This case study reveals how a finance organization can become a key driver of strategic execution by leveraging machine learning (ML)-based forecasting and embedded business partners.

This approach fosters cross-functional alignment, builds a culture of accountability, and enables data-driven decision-making around strategic pricing and resource optimization, ultimately enhancing financial health and positioning the company for sustainable growth.

The AFP FP&A Case Study series is designed to help you build up key FP&A capabilities and skills by sharing examples of how leading practitioners have tackled challenges in their work and the lessons learned.

Key Learnings

1. The role of finance business partnering: Embedding finance professionals across business segments is critical for communicating the implications of forecast results and key performance indicator (KPI) trends. Finance business partners are essential in demonstrating the impact of financial initiatives, helping to secure buy-in and aligning teams toward shared financial goals.
2. Cross-functional impact of the finance organization: By leveraging advanced financial forecasting techniques and engaging with business units through partnering, the finance teams can create significant cross-functional impact. This approach not only enhances financial health but also builds a culture of accountability and alignment across the organization, driving strategic execution through collaborative channels.
3. The power of forecasting models: Forecasting models form the foundation of data-driven decision-making. Especially in scenarios with large data sets and multiple driving parameters, ML models are instrumental in creating accurate forecasts, which enables proactive decision-making that mitigates risks before they impact financial outcomes.

Background

This case study is about a rapidly growing company in the financial technology (fintech) sector. Emerging as a trusted partner to some of the world’s leading banking institutions, payment applications and fintech aggregators, the company offers a robust cloud-based platform and software-as-a-service (SaaS) solutions. It empowers its customers with advanced tools for real-time payment processing, fraud detection and financial analytics.

Over the past few years, the company has scaled its revenue at a breakneck pace, riding the wave of a growing global demand for digital financial services. Originally a regional player, the company has expanded into a global operation, with footprints firmly established across the Americas and Europe, Middle East and Africa (EMEA) markets and strong growth signals in Asia-Pacific (APAC) markets. This geographic expansion has not only broadened its customer base but also solidified its reputation as an innovator in the fintech space.

Challenge

Like many mid- to large-growth-stage technology companies, particularly cloud-based SaaS providers, the company faced a key financial challenge: optimizing cost efficiency and improving gross margins.

Despite soaring revenues, the company’s gross margin percentage remained stubbornly stagnant. While top-line growth dazzled stakeholders, stagnant margins raised concerns among investors, board members and other key stakeholders.

Gross margin — often scrutinized by investors assessing a company’s readiness for its next funding round or a public offering — is a critical measure of efficiency and competitiveness. Investors frequently benchmark a company’s gross margins against those of peer companies, making it essential for providers to manage this metric effectively.

Figure: Sample scenario with disproportionate growth in revenue and gross margin.
Gross margin % less than industry benchmark.

As the company scaled, it faced the challenge of balancing aggressive revenue growth with effective cost management. Siloes developed as revenue-generating teams, such as sales and go-to-market (GTM), operated independently from cloud resource management teams, such as engineering and DevOps.

Driven by revenue targets, the sales and GTM teams prioritized customer acquisition and service expansion to meet market demands, and they lacked insight into the cost implications of their growth strategies, particularly as it related to the impact on cloud usage.

Meanwhile, engineering and DevOps teams, responsible for managing cloud resources and controlling operational costs, were focused on system performance and cost efficiency. As a result, they sometimes viewed GTM’s initiatives as excessive or unsustainable.

The structural opposition between revenue-generating and cloud resource management teams can create a healthy tension between revenue growth and cost containment. However, the company was experiencing too much opposition, which resulted in an organizational disconnect that hampered effective resource planning and cost optimization.

Revenue growth initiatives led to “cloud sprawl,” where the rapid expansion of cloud resources outpaced cost control measures. This not only increased operational expenses but also put pressure on the gross margin. Addressing this challenge required a strategic alignment of revenue and resource management teams to enable financially sustainable and operationally efficient growth.

Approach

To overcome the disconnect between revenue-generating teams and operational resource managers, the finance organization followed a framework that leveraged data-driven insights and advanced forecasting techniques.

Figure: Framework for unlocking financial performance & improving gross margins.

Financial Business Partnering Across Business Segments

Creating strong partnerships across business segments — sales, GTM, engineering and DevOps — was crucial for success. By embedding finance professionals as business partners within each team, the finance organization was able to provide data-driven insights, inform on the financial impacts of decisions, generate support for cost-efficiency initiatives and create alignment on shared goals.

Baseline Analysis

The FP&A team performed a baseline analysis of the cost of goods sold (COGS), focusing specifically on the components tied to cloud infrastructure. The primary components of COGS consisted of:

• Cloud resources: This included expenditures on cloud infrastructure, data storage, compute power and networking, which enable the delivery of services to customers. These costs can scale significantly with growth in customer base and service usage.
• Software platform upkeep: This encompassed secondary software tools required to maintain platform functionality, security and performance, as well as personnel costs tied to software maintenance, support and operational oversight.

