How Next-Generation Profitability Analysis Can Boost Your EBITDA

Traditional profitability analysis methods that give all customers, products and channels the same weight are outdated. Because not all revenue is good revenue – and chasing sales volumes without understanding their contribution to profitability can backfire.

High-performing organizations are adopting more sophisticated, data-enriched approaches that pinpoint where real value is being created in the business. The objective is to drive sustainable growth and maximize EBITDA by making more informed decisions on pricing, product mix, customer prioritization and operational efficiency.

Recent research by Alvarez & Marsal highlights that poor margin management – essentially saying "yes" to every customer dollar regardless of how profitable it is – can erode value. Instead, companies should prioritize true customer and product profitability, even if it means cutting some top-line volume by consolidating or eliminating certain customers, products or channel. The focus should on improving EBITDA and cash flow.¹ The key to profitable growth lies in pursuing the right revenue, not just more revenue.

This approach requires a shift from static, basic, backwardlooking profitability reports to dynamic, granular and forwardlooking profitability insights that drive both strategy and day-today execution. Traditional accounting reports and dashboards are often too high-level and lagging – they tell you what happened in the last quarter but not why or what to do next. By contrast, next-generation profitability analysis taps into real-time data and advanced analytics to provide actionable answers with insight and agility.

The Product-Customer-Channel Sweet Spot

Which products and services are actually making money? Which customer relationships are truly profitable? Which operational levers can we pull to increase margins?

The key to answering these questions is to analyze profitability at the intersection of product, customer and channel. This is often not possible through traditional product-level or channel-level P&L (Profit & Loss) reporting. For example, the overhead costs associated from a product portfolio with many SKUs or custom variants tend to remain invisible in a simple gross margin report.

A cost-to-serve (CTS) analysis, which integrates operational data with financial data to assess profitability by customer, product and channel, helps direct attention to the value-accretive combinations (and away from the dilutive ones).

When done right, it is a powerful tool to optimize operations and enhance profitability – yet most companies do not leverage it today. Those that do gain a clearer picture of which offerings and customer relationships truly create value, which SKUs and services drive the most profits, and which customer segments are essentially subsidized by others.

Allocating Cost and SG&A Expenses With Precision

One major obstacle to obtaining granular profitability insights is the inaccurate allocation of indirect costs, including overhead and SG&A (Selling, General and Administrative) expenses.

If these costs are spread evenly or arbitrarily across products or business lines, the financial picture can be grossly misrepresented. Unfortunately, many organizations still allocate indirect expenses using simplistic methods – for example, using a single cost driver (like revenue or volume) to "prorate" all overhead, which can lead to some products or services being over or undercosted. Without precise allocation, it's difficult to identify which offerings or client accounts are consuming disproportionate resources and thus eroding margins.

Misallocated SG&A expenses can mislead managers, leading them to "push" a product that looks profitable on a spreadsheet but barely breaks even when support costs are factored in. This lack of clarity often drives wrong decision-making and disconnected priorities, such as sales teams chasing volume in a low-margin segment, or operations teams overloaded by a "profitless" product line. To avoid this, organizations need to regularly refine their cost allocation models to reflect reality.

A key enabler is to leverage advanced tools to collect real-time data on resource usage and costs. Modern ERP and analytics systems can capture detailed operational metrics (transactions processed, warehouse picks, service call minutes, etc.), which can then be mapped to costs.

Once this data is fed into profitability models, companies can allocate their indirect costs more accurately, quickly spotting when certain products or customers are using more than their fair share of overhead.

Crucially, as businesses and operating models evolve, the costing model should be revisited. New products, new channels, automation or organizational changes can all shift how indirect costs are incurred, and cost allocation rules and assumptions must be updated regularly (e.g. annually or semi-annually) to keep the picture accurate. Finance teams should also look for ways to optimize those indirect costs – if a particular customer segment requires an excessive amount of support hours, that insight can spur an effort to streamline processes or adjust service levels.

