Driver-Based Financial Analysis: Turning the Levers That Actually Move the Business

Robert Majdak Sr. M.B.A. and Crystal Majdak M.S.A.

If you’ve ever stared at a spreadsheet and thought, “Cool numbers… but what do I actually do with this?”—you’re not alone. That frustration is exactly why Driver-Based Financial Analysis matters, especially for new career professionals who want insight, not just information.

At its core, driver-based analysis focuses on the few variables that truly drive outcomes. Instead of obsessing over hundreds of line items, you identify the levers that matter most—and model how changes to those levers impact results. It’s less about bookkeeping and more about strategy.

Let’s ground this in a real-world-style scenario.

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The Scenario: A Growing Subscription Fitness App

Imagine you’re a financial analyst at a subscription-based fitness app company. The app targets busy professionals with on-demand workouts and wellness content. Leadership has one big question going into next year:

 “How do we grow profitably without burning cash?”

You could respond with a 40-tab budget. Or—you could lead with driver-based analysis.

 You start by identifying the core business drivers:

1. Active Subscribers
2. Monthly Subscription Price
3. Customer Acquisition Cost (CAC)
4. Monthly Churn Rate
5. Average Content Cost per User

Everything else—revenue, expenses, cash flow - flows from these five drivers.

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Step 1: Build the Revenue Engine

 Revenue isn’t magic. It’s math.

Revenue = Active Subscribers × Monthly Price

 Instead of forecasting revenue directly, you model subscriber growth:

• Starting subscribers: 120,000
• New subscribers per month (driven by marketing spend and CAC)
• Subscribers lost each month (churn rate)

 Suddenly, revenue becomes a story:

• A 1% reduction in churn keeps thousands of users longer
• A $2 price increase has more impact than a massive ad campaign

 That’s insight executives can act on.

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Step 2: Tie Costs to Behavior, Not Assumptions

 Next, you map expenses to what actually causes them.

• Marketing costs are driven by CAC × new subscribers
• Content costs scale with active users
• Support costs increase as the user base grows

 Now, instead of saying “marketing is up 12%,” you can say:

“Marketing spend increased because we chose to accelerate subscriber growth by 15%.”

 That’s a strategic choice—not a surprise expense.

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Step 3: Stress-Test the Drivers

Here’s where driver-based analysis really shines.

 You run scenarios:

• What if churn drops from 4% to 3%?
• What if CAC rises due to platform ad costs?
• What if we bundle premium content and raise prices?

 In one scenario, you discover that:

• Reducing churn by just 1 percentage point generates more profit than acquiring 20,000 new users.

 That insight reshapes priorities. Product and customer experience suddenly matter more than ad spend—and the numbers prove it.

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Why This Resonates with Business Professionals

Driver-based analysis aligns with how professionals think:

• Systems over silos
• Impact over activity
• Clarity over complexity

 It also earns you credibility fast. When you can explain financial outcomes in plain language—“This metric moved because that behavior changed”—you stop being “the numbers person” and start being a strategic partner.

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The Big Takeaway

Driver-based financial analysis isn’t about predicting the future perfectly. It’s about understanding what actually moves the business so leaders can make smarter decisions, faster.

If you want to stand out as a finance or analytics professional, don’t just report results.
Identify the drivers. Model the trade-offs. Tell the story.

That’s how finance becomes strategy—and how you become indispensable.

Think Strategic: Dashboard-Driven Insight and Storytelling using a Car Sales Scenario

Robert Majdak Sr. M.B.A.

In 2025, U.S. automotive markets experienced a complex blend of consumer preferences, economic forces, and evolving powertrain demand. While electrified vehicles continue gaining traction, combustion-engine passenger cars remained the majority of new retail sales according to industry forecasts, with internal combustion engine vehicles accounting for more than three-quarters of retail unit sales in major months (August & September) of 2025. (J.D. Power)

For analysts tasked with interpreting this landscape, dashboard-driven insight and storytelling became essential. Rather than presenting dry tables or static reports, analysts used dynamic dashboards to bring patterns to life, showing where combustion vehicles sold most and what types buyers preferred.