The team then analyzed the causal relation between cloud cost and key business drivers, such as transaction volumes, user growth, fraud detection activities and regulatory compliance requirements.

They identified that a combination of higher user growth and transactions per user were primary drivers of end-user-specific cloud costs. Secondary drivers of the cloud spend were specific product feature usage (like fraud detection and risk modeling) and compliance overhead (like data retention).

Understanding these costs in detail was essential to identifying which elements drove the largest expenses and which services may need optimization.

Forecasting with ML Models

The complexity and granularity of cloud cost components involved extensive datasets with intricate interdependencies. ML-driven forecasts helped FP&A transform complex relationships into actionable insights.

Python packages like Scikit-Learn and Prophet were particularly effective for time-series forecasting on large datasets, but R and SAS could have been used as well, given their robust capabilities for such analyses.

FP&A employed ML models to forecast future metrics under two scenarios: 1) maintaining the status quo and 2) implementing specific cost management initiatives. These models provided insights into the potential financial trajectory, illustrating to all stakeholders how a lack of intervention could impact margins over time.

“What-if” analyses allowed leadership to visualize how initiatives — such as optimizing cloud resources, adjusting infrastructure and modifying sales goals — could help improve gross margins and drive the company toward its goals.

Gross Margin Improvement Plan Based on insights from the baseline analysis and forecasts, finance created a detailed gross margin improvement plan, which outlined specific initiatives for cost control, resource optimization and pricing adjustments with relevant metrics and goals.

At a high level, the plan covered the following key areas:

Cost Component and Pricing Disconnect

• Highlight discrepancies between product pricing and underlying cost structures. For example, certain product tiers include features with high operational costs that are priced too low to cover expenses, leading to margin erosion.
• Create visualizations to show how current pricing fails to account for cost-heavy components, such as compute-intensive analytics or high-storage requirements, emphasizing the need for a realignment.

Vendor Discounts and Negotiations

• Outline opportunities to secure cost reductions through strategic negotiations with cloud vendors. Demonstrate how long-term commitments or volume discounts can lower the unit costs of cloud resources.
• Present scenarios in which leveraging reserved instances or exploring multi-cloud strategies could reduce dependency on a single vendor, thereby improving negotiating leverage.

Monitoring and Maintenance Spend Reduction

• Identify internal inefficiencies, such as underutilized resources, redundant systems and suboptimal scaling practices, that contribute to unnecessary cloud spending.
• Quantify potential savings from initiatives such as implementing auto-scaling, optimizing resource allocation and decommissioning idle instances.

Rationalization of High-Cost Product Features

• Demonstrate how certain product features disproportionately drive up costs without delivering equivalent customer value or revenue. For example, advanced analytics modules or real-time processing capabilities are underutilized by the majority of customers and require significant compute power.
• Propose strategies to rationalize these features, such as bundling them into premium pricing tiers, offering them as add-ons or phasing out features with low adoption and high overhead.

Executive Communication and Buy-in

As the initiative required technological investment and cross-functional alignment, communicating the plan effectively to executives was essential to securing their buy-in and support. The team demonstrated how the gross margin improvement plan directly supported broader strategic objectives, such as preparing for the next funding round or positioning for an IPO, by explaining how improved gross margins:

• Strengthen financial performance metrics: Improved gross margins enhance earnings before interest, taxes, depreciation and amortization (EBITDA) and free cash flow, making the company more attractive to investors.
• Boost competitive positioning: Rationalizing pricing and reducing costs allows for more competitive offerings without sacrificing profitability.
• Ensure scalability: Optimizing costs creates a more sustainable foundation for scaling the business while maintaining operational efficiency.

Milestone-Based Goals with KPIs and Tracking

To make it easier to track progress and maintain momentum, finance broke down the overarching goal into smaller, achievable milestones, with a set of KPIs and metrics for each milestone. The metrics review was integrated into the existing cadence of performance management, allowing for continuous visibility into progress and real-time adjustments to the plan without adding significant effort.

Outcome

As a result of the gross margin improvement plan, the company redesigned its pricing and packaging to follow a more cost-reflective model. This approach helped it:

• Eliminate disparities between cost and revenue generation, ensuring a healthier gross margin (5-7% margin gain over time).
• Reduce resource wastage, driving down operational cost, with 2-3% gains over time.
• Increase gross margins, improving overall financial health, crucial to investor appeal and IPO readiness.
• Raise the average deal size by aligning pricing more closely with customer value and reducing friction in certain areas of the customer journey.

The outcome highlights how strategic pricing, resource optimization and strong cross-functional partnerships can substantially enhance financial performance and position a company for sustainable growth.