managing finances is one of the greatest areas of concern for many Americans. Fortunately, there is a significant opportunity for credit unions to develop analytics-driven programs that can spot those at-risk members and help provide solutions through financial education and special arrangements.  The first step begins with the credit union analyzing member data to recognize those members encountering financial hardships and then provide temporary assistance to help.

Here are some ways credit unions can maximize data analytics to guide members to better financial health:

1. Create data-driven profiles and member segmentation. In order to best determine who each individual member is, what their needs are, and how to best engage them, leveraging and analyzing member data is the first step. A combination of demographic data along with financial behavior-driven patterns that can be segmented from financial account and transaction data (like income, spending patterns, direct deposit data, and loan debt) will ultimately be the most helpful when segmenting members and compiling profiles. By profiling high-risk members, the credit union can know who specifically needs financial education tools and guidance.

2. Tracking FICO Score Migration. FICO Scores can reveal much about members and borrowers financial wellness. Many credit unions do not track the migration of credit scores for a given individual.  By saving each members’ FICO score with a date stamp, the credit union can extrapolate a potential financial wellness concern.  Factors that are considered in making FICO scores are changing. There is more emphasis on trends in someone’s credit history than before. Now consumers will be rewarded for making larger payments and getting rid of debt, but those who are accumulating more debt will be penalized (even if they are making the minimum payments).

3. Spot and track behavioral changes. Changes in deposit and ACH patterns are another tell-tale sign of a change in individual’s cash flow. Whether faced with unemployment, or a business is struggling and on the verge of bankruptcy, these deposit trends and spending changes can be analyzed through your CU’s analytics program. If the trend persists, this is an opportunity for the credit union to reach out and offer financial wellness tools and support before the situation becomes more dire.

4. Understand members’ debt-to-income ratio. According to Smart Assets, any debt to income ratio higher than 43% suggests a risky borrower or someone in need of financial wellness education. For those members who use the credit union as their primary checking account, the CU has an opportunity to monitor ACH payroll income, utility, cell phone, loan payments and other expenditures. Analyzing members’ debt-to-income ratio the credit union can determine how well certain members manage monthly debts and who could potentially struggle to repay loans.

Using Data for Good

Thanks to today’s data analytics and AI capabilities, credit unions not only have the potential to get an instant snapshot of members’ financial health, but they can offer personalized advice or guidance on how specific members can improve their spending and savings.

The credit union can then create special arrangements to help those at-risk members such as:

  • Postponed payments
  • Loan restructuring or change in interest rate
  • Temporary overdrafts or line of credit
  • Debt consolidation
  • Access to term deposit accounts
  • Refinance options

Make it a goal to explore members’ attitudes toward financial matters, determining their awareness of financial management, and their willingness to use financial wellness tools.  This creates a tremendous opportunity for the credit union to become a trusted advisor and to create services centered on helping members achieve financial peace.  As member-owned community cooperatives, credit unions are perfectly positioned to use analytics for good and improve the financial wellness of all their members.

If your credit union needs assistance on utilizing data analytics to help those members in need of financial wellness support, contact the Knowlton Group.

Sources:

1. CNN Money: 40% of Americans can’t cover a $400 emergency expense

2. Top Money Statistics 

3.  Fortune: Why Your Credit Score Could Soon Go Up

4. Smart Asset

For years, financial institutions have enjoyed the abundance of low-cost deposits. Today, we are in a new era of banking, with deposit acquisition becoming a significant point of focus for most credit unions.

The change represents another consequence of the Federal Reserve’s decision to raise short-term rates which influences the mortgage market, stocks and other corners of the economy. (1) Higher-rates available in money-market funds and other investments are luring consumers to move their money out of minimal interest-bearing accounts.

Adding to the challenge, the competition for acquiring new deposits is coming from not only big banks but from new digital players. In a rising rate environment coupled with a healthy economy, achieving deposit growth goals is one of the best ways to control funding costs while meeting the lending needs of members. Could data analytics be the game-changer to best understand member behavior and motivations to attract, win, and keep new deposits?

Here’s how you can maximize your member data for greater deposit growth:

Segment Your Existing Members

Member segmentation allows the credit union to divide specific target markets and member commonalities into more specific groups. By tracking and measuring important indicators in a member’s life – age, gender, marital status, income, a move, and more – you can segment members into easily targeted groups. This is important for optimizing marketing spend, effective reach and for acquiring deposits. Segmenting your members will help the credit union target the right member for the right deposit programs. Using data analytics, you can successfully segment your members’ data to create personalized, compelling marketing messages that are relevant and allow you to cater to your member’s needs—leading to potential deposit acquisition opportunities.

Create Member Acquisition Programs

Consumers have tons of options when it comes to financial services. Attracting their business requires smart, personalized marketing. Do you know which of your products consumers are more likely to use, or the segments which represent the strongest growth potential for your credit union? Through data analytics you can target the high-growth, high-opportunity member segments, create more relevant messages and product offers and promote your deposit services to the right prospective member, with the right message, at the right time.