Regional Patterns in Combustion Sales

The first dashboard view reveals combustion passenger vehicle sales across U.S. regions. The South led sales by a wide margin, reflecting warmer climates, longer driving distances, and strong pickup and SUV demand. The West followed, while the Midwest and Northeast showed smaller shares.

This visual surfaced a simple but powerful insight: although total U.S. retail volumes grew modestly in 2025, major regional differences could inform production, marketing, and inventory decisions. The South’s dominance suggested continued demand for larger vehicles (often combustion), while lower volumes in the Northeast might point to stronger adoption of alternative powertrains in urban centers—a story the dashboard makes immediately visible.

Segment Preferences Among Combustion Buyers

A second dashboard highlights sales by vehicle segment among combustion passenger cars. SUVs dominated the segment mix, followed by midsize sedans, compact cars and pickups. This ordering aligns with broader market trends, where lifestyle preferences and perceived utility increasingly influence buying decisions.

From a storytelling perspective, this visualization does more than show quantities. It helps analysts explain why the South sells more combustion vehicles: that region’s consumer base tends to favor rugged vehicles like SUVs and to  a somewhat lessor degree, pickups. Because dashboards layer visuals with context, analysts can narrate this pattern back to leadership quickly and with clarity.

From Data to Decisions

Dashboard-driven insight and storytelling bridge the gap between raw data and strategic understanding. For 2025 combustion vehicle sales, dashboards revealed not only performance figures but also the narrative threads that explain how different regions and segments shaped market outcomes. In doing so, analysts moved beyond reporting history to guiding future decisions—informing where to allocate inventory, which advertising messages resonate regionally, and how to balance combustion and electrified portfolios in 2026.

In an era rich with data but poor in actionable context, dashboards become the storyteller’s stage. When analysts think visually and narratively, they turn complex data into clear direction.

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References (APA)

J.D. Power. (2025, December). GlobalData forecast: U.S. automotive sales 2025. J.D. Power. (J.D. Power)

J.D. Power. (2025, September). GlobalData automotive forecast: internal combustion engine share trends. Business Wire. (businesswire.com)

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Start Strong, Think Strategic: Analytical Strategies for New Financial Analysts

 

Robert Majdak Sr. M.B.A.

How modern analysts turn data into decisions in a fast-moving, AI-assisted finance landscape.

Entering the financial analysis profession in 2026 requires more than technical competence. New analysts are expected to interpret complex data, communicate insight clearly, and support decision-making in environments shaped by automation, artificial intelligence, and continuous change. While tools evolve rapidly, the strategies that separate effective analysts from average ones remain grounded in disciplined thinking. Three analytical strategies dominate modern practice and provide a strong foundation for analysts early in their careers.

1. Driver-Based Financial Analysis

Driver-based analysis focuses on identifying and modeling the variables that truly influence financial outcomes. Rather than relying on static historical comparisons, analysts isolate operational, market, and behavioral drivers—such as pricing elasticity, customer churn, labor efficiency, or supply chain volatility—and link them directly to revenue, cost, and cash flow outcomes.

This strategy is popular because it supports agility. When assumptions change, models update quickly, allowing leaders to understand why results are shifting, not just what changed. For new analysts, mastering driver-based thinking builds credibility early. It demonstrates an ability to connect financial results to business reality, a skill that senior stakeholders consistently value.

2. Scenario and Sensitivity Modeling

Modern financial decisions are rarely made under certainty. Scenario analysis allows analysts to test multiple future states—optimistic, baseline, and downside—while sensitivity modeling evaluates how outcomes respond to changes in specific inputs. Together, these techniques help organizations manage risk proactively.

In 2026, scenario modeling is deeply integrated with forecasting platforms and planning software, enabling near real-time iteration. New analysts who adopt this strategy quickly learn to think probabilistically rather than deterministically. This shift improves judgment, sharpens communication, and aligns financial insight with strategic planning. Importantly, it trains analysts to frame uncertainty as manageable rather than threatening.