Understanding cost-to-serve and creating transparency on margins is the foundation of an effective commercial strategy for profitable growth. Technology is a centerpiece of that approach, helping finance and operational teams dramatically reduce the time spent gathering data and instead focus on analyzing it. The result is greater accuracy in cost allocation, faster reporting and the ability to drill down into profitability by product, customer, region or even individual orders at the click of a button.

Order Discipline: How to Ensure Every Sale Hits the Target Margin

Another pillar of next-generation profitability analysis is order discipline.

Too often, companies discover that while their average margins look acceptable, a significant number of individual orders or deals are actually below target – sometimes even loss-making. These sub-par orders can quietly drag down overall profitability. To fight this, leading companies establish clear and consistently applied quote and order management rules. Examples include setting minimum order value thresholds, minimum margin requirements or adding surcharges for smaller orders. The idea is to avoid accepting businesses that fail to cover their share of costs. If an incoming order is too small or low-priced to be profitable, there should be either an automatic price adjustment, an additional fee or a firm policy to upsell/bundle it with other sales.

A compelling example comes from the e-commerce sector. ABOUT YOU, a German online fashion retailer, introduced a minimum order value requirement to weed out loss-making orders that were hurting its fulfillment economics. This move was part of a broader profitability push as the company aimed for EBITDA break-even. By enforcing a floor on order size, the business reduced the frequency of small, unprofitable orders and improved its fulfillment cost-to-revenue ratio². In other words, they realized that shipping a $20 order costs nearly as much in picking, packing, and delivery as a $50 order – so they set a threshold to encourage larger basket sizes and started charging extra for smaller purchases.

Many B2C and B2B companies use similar tactics. Large retailers often set a free shipping minimum to incentivize higher order value and protect margin on the logistics side.

In B2B manufacturing or distribution, it's common to see minimum order quantities or values to ensure that the overhead of processing an order (entering it, setting up production, shipping, invoicing) is covered by its profit.

Beyond order size, profitability leaders also institute margin guardrails in their quoting process. Sales reps may be given clear floors for discounting – and any deal that falls below a certain gross margin percentage triggers an approval workflow to a manager or finance. This ensures that "exception" low-margin deals are consciously decided, not accidentally slipped through. It also creates a feedback loop: if the sales team finds they often need to price below threshold to win in a certain segment, that's a strategic signal to either reduce costs or reconsider serving that segment. By managing pricing and orders with these controls, companies avoid the trap of volume for volume's sake. They won't spend resources on orders that destroy value. Instead, they maintain a healthy margin mix and direct their effort toward the most profitable opportunities. At a recent retail event, executives emphasized that pursuing volume without profit is pointless; every transaction must, on average, generate a profit for the business.

Margin Concessions: Do It Only Sparsely, and Surgically

When it comes to key accounts – those large, strategically important customers – companies may at times choose to bend those margin rules, as long as they do it with eyes wide open.

Granting a lower margin for a key account on certain orders or products can help strengthen relationships, increase volume or lock in long-term contracts, leading to profits in the long run. The classic example is giving volume discounts or special pricing to a top customer in exchange for loyalty or a multi-year purchase commitment. These decisions represent deliberate trade-offs between immediate profit on a particular sale and the longer-term value of the customer. Such trade-offs should be carefully evaluated and authorized by C-level, rather than ad-hoc exceptions made in the field.

These margin concessions must be managed tightly to avoid a downward spiral in pricing and profitability.

For example, under pressure to hit quarterly sales targets, sales teams might accept a zero-margin deal to book the revenue and avoid conflict with a key account. But once a customer knows you'll drop price to keep their business, they may push for even deeper concessions, and your competitors might do the same, eroding industry margins. This practice is perhaps most prevalent in the consumer goods sector, where companies often chase volume at the expense of profit, ultimately hurting their brand health and pricing integrity. In short: if you do give special terms to key accounts, do it for the right strategic reasons, ensure it's part of a broader and always quantify the impact (for instance, we'll accept a two-point margin drop on this customer in exchange for 20% higher volume, which still increases total profit by $X.)

