How to Measure Product Portfolio Performance in Complex Manufacturing: Metrics to Track

Product portfolio management in complex manufacturing is the process of deciding which products, variants, and configurations to maintain, simplify, or retire based on profitability and demand. As manufacturers move from engineer-to-order to configure-to-order models, product portfolio complexity becomes harder to manage and optimize.  

Your current product portfolio decisions may rely on aggregated ERP or CRM data. But average margin or total revenue rarely reveal which configurations actually drive profitability and when.  

These product portfolio metrics within CPQ will help you understand how to simplify and rationalize configurable products to prioritize your most profitable opportunities.  

How to go deeper with portfolio optimization  

Many manufacturers manage product portfolios at the SKU level. In configurable environments, that approach can oversimplify the commercial impact of certain configurations or customer requirements.  

Profitability and sales performance are also determined by configuration choices, rather than simply product families. Two variants under the same type of product can perform dramatically differently across margin, win rate, and regional demand. 

In configurable manufacturing environments, portfolio management must go beyond final product analysis and examine configuration-level performance metrics as they relate to margin, win rate, and even regional performance. 

Where do configuration-level product portfolio metrics come from? 

These metrics are generated from configuration and sales data captured during the quoting and ordering process. 

By analyzing configuration-level data, including selected attributes, variants, win/loss outcomes, margin rates, and territory performance, manufacturers can connect engineering decisions directly to commercial results. 

This requires analytics that sit within CPQ and configuration systems. 

Who should use product portfolio metrics from CPQ data? 

Product portfolio metrics are not owned by one department. In complex manufacturing, they require cross-functional visibility: 

• Engineering leaders use them to rationalize variants and reduce unnecessary complexity. 

• Product managers evaluate performance across markets and segments. 

• Sales leaders assess win rates and margin performance by territory. 

• Executives use portfolio data to align profitability with long-term strategy. 

How to measure product portfolio performance in complex manufacturing: 5 insightful metrics  

By simplifying product portfolios and eliminating unnecessary options for your customers, you create greater bandwidth for innovation and engineering focus, faster sales cycles, and prioritization of higher value opportunities. 

What are the performance metrics that every manufacturer needs to know for deeper product portfolio management? 

Metric: Win or loss rate by product attribute  

In CPQ (Configure, Price, Quote), a product attribute is a specific characteristic or property of a product that can be selected, configured, validated, or used in pricing logic during the quoting process. For example, a product attribute may be an engine type, a body length in a heavy vehicle, or even a wall color for an elevator.  

When analyzing win rates on the attribute level, you may find that in an industrial machine, selection of Spanish or French for control panel language is associated with a substantially high win rate of 50%. Or, heavy vehicles with vinyl seats selected have a very high win rate. Now, you know how to increase the likelihood of a configuration being successful.  

On the other hand, loss rate by product attribute is equally informational. Perhaps medical trays with 60 instruments or more have very low win rates—too complex. Now you know whether you should refine the product or move it from configure-to-order (CTO) to an engineer-to-order (ETO) process for special approval and processing only.  

Metric: Sales by product attribute or value  

Analyzing sales by specific product attribute, such as installation site country, application type (e.g., home, commercial, office), or sales channel (e.g., direct vs. self-service) gives product managers and engineering teams a much clearer view of real functional demand.  

Instead of relying on assumptions in market needs, product stakeholders see which configurations actually convert in which contexts. For example, if a certain feature set consistently wins in commercial applications but underperforms in residential, that signals where to focus roadmap investment, standardization, or simplification. Similarly, strong performance in one country may justify regional variants. 

This kind of functional needs analysis supports smarter portfolio optimization. Engineering can prioritize high-revenue configurations and eliminate low-performing combinations that add complexity without driving revenue. Product managers can also tailor packaging, for example, by channel and simplify offers for self-service while preserving advanced options for direct sales. The result is fewer SKUs, lower operational complexity, and a portfolio shaped by real market demand. 

Metric: Average margin rate per deal  

Looking at average margin by territory can reveal important performance gaps. For example, Brazil may show both a strong win rate and healthy average margins of 20%, while Canada closes a similar number of deals but at significantly lower margins. 

This insight helps you determine whether pricing adjustments are needed, whether discounting behavior differs by region, or whether market conditions justify a different margin target. Instead of focusing only on win rate, you can balance growth with profitability and make deliberate decisions about where and how to protect margin. 

Metric: Cross-analysis by product attribute or configuration parameter  

Cross-analyzing attributes against defined configuration parameters — such as installation site country, application type, building size, or sales channel — uncovers meaningful patterns in buyer behavior. For example, you may discover that customers in Germany consistently select lower energy output components than those in the U.S., or that high-rise construction projects are more likely to go through direct sales rather than self-service. You might also find that certain cabinet locations correlate with stricter noise requirements. 

These insights allow you to adjust pricing, refine packaging, guide sales conversations, or even regionalize your product strategy. But to unlock this value, the relevant parameters must be clearly defined and structured within your CPQ process. Without consistent data capture at the configuration level, these patterns stay hidden.  

Metric: Number of solutions by category  

Tracking the number of solutions sold by category helps identify what is and isn’t moving. For example, if your “High-Performance 6000 RPM Motor” configuration has seen little to no sales over multiple quarters, it may be adding unnecessary complexity to engineering, pricing, or inventory management.

Low- or zero-volume categories that have been included in zero solutions can often be consolidated or removed from the portfolio entirely to reduce SKU sprawl and operational overhead. While ERP shows what ultimately shipped, CPQ data provides visibility into configured solution categories and demand patterns that may not be obvious from finished goods data alone. 

What product performance metrics enable 

Together, these product portfolio performance metrics allow manufacturers to: 

• Identify underperforming variants 

• Compare margin performance across markets 

• Rationalize product complexity using commercial data 

• Align engineering decisions with real-world demand 

• Move from anecdotal portfolio management to measurable optimization 

Proactively optimize your portfolio of highly configurable products 

Portfolio optimization requires designing products and configurations that support profitable, repeatable decisions. Tