Unlocking profitability insights

Our cost-to-serve survey gleaned some key insights that highlight considerations for building the next generation of finance capabilities.

Many organizations are already investing heavily in modernizing their digital core—often through large ERP transformations. These investments aim to enable a more automated and insightful finance function, one that moves toward a touchless, continuous close and reporting process by leveraging core ERP investments and emerging technologies.

But modernization doesn’t always solve every challenge.

In many organizations, some of the most complex reporting requirements and manual processes still sit outside core systems, often in spreadsheets or disconnected tools. This gap creates an opportunity for complementary technologies and flexible calculation and allocation engines that can deliver continuous insights to the business.

Cost-to-serve analytics has emerged as a particularly high-value use case within this environment. By integrating financial and operational data across the enterprise functions and applying advanced analytics, organizations can gain visibility into profitability at the customer, product, and channel levels—unlocking insights that traditional reporting rarely surfaces and enabling smarter business decisions. 

Cost-to-serve analysis quantifies and examines the activities and costs incurred across the end-to-end value chain to deliver products or services to customers. Rather than focusing solely on product margins or direct costs, cost-to-serve provides a broader view of the resources required to support each customer relationship by providing transparency into the actual cost of servicing customers across different dimensions (segments, channels, etc.).

This level of transparency can help organizations:

• Evaluate and determine future service levels and profitability;
• Activate supply chain strategy;
• Negotiate new business terms;
• Assess customer acquisitions; and
• Optimize operational efficiency.

According to the Unlocking Profitability Insights survey, only 38% of respondents reported using cost-to-serve analysis today. Among those who do, the primary goals were clear: identifying opportunities to grow and maximize profits, and ensuring products and services are properly priced.

Interestingly, the least common goal cited was leveraging technology-driven insights such as AI-enabled dynamic pricing—suggesting that while organizations recognize the value of cost-to-serve, many are still in the early stages of unlocking technologies’ full potential.

To understand cost-to-serve, it is foundational to define a customer P&L

One of the most critical components of cost-to-serve analysis is the development of a well-defined customer profit-and-loss (P&L) view.

A comprehensive customer P&L helps organizations move beyond high-level analysis to understand the full picture of costs associated with servicing customers, from logistics and service operations to order frequency and support.

Without this visibility, organizations risk overlooking hidden cost drivers that can significantly impact profitability—while a well-defined P&L can introduce new areas of opportunity.

The strategic value of understanding true cost-to-serve

When organizations fully understand the total cost of supporting each customer, product, or service, decision-making becomes far more strategic.

Several key advantages emerge, including:

• A cross-functional value chain perspective – Cost-to-serve encourages organizations to take a horizontal view across the cross-functional value chain, capturing costs beyond traditional inventory or direct service expenses.
• Dynamic forecasting and advanced analytics – With the right data model, finance teams can generate both descriptive and predictive insights, enabling more forward-looking decision-making.
• Collaborative decision-making – Cost-to-serve insights often facilitate cross-functional business alignment that leverages fact-based data to create opportunities for more collaborative business decisions.
• More strategic pricing – Greater visibility into customer segments and specific markets along with service costs allow organizations to align pricing strategies with value objectives and the long-term business vision.

Developing a cost-to-serve model requires integrating multiple types of data to build a comprehensive view of profitability. Key data sources often include:

• Customer data: segmentation, geographic locations, and service characteristics
• Operational data: information on process efficiency and operational performance
• Product data: cost, pricing, and demand patterns across products or SKUs
• Transactional data: order frequency, order size, and cost per transaction
• Channel data: distribution and fulfillment costs across delivery channels
• Financial data: direct and indirect costs, including fixed and variable cost structures

Together, these inputs provide a foundation for analyzing how resources are consumed throughout the organization and inform the cost-to-serve model.

Building a cost-to-serve model

For many organizations, developing a common view of profitability is often the first step in a successful cost-to-serve journey.

This process typically includes three steps:

1. Extracting source data
Relevant data frequently exists within the organization but may be distributed across systems and functional silos.

2. Developing a common data model
Connecting data sources at the product, customer, and operational levels enables consistent analytical views across the organization.

3. Generating analytical lenses into the business
Once a unified data model is in place, organizations can analyze profitability through multiple perspectives, including:

• Customer profitability – Establish baseline efficiency, refine service policies, and collaborate with customers on process improvements.
• Product profitability – Support portfolio decisions at the brand and SKU level while informing product mix and service strategies.
• Plant or regional performance – Identify operational inefficiencies such as downtime, energy consumption, and labor productivity opportunities.

Finance leaders are navigating an increasingly complex business environment. Shifting market dynamics, economic uncertainty, and geopolitical changes are all raising expectations for organizational agility creating an imperative for dynamic finance. At the same time, technology is evolving rapidly.

Several key trends are shaping how organizations approach finance transformation, including:

• Accelerating investment and innovation in new enterprise technologies;
  • A shift from systems that are “built to last” toward architectures that are “built to evolve”;
• Ongoing migration to cloud platforms to reduce legacy technology debt; and
• Implementation approaches centered on minimum viable products (MVPs), where additional value is delivered after go-live.

These shifts are fundamentally changing how finance teams deploy and use technology.

Technology disruption in cost accounting

Emerging technologies are also beginning to reshape traditional cost accounting practices.

According to the IMA and Deloitte survey, respondents identified two primary ways technology is expected to disrupt cost accounting and profitability analysis—automating routine tasks and processes and enabling real-time data analysis and reporting.

Despite this shift, many organizations still rely on traditional tools to model profitability. Survey responses showed that the most common tools currently used include spreadsheets (30%), standard ERP applications (21%), and custom ERP-based applications (15%). While spreadsheets remain prevalent, the growing demand for real-time insights and advanced analytics is pushing organizations toward more scalable and integrated solutions.

The cost-to-serve journey toward a competitive advantage

Ultimately, the role of profitability analysis in finance is evolving.

Cost management and profitability insights are no longer simply about reporting historical performance. Instead, they are becoming a strategic capability that enables organizations to respond quickly to market changes, optimize operations, and capture growth opportunities.

Organizations that embrace dynamic insights powered by integrated data and modern technology are often able to move faster, price smarter, and operate more efficiently. For many finance teams, the journey toward this future begins with practical steps such as implementing cost-to-serve analytics or building customer profitability models. Those starting points may seem incremental. But in practice, they can unlock transformational value and turn insights into a powerful driver of business performance.