Maximize Cross-sell Opportunities

I’ve mentioned before, the cost to acquire new members is ten times more than cross-selling/up-selling to existing members . Cross-selling deposit accounts to existing members is one of the best ways to grow core deposits. Through analytics you can gain a deep understanding of your existing members wants, needs, products and service offers and execute effective cross-selling initiatives. Data analytics helps you determine current product penetration and propensity by member as well as any gaps in products within your existing member-base. An analysis of existing member behavior can lead to efficient cross-sell of products. These efforts can drive an increase in member engagement while ensuring your credit union is maximizing all opportunities to promote high-interest yielding deposit accounts to your existing members.

Execute Retention Strategies to Keep Members

Controlling attrition is a top priority for credit unions. A change in address, marital status, or even a change in job status can lead to a member switching financial institutions. By using data analytics, credit unions can take proactive measures to understand and analyze the factors which might trigger thoughts of attrition in members. Using member data, credit unions can dramatically improve their ability to anticipate member behavior and key life events at an individualized basis. Data analytics is helping credit unions gain greater insights into their member’ needs, preferences and likely behaviors that lead to attrition. This valuable information enables the development of member loyalty strategies that strengthen the credit union’s ability to retain the members that are costly to attain.

Fine-Tune Marketing Messages

With the amount of the data now available, credit unions can create individual messaging to members and prospective members. This is a crucial factor for success in a time when deposit growth is critical. Analytics can empower the credit union to access extremely granular and detailed information on each member and allow marketing departments to send promotions on deposit services to each member segment based upon their specific needs and financial objectives. Emphasizing this personalized approach to marketing will allow members to feel a more personal connection with your credit union.

Deposits fuel revenue and the lending operations of the credit union. Without deposit growth, credit unions could face the challenge of reining in lending or pursuing more expensive funding.

Those credit unions that recognize the strategic importance to focus on deposit growth through data analytics will be the most successful in this competitive environment. Is your credit union looking for support to help maximize your member data? The Knowlton Group can help you devise a plan to put your member data to work.

1. The Biggest Banks Are Gobbling Up Deposits. Here’s Who’s Not. Wall Street Journal April 2108

If you have reviewed the some of the important steps and hurdles to overcome for credit unions to improve their analytics maturity outlined in our first article on Why the Lag, then you are ready for some more steps in the process.

Challenge:

 Maintaining data quality is a hurdle for many credit unions, but it is a critical component to becoming data-driven. To achieve consistent and reliable member data, credit unions must constantly manage data quality at the source so that they can trust and use the data to enable quicker and more knowledgeable decision-making.  As the saying goes, “garbage in, garbage out” so the importance of clean data can’t be understated.

Solution:

Step one is to know what data you’re collecting, why you’re collecting it and where it comes from.  Make sure that every component is coming from a trusted and knowledgeable source. Validate data as it is entered by automatically flagging missing, incorrect, and/or inconsistent information. Whenever possible, eliminate the opportunity for free-text fields and opt for drop-downs instead.

If you discover problems with incoming data, go all the way back to the original source to make corrections.  The data warehouse or analytics platform is not the place to make those data quality corrections.  Otherwise, you will constantly be correcting for inaccuracies.  Use the data warehouse to identify issues, and then make the corrections at the source.

Challenge:

Lack of leadership buy-in is another challenge we see for those credit unions failing to successfully implement a data strategy. For any new initiative to work well, all departments within they credit union need to communicate, work together and see the payoff of becoming data-driven.  Buy-in will require fortitude and integration into the strategic plan, culture and budget.  This is where analytics becomes as much of a change management problem as it is a technical one.

 Solution:

 To gain support and financial approval for your data analytics initiatives, you need to give senior managers a snapshot of how these efforts can pay-off.  Be sure to provide the “why” the credit union should invest in data analytics and the multitude of ways the data will improve efficiency, member engagement, marketing effectiveness and more. Be transparent and encourage team members to want to be a part of this transformation with concrete examples of how it will improve the “whys” for your credit union (time savings, member service increase, cost reduction, etc.)  Show evidence and examples of how the competition is using data to grow and increase market share.

 Challenge:

 A lack of analytics talent is a major obstacle faced by credit unions desiring to be data-driven. Hiring, training and managing highly skilled, knowledgeable, data-savvy personnel is costly. Given the explosive growth on the job posting sites for those with analytics expertise and the intensifying competition to fill more jobs than there are qualified people, it is difficult to attract and retain the right talent.

Solution:

An effective data analytics talent effort should consider not just compensation but also cultural fit. Striking this balance is critical to set both the data scientist/ data analytics hire and the credit union up for long-term success. Also, consider if anyone in house has the foundational skills necessary to build upon.  Your “Excel gurus” could very well be trained to become your organization’s modern analytics expert.

Millennials, particularly, find it appealing to work with organizations with a strong social and community conscience.  Credit unions inherent operating model – from their community focus to their charitable presence – are well-positioned to offer job applicants the right cultural fit.

Still not sure if your internal team has the right skills?  Consider working with outsourced firms that can augment your internal data efforts.

Is your credit union making the most of member data? If not, what is holding you back?  At The Knowlton Group, we believe that every organization – no matter their size – can become data-driven. The best data and analytics program starts with a great strategy and clearly defined roadmap and implementation plan. Our personalized approach to each engagement ensures that the specific needs and goals of your credit union are captured for maximum results. Want to know how you can further improve your members’ experiences? Let’s talk. Contact me today at brewster@knowlton-group.com or call 860-593-7842.