3. Dashboard-Driven Insight and Storytelling

The most technically accurate analysis has limited value if it is not understood. Dashboard-driven analysis combines visualization, key performance indicators, and narrative context to translate data into insight. Modern dashboards emphasize clarity, trend recognition, and decision relevance rather than data density.

This strategy is widely used because it supports faster, better decisions across organizations. For new analysts, dashboard literacy accelerates influence. By learning how to design clean visuals, highlight exceptions, and tell a coherent financial story, analysts move from reporting numbers to shaping conversations. In a workplace saturated with data, the ability to curate insight is a competitive advantage.

Thinking Strategically from Day One

These three strategies—driver-based analysis, scenario modeling, and dashboard storytelling—share a common purpose: they elevate analysis from mechanical reporting to strategic contribution. New financial analysts who adopt them early build confidence, trust, and professional momentum. In a modern finance environment defined by speed and complexity, starting strong is less about knowing every answer and more about asking the right questions with structured, strategic intent.

Start Strong, Think Strategic: A 2025 Guide for New Financial Analysts

 

Why mastering the right software features early accelerates your career

In today’s data-driven workplace, financial analysts must be both accurate and insightful. For those just starting out, the right financial software tools can make the learning curve rewarding rather than overwhelming. Drawing from popular platforms such as QuickBooks and Microsoft Dynamics GP (Great Plains), here’s a strategic roadmap for building early wins in your role as a financial analyst.

1. Start with Clear Dashboards and Reporting
QuickBooks and similar platforms offer dashboards that place key metrics—revenue, expenses, cash flow—front and center. These visual summaries enable new analysts to understand business health quickly, without needing to dig through raw journals. Customizable financial reports allow users to tailor views to specific questions, helping you communicate insights early and confidently.

2. Leverage Automation for Accuracy
Routine tasks like transaction categorization and bank reconciliations can eat time and invite errors. QuickBooks workflow automation and Great Plains’ automated payables/receivables processes reduce manual work, freeing you to focus on analysis and interpretation rather than data entry. Early mastery of these efficiencies signals professionalism and increases team trust.

3. Build Confidence with Basic Financial Controls
Understanding internal controls—such as role-based access, version histories, and real-time reconciliation tools—gives you a solid foundation in how financial data stays reliable. These features aren’t just compliance checks; they help you verify your numbers independently, a key skill for analysts delivering accurate forecasts and recommendations.

4. Explore Cash Flow and Forecasting Tools
While core accounting works with historical data, forward-looking analysis focuses on projections. Many modern ERP systems include cash flow and forecasting modules. Even basic forecasting lets you see patterns and trend lines early in your career, strengthening your ability to anticipate needs and support strategic planning.

5. Practice Budgeting and Variance Analysis
Budgeting features, even at a basic level, provide context for what should happen versus what did happen. Comparing actual to budget and interpreting variance builds analytical muscles you’ll use throughout your career. QuickBooks includes budgeting tools that allow you to create and track financial goals and variances within the platform.

6. Integrate with Familiar Tools
Both QuickBooks and Dynamics GP support integration with software like Excel or BI platforms. These connections let you export or link data into tools you may already know well, helping you extend insights beyond the accounting system itself.

By focusing first on dashboards, automation, controls, forecasting, budgeting, and integrations, new financial analysts can produce visible contributions early in their roles. These aren’t just software features—they are confidence builders that transform raw data into strategic insight. Embrace them intentionally, and you’ll accelerate both your learning and your impact in 2026 and beyond.

Have an Insightful day!

Robert Majdak, Co-Founder

Crystal Majdak, Co-Founder

Management Insights Group

Building Belonging at Work: How Mentorship Shapes a Human-Centered Corporate Culture

 

In 2026, corporate culture is no longer a “soft” concept—it is a core driver of attraction, retention, and performance. Mentoring a corporate culture means intentionally shaping an organization that people actively want to join, contribute to, and grow within. Employees are not merely hired for tasks; they are invited into a shared purpose that is larger than any single role. When this happens, motivation becomes intrinsic, and work becomes meaningful.