Shift to Value-Based Pricing for Higher Margins

An equally important lever of profitability is pricing strategy – specifically, shifting from "cost-plus" to more strategic, value-based pricing models

Cost-plus pricing (taking your cost and adding a fixed markup) has been the default in many industries, but it often leaves money on the table by ignoring the value perceived by the customer and their willingness to pay. Value-based pricing means if you deliver higher value or unique benefits compared to competitors, you should price accordingly to capture a fair share of that value for your firm. This approach can significantly boost margins, especially for differentiated offerings, because customers will pay more when they clearly see the superior value – be it in quality, service, brand prestige or outcomes delivered.

Several advantages come with this shift:

• Higher Margins: Companies capture more of the economic value they create. Rather than pricing only slightly above cost, they price closer to the maximum customers are willing to pay, which increases profit per unit. In fact, studies suggest value-based strategies can lift margins well above those achieved with cost-plus formulas.
• Differentiation and Competitive Edge: In pursuing value-based pricing, a company must understand and emphasize its unique value proposition. This often leads to better product positioning and innovation focused on what customers truly care about. By pricing on benefits, you also educate the market on those benefits, which can strengthen your competitive position.
• Customer Satisfaction and Loyalty: When pricing is aligned with perceived value, customers are more likely to feel the price is fair for the benefits they receive. They don't feel nickel-and-dimed, but rather that they are paying for quality or outcomes. Satisfied customers, in turn, tend to be more loyal and less likely to churn.

Implementing value-based pricing requires deeper customer insights and market research. Companies need to gather data on how different customer segments value various features and services. Techniques such as customer surveys, willingness-topay analysis and joint studies help estimate the perceived value. It also requires a mindset shift internally – sales and product teams must move away from purely cost thinking and towards value communication. This can be challenging, but the payoff is compelling, and includes not only improved margins but a better understanding of what customers need and want.

For companies embarking on value-based pricing, a few practical steps include: segmenting your market (different segments may value your offering differently), quantifying your value (e.g. how much cost you save the customer, or what premium performance you offer), and communicating value clearly (train the salesforce to sell on value, not price). Also, pricing should be dynamic – as you deliver more value or as customer perceptions change, you adjust prices accordingly. The reward for all this effort is a more optimized price architecture that maximizes profit while maintaining customer trust. As many private equity operating partners will attest, pricing initiatives (especially moving to value-based pricing) can yield some of the fastest and largest improvements in margin, often with minimal additional cost. It is about capturing the value that's already being created.

Actionable Data at Managers' Fingertips with Real-Time Dashboards

Gone are the days when finance or BI teams produced static monthly reports that were outdated by the time they arrived.

Today's business environment moves quickly, and having up-to-the-minute visibility into revenues, costs and margins is essential. Modern profitability dashboards aggregate data across customers, products and channels, updating it continuously (or at least daily) and enabling cross-functional teams to monitor performance and respond faster to emerging threats or opportunities. The benefit is not just in seeing the data, but in being able to diagnose, predict and act on it in a timely manner. For example, if a particular product's margin is trending down this week due to a spike in logistics costs, the operations team can notice it on the dashboard and immediately investigate why. If a certain sales region is selling an unusually unprofitable mix of products, the sales leader can spot that and take corrective action (like adjusting incentives or pricing) before it hurts the monthly results.

A well-designed profitability dashboard typically includes key KPIs and drill-downs. At a high level, it might show metrics like gross margin percentage, EBITDA margin and variances against target for the overall business. Users can then slice those metrics by product line, customer segment, channel, geography, etc., often with interactive charts. Crucially, these dashboards allow users to click down to the next level of detail – for instance, from total company margin  to margin by division  to margin by product family  to margin by SKU, all in a few clicks. This empowers managers to trace issues to their root cause quickly. If the total margin is off, you can find where it's coming from (a particular product or customer or region) and explore why (maybe that segment had an unusual cost-to-serve event, or pricing was off, or volume shifted). Some advanced dashboards even integrate operational metrics (like production efficiency or customer service levels) alongside financial ones, so you can see cause-and-effect in real time.

