Financial Analytics: What It is, Why It’s important, and What Businesses Can Learn From It

Eighty-six percent of senior finance leaders admit they don’t get much value from financial analytics. The problem is that these tools are supposed to improve your forecasting and make it easier to answer strategic business questions. But instead, they often only generate backward-looking reports on financial performance, basic visualizations, and reactive ad hoc analyses.

To get real value out of financial analytics, you don’t just need a financial dashboard for simple reporting — you need something that can turn budget variance analysis into strategic insights. With actionable insight into how your numbers change month to month—and, more importantly, why they change—you can proactively answer the toughest strategic business questions (like the four listed here).

What Is Financial Analytics?

Financial analytics, which can also be referred to as financial analysis, is the process of aggregating financial data and turning it into strategic insights about a business. Compared to more cyclical processes like the month-end close, financial analysis is an ad hoc tool for answering difficult questions about your business.

Financial analytics applies to both historical data and financial planning scenarios. Different types of financial analysis will help you look at financial statements and operational data to spot trends that inform assumptions in your models. Others will help you forecast future performance as you pull different levers and see how they impact the business.

Why Finance Leaders Struggle With Financial Analytics

A study from FSN found that although finance professionals see financial analytics as a core pillar of the function’s future, 86% struggle to find value in it. 

Your finance function sits at the intersection of all data in the organization, so it may come as a surprise that so many leaders struggle to analyze it all. But the survey results become less surprising when you think about how many hurdles there are to effective financial analysis:

• Manual processes force you to spend the vast majority of your time collecting data, leaving little time to analyze it deeply.
• Growth-driving financial analysis requires deep collaboration with business partners to understand which questions to focus on, but speaking the same language as department leaders can be challenging.
• While spreadsheets are effective at analyzing financial data, the process of building out models and aggregating data can take so long that your insights are stale by the time you deliver them. 

The finance teams that unlock the true value of financial analytics have a strong foundation of real-time data and agile tools.

Why Financial Analytics Is Important for Businesses & How To Do It Right

Financial analytics is important as it aids in assessing financial risk and developing data-driven answers to key strategic business questions. Instead of having executives and departmental leaders make decisions based on gut feelings, financial analytics makes business data more approachable and helps all key stakeholders understand the “why” behind the numbers.

The best finance teams proactively surface strategic insights that drive growth and efficiency in their organizations. But for the rest, manual processes and disparate data sets relegate financial analytics to a backward-looking exercise that lags behind business needs.

There are near-infinite use cases for financial analytics. Still, the following four examples of strategic questions about a business should give you an idea of how valuable proactive analysis can be.

1. Shorten Your Sales Cycle to Maximize Profitability

Financial analytics can give you insights into the speed and throughput of your pipeline. Those insights help you identify specific opportunities to accelerate the sales cycle.

The right financial analytics system doesn’t just show you that the opportunities in your pipeline decreased compared to the previous week—it helps you understand the “why” behind that variance in your sales cycle. Without financial analytics, you’d have to rely on reporting in a CRM like Salesforce or HubSpot for insights into customer statuses, which tells you part of the story but not the whole picture.

These systems do a good job of telling you what the pipeline looks like at any point. But they don’t give you the historical context that reveals why the numbers are changing. If you want to uncover opportunities to shorten the average sales cycle, you need to use financial analytics to study changes in critical sales pipeline metrics over the course of a week or month.

Time series data for your pipeline can help you understand when:

• You’re spending too much time giving demos to unqualified leads.
• There’s too much lag between when a prospect submits a demo request and when a rep sets the meeting.
• You need to realign headcount planning and revenue goals with your sales rep ramp.

The right financial analytics setup can help you identify issues like these that might otherwise fly under the radar. And once you do, you can collaborate with your VP of sales to come up with solutions that will maximize profitability by helping them focus on what they do best—closing deals and hitting revenue goals.

2. Make Informed Business Decisions To Maximize Profit Margins

Analyzing the variance in your costs and forecasting how they’ll change at scale can help you identify if/when your business model needs to change.

Executives outside of finance might see that the numbers look strong on the surface and that profit margins are meeting goals. But financial analysis can uncover the variance in the numbers and show that the business model won’t work as the business scales.

By flagging these issues early, CFOs and their teams can work with business leaders to proactively change their business models and make more informed decisions that maximize long-term profit margins.

I saw this play out firsthand while leading finance operations at Palantir. There were two business model challenges that may have gone unnoticed without deep insight into why our profit margins were changing month-over-month:

• Hosting Costs: Usage-based cloud pricing with AWS could have crippled the business because of the sheer volume of data new customers would introduce. Highlighting the deeper variance in our profit margins helped make the case to shift to a more predictable AWS pricing model. Profit margins dropped in the short-term but increased dramatically over the long-term.
• Customer Success Costs: Part of our business model was to assign field service reps (FSRs) to each new customer. Sending these highly technical contractors with government-level security clearance to new customer sites helped ensure product success. But it was incredibly expensive. We changed customer onboarding to eliminate the high-cost FSR approach, which increased our margins and made our cost structure more consistent.