At the foundation of this culture is a clearly articulated code of conduct that employees willingly adopt as their own. This code is not enforced through fear or compliance, but through shared values and a sense of fit. Fit, importantly, is about belonging, not control. When employees feel aligned with organizational values, they naturally help new colleagues integrate and succeed.

An effective code of conduct should be simple, human-centered, and actionable. Three expectations are often enough:

a. Treat colleagues with the respect you expect for yourself.

b. Treat customers the way you would want to be treated when seeking help.

c. Consistently aim to exceed customer expectations.

These principles resonate because they reflect everyday social norms and recognize a basic truth—organizations exist because customers choose them.

Likewise, customers are best served when employees:

a. Collaborate.

b. Trust one another.

c. Contribute their fair share.

From this shared baseline, a broader corporate culture can grow. Senior leaders must translate their vision into clear, realistic, and measurable goals. Transparency matters: goals should be visible, explained, and connected to daily work. Mentorship becomes the engine of alignment. Executives mentor managers by clarifying priorities and providing resources. Managers, in turn, mentor their teams by showing how individual responsibilities support the larger strategy.

Accountability must exist at every level, from the CEO to frontline employees. However, modern accountability emphasizes learning over blame. When outcomes fall short, the focus should be on identifying problems and implementing solutions, not assigning fault. This approach builds psychological safety, encourages innovation, and strengthens commitment.

Ultimately, mentoring a corporate culture is about sustaining customer satisfaction through people who feel valued, trusted, and engaged. A company with a “heart,” as Glasbergen’s classic illustration suggests, is not sentimental—it is strategically wise. Organizations that invest in belonging, clarity, and mentorship are better positioned to manage complexity, adapt to change, and grow with purpose in a human-centered economy.

Have an Insightful day!

Robert Majdak, Co-Founder

Crystal Majdak, Co-Founder

Management Insights Group

SQL - Foreign Keys

Removing Obsolete Data

            One of my clients recently discussed their irritation at deleting obsolete records in parent tables that had many child table relationships. They had developed an extensive cleanup process to back-into their table relationships, deleting records from the child tables first then moving upwards in through the table relationships by following their database design charts. While I certainly understand that and similar methods, I think we can improve upon the process by designing databases more efficiently and changing some table designs. One thing I am going to cover here is the result of designing and altering tables to deter the initial problem. We know that due to referential integrity the parent-child relationship will be maintained by the database. So let us change the rules a little. We are going to discuss how to use ON DELETE CASCADE upon our FOREIGN KEYS and alleviate the problem of cleaning up our database of obsolete data connected with parent-child relationships.

Foreign Keys

The idea behind creating foreign keys is to develop relationships that connect tables together. Therefore, the first table (parent) has a column that references a column in another table (child). The column in the first table can also reference another column within itself (self-reference). The most common foreign key relationship we usually think of is the former one (parent-child).

Figure 1  Parent-Child relationship

Between store.employees2.DIVISION_ID and store.divisions.DIVISION_ID

           In Figure 1, we can see that the employee table has a Foreign Key column named EMPLOYEES2_FK_DIVISIONS connecting it to the division table Primary Key DIVISION_ID column.  In this way, these two tables are linked and related.

Figure 3 DIVISION table

We also see that listed in Figure 2, that the EMPLOYEE table has a foreign key named “EMPLOYEES_FK_DIVISIONS” in the R_CONSTRAINT_NAME column and the associated constraint named “DIVISIONS_PK” which obviously matches the DIVISIONS_PK constraint in the Figure 3 where the DIVISIONS table has it listed as the DIVISIONS table’s Primary_Key. Figures 4 & 5 illustrate the CREATE TABLE syntax under Oracle 11g used to create these tables.