Moreover, real-time dashboards promote a data-driven culture. When everyone from the CFO to the front-line sales manager has access to the same live profitability metrics, it encourages proactive ownership of results. Departmental silos break down as teams collaborate around the data. For example, if the dashboard shows that a certain customer's orders are consistently under the minimum margin (perhaps due to frequent rush shipments), the sales, operations and finance teams can jointly strategize on how to fix it – maybe by renegotiating terms or changing the service model.

Fact-based decisions

The transparency also helps avoid finger-pointing; the data highlights facts and trade-offs objectively. Additionally, modern BI tools often allow users to set alerts on the dashboard, so they get notified if a metric crosses a threshold (e.g. "alert me if product X's weekly margin falls below 20%"). This ensures timely action. Ultimately, the investment in real-time profitability monitoring pays off by enabling businesses to adapt "on the fly" and capture opportunities (or stem losses) much faster than their more sluggish competitors.

To maximize its impact, a profitability analysis dashboard should be embedded within cross-functional governance processes, such as the Sales and Operations Planning (S&OP) process or similar frameworks. This will ensure that profitability insights are reviewed and acted upon in structured daily, weekly, monthly, quarterly and yearly loops. By embedding the dashboard into these governance cycles, organizations can align decisionmaking across functions, maintain a consistent focus on profitability and drive continuous improvement. Weekly reviews might focus on short-term tactical adjustments, while quarterly and yearly loops could address strategic shifts based on longterm trends. This approach ensures that profitability metrics are not just monitored but actively used to guide collaborative, datadriven decisions across the organization.

Predictive Analytics and AI: Anticipating Margin Trends

Looking ahead, organizations will increasingly turn to predictive analysis and machine learning to anticipate profitability trends and inform both strategic and operational choices.

While dashboards provide visibility into what's happening now or in the recent past, AI models can help forecast what is likely to happen next if current patterns continue, or if certain levers are pulled. Advanced machine learning algorithms can sift through vast amounts of historical and real-time data – including sales patterns, cost drivers, customer behaviors, economic indicators, etc. – to uncover hidden patterns and relationships that would be impossible for humans to discern. They can then use these patterns to predict future outcomes, such as revenue and margin trajectories, or to flag anomalies. For example, an AI model might analyze dozens of variables (marketing spend, search trends, inventory levels, weather, etc.) and predict that next month's profitability for Product A will dip because of an uptick in supply costs and a forecasted demand drop. Such foresight gives management a chance to take preemptive actions (like adjusting pricing or promotions, securing cheaper suppliers, etc.) to protect margins.

One of the big advantages of AI-driven analysis is the ability to uncover subtle patterns and drivers of profitability that traditional analysis might overlook. They bring out the interactions of factors – maybe a particular product is only profitable when sold in certain bundle sizes and through a particular channel, and loses money otherwise. AI can surface these insights so managers can refine pricing or sales strategy accordingly

AI for customer segmentation

Another powerful application of AI is in profitability segmentation and classification. Machine learning models can classify products or customers into performance tiers (high-profit, medium, low-profit, loss-making) based on a multitude of attributes. This goes beyond simple ranking by margin; the models can consider forward-looking indicators as well. For example, an AI model could classify a customer as "high lifetime value potential" even if their current profitability is mediocre, because it detects patterns (like increasing order frequency and responsiveness to marketing) that usually precede big growth. Conversely, it might flag a historically profitable customer as at-risk if subtler signs (like changes in order mix or slower payment) match patterns of customers who later turned unprofitable or churned. Such insights help the company direct its sales efforts – doubling down on nurturing the truly valuable customers/products and rethinking or repricing the underperformers.