Pricing and costs are the value drivers that impact business model profitability. Using financial data analytics to identify ways to change them and maximize profit margins will position your team as a more strategic finance function.

3. Maximize Net Revenue Retention

Analyzing the variance in customer acquisition costs (CAC), lifetime value (LTV), and other customer data can help you identify the scaling mechanism for adding new customers, but maximizing net revenue retention—will ensure customers you sign today will pay more for your products and services next year and beyond.

If your customers are between 90% and 110% net retention after a year or two instead of 120%+, that’s a sign that it may be time to update your pricing model.

One way to do customer retention analysis for NRR is by creating a customer cohort heat map. Your financial reporting and analytics tool should be able to show you a clear visualization of net revenue retention month-over-month and year-over-year. You can use that data to look for commonalities between long-standing accounts that deliver flat or declining revenue.

If your customers are between 90% and 110% net revenue retention after a year or two instead of 120%+, that’s a sign that it may be time to update your pricing model. You need to find the pricing factor that will help scale revenue and maximize LTV.

One company that has done a good job of figuring out its net revenue retention scaling factor is Fivetran. Their SaaS pricing strategy is built around “monthly active rows” in their database. So, as their customers’ data streaming needs increase over time, so does Fivetran’s revenue. Taking this unique consumption-based approach to pricing instead of offering standard contracts has been critical to the company’s success.

There’s no one-size-fits-all way to increase net revenue retention. But when you have a financial analytics system that provides deep insight into the variance in your numbers, it’s easier to find the scaling mechanism for your business.

4. Evaluate Marketing Spend To Optimize CAC

By measuring marketing spend against cash collections, you’re able to align sales and marketing on ROI. Instead of having marketing wonder why sales isn’t closing leads and sales wonder why marketing keeps sending low-quality leads, financial analytics provides deep insight into what’s working and what isn’t.

Measuring revenue attribution is a common challenge for marketing teams, which is why collaborating with finance can be so valuable. Financial analytics can help identify which ad channels provide the best possible CAC. It can show how additional marketing headcount impacts revenue. And it can help show whether or not marketing agency relationships are worth the monthly investment.

Tracking variance in all of this marketing data will facilitate productive conversations between your department and the marketing lead. Don’t just come to the table looking to cut costs. Help connect marketing strategies and initiatives to overarching business goals and act as a trusted advisor in strategic planning.

Building this cross-functional relationship will help marketing optimize its spending, ensure sales gets a higher-quality pipeline of leads, and drive revenue increases for your business.

Types of Financial Analysis

Many different types of financial analysis can help you put the broad concept into practice for your organization. This isn’t an exhaustive list, but some of the most common and universal ways to run a financial analysis include:

• Horizontal and vertical analysis. These are two ways of looking at your financial statements to spot discrepancies in the data. Horizontal analysis compares different periods to a base period, whereas vertical analysis breaks down line items by their components as percentages of the whole.
• Cohort analysis. A way to compare financial and operational data based on groupings of customers. You could look at cohorts either based on time or by segment.
• Scenario analysis. A type of financial analysis that helps you forecast the future based on various what-if scenarios. For example, you could create scenarios in your financial model for a base case for your hiring plan, an aggressive hiring plan, and a conservative one. This analysis would show how changes in model drivers impact business performance and cash flow.
• Trend analysis. This type of financial analysis involves looking at period-over-period data for individual metrics or groups of metrics to help surface trends about the business. For example, charting data for both top-line growth and profitability could help show that while the business is growing revenue, downtrends in net income, or net operating income, indicate inefficiencies in how you deploy working capital.
• Budget variance analysis. A way of comparing your forecast and actual data side-by-side to understand how the business is tracking toward plans. The most common use case for this kind of data analysis is understanding areas of over- or under-spending throughout a quarter.

There are many other types of financial analysis (especially for corporate finance or investment banking use cases), including liquidity analysis, solvency analysis, profitability analysis, and valuation analysis.

However you choose to approach an ad hoc analysis request, be sure you’re surfacing strategy insights for the business. This should never be an exercise in charting data for the sake of simply showing what the numbers are on a dashboard. Always focus on the “why” that’s driving those numbers.

Finance Analytics in Real Time with Mosaic

Your strategic role in the business is to constantly look for ways to increase financial efficiency—to question the status quo and find new ways to drive growth. That job gets a lot easier when you have the right financial analytics software at your disposal.

Mosaic gives you advanced analytics, incorporating machine learning and accessible financial business intelligence that improve planning and enable better business decision-making.

Through integrations with various data sources like your—ERP, CRM, HR system, payment systems, and more—it gives you real-time insight into your financial data. It provides a flexible canvas that makes it easy to collaborate with anyone in the business around your financial numbers. And it gives you an at-a-glance view of your company’s financial status, allowing you to spend more time thinking strategically and less time on backward-looking reporting.

With Mosaic, you won’t be part of the 86% of senior finance leaders who aren’t getting value from financial analytics. You’ll be creating competitive advantages with your strategic insights.

Want to learn how it works? Request a demo to start experimenting with the financial analytics capabilities.