Figure 4 SQL to create the table EMPLOYEES2

Figure 5 SQL to create the table DIVISIONS

 Deleting the Child Relationships Through the Parent Connection

A useful purpose that can be extended through the use of foreign keys is in the maintenance and cleanup of old data, the very point of this article. By using the ON DELETE CASCADE clause you can specify that through the FOREIGN KEY, that is, a Parent table which is having a row deleted, can act as the catalyst to allow any matching rows to be deleted in associated Child tables. After identifying these table relationships in your database design, you could write the following ALTER TABLE syntax (Figure 6) while you are in the creation mode to prepare for future maintenance ahead of time.

Figure 6 ON DELETE CASCADE

            By logically choosing the correct data flow, you can prepare your design to remove all associated records, that is child records, when a parent record is deleted. Consider a company that closes Division X after moving some employees to Division Y. When Division X is finally removed from the database, its Primary Key will drop and all associated Foreign Key relationships should drop as well. This is because the employee records still remaining and associated with Division X, drop as a result of the ON DELETE CASCADE.

            Play with this concept yourself in some test records that simulate your actual experience to get the concept clear in your mind. Then develop your new database with this concept in mind. Moreover, look at your current database and plan some table upgrades into your next iteration of software upgrades where this method of maintenance will streamline your database maintenance operations going forward.

Video Presentation

Have an Insightful day!

Robert Majdak, Co-Founder

Crystal Majdak, Co-Founder

Management Insights

SQL - Savepoints

Overview

Savepoints are a very handy feature in Oracle especially during development or data manipulation. The basic idea is that as a set of transactions are being accomplished; you can create a point in time to return the data without having to completely redo all work done. That is a tremendously valuable feature, let’s say you moved through 6 of 10 steps into your development process and decided you needed to only back track 1 step. With an intermittent savepoint in place, you can go backwards to that point into your development instead of rolling back all of your work in that session saving you valuable redevelopment time.

Here is How it Works

            I am going to setup some holiday pay bonuses now while I have plenty of time to consider how they hit our budget. For this discussion, I am going to limit the increases to department #80. The bonus is a function of a percentage of the employee’s salary and commission with the commission being used to decide the bonus multiple. The script used to populate the initial bonus data in the rec_merit_incr field of the employee table is shown in Figure 1.

Figure 1. Initial bonus data calculations.

            The commission breakpoint is .25 which decides whether an employee receives the .1 or the .075 bonus calculation. I would like to consider bumping the .075 up to .095 to get an updated sum for the holiday budget. Up to this point I have created new columns and populated data in support of the project. For the work so far set in place, I will create a SAVEPOINT named merit1 as shown in Figure 2. I name it in a way that another colleague will not likely create a similar SAVEPOINT name during this development session. Then I run the SAVEPOINT command.

Figure 2. Created SAVEPOINT.

      I will now continue the work by updating the calculation to .095 and running the SQL script for that subset of employees under consideration. You can easily see how employees like Christopher have a new merit increase test value. In aggregate, I decide that the increase for the entire employee subset is not yet budget supported so I return the data back to the original values in the system when SAVEPOINT merit1 was created.

Figure 3. Updating script and subsequent output.

Rolling back the data to the SAVEPOINT

            All I need to do to return the data to the pre-merit1 SAVEPOINT values is to enter the command into the system to rollback the data up to the merit1 SAVEPOINT. The system will then respond with "Rollback complete". See Figure 4.

Figure 4. Rolling back the data to the specified SAVEPOINT.

            We can then verify the data is in the correct state by verifying it using the script I used earlier before the .075 was changed to .095.

Figure 5. Data after the "ROLLBACK TO SAVEPOINT merit1" command.

Keep in mind that another scenario we could play out is to reduce other employees with higher bonus values or blend the subset with a budget compromising overall average. We might also consider budget-rebalancing ideas. Whatever the approach to be taken, we can use the SAVEPOINT feature in any transaction analysis to lockstep our way backwards as redevelopment dictates.

Video Highlights

Have an insightful day!
Robert Majdak, Co-Founder
Crystal Majdak, Co-Founder
Management Insights Team