Predictive analytics also excels at scenario planning: companies can ask "What if?" and have models simulate the impact on profitability. What if we raise price by 5% in this segment – how might volume change and what would be the net effect on EBITDA? What if oil prices surge or a tariff is imposed – which product lines' margins are most exposed, and by how much? AI-driven tools can process these scenarios quickly and even recommend optimal actions (this enters the realm of prescriptive analytics).

AI might analyze cost and price elasticity data to suggest which products could tolerate a price increase without significant volume loss, thereby improving margin with minimal risk. In practical terms, an organization might use predictive models to forecast profitability by customer for the next quarter; if the model predicts certain accounts will drop in profit (maybe due to expected orders of a less profitable product mix), the account managers can be alerted to take corrective action (perhaps upsell more profitable lines or renegotiate terms).

Predicting opportunities

Predictive analysis is particularly valuable for New Product Introduction (NPI), as it enables organizations to forecast profitability across various markets before launch. Predictive models can analyze historical data from similar products, market trends, competitive dynamics and cost structures to estimate how a new product will perform in different regions, channels or customer segments. The analysis can reveal, for instance, that a product is likely to achieve high margins in one market due to strong demand and low competition, while another market might pose challenges due to higher costs or price sensitivity. This foresight allows companies to tailor their go-to-market strategies, optimize pricing and allocate resources more effectively. Additionally, predictive analytics can help identify markets where the product might struggle to meet profitability thresholds, enabling adjustments to the product design, supply chain or marketing approach. Through these insights, organizations can significantly improve the success rate of new product launches and ensure that profitability is built into the strategy from the outset.

In summary, AI and machine learning add an anticipatory layer to profitability management. Instead of just reacting to financial results, companies can anticipate margin pressures or opportunities and respond in advance. Early adopters are already seeing benefits: fewer surprises in earnings, more optimized pricing strategies and targeted improvements where analytics show the biggest ROI. As data becomes more abundant (from IoT sensors in manufacturing to detailed customer interaction data in services), these models will only get more accurate. They will inform both the big strategic choices (Which business segments should we invest in or exit? What's the outlook for our margin next year and where do we intervene?) and the day-to-day decisions (Which order should be flagged for review? Which customer is likely to become more profitable with a bit more attention?). Of course, human judgment remains vital – AI provides analysis, not wisdom – but it's a powerful assist. Companies that master these tools will direct their efforts toward the highest-margin opportunities and preempt many problems, giving them a significant competitive advantage in maximizing EBITDA.

Conclusion: Turning Profitability Analysis into a Strategic Advantage

In an environment where every point of margin counts, smarter profitability insights are essential.

Companies that truly understand where and how profits are made can make fundamentally better decisions. They can prioritize high-margin opportunities, focus resources on the most profitable products, customers and channels, and confidently cut or improve the areas that are diluting returns. By eliminating wasteful complexity that adds cost but not value, they essentially reduce the "cost-of-complexity" tax on their business. These companies are also more agile, because they detect shifts in profitability drivers sooner and adapt faster to change.

In contrast, companies sticking with outdated, one-size-fits-all analysis will increasingly operate with blind spots. They may chase growth in areas that actually undermine their profitability or miss chances to double down on their strengths.

Smarter profitability analysis equips executives and private equity operating partners with granular insights to take decisive actions, whether reallocating resources, repricing products, re-segmenting customers, tightening cost controls or investing in what works. The impact shows up directly in EBITDA improvement, greater resilience, stronger competitive edge, and ultimately, in enhanced shareholder value.

Footnotes

1. https://alvarezandmarsal-pepi.com/insight/show-me-the-money-but-the-right-money-please-part-1-where-is-the-money/

2. https://s205.q4cdn.com/202422753/files/doc_financials/2022/q4/23-5-11_PressRelease_AY_EN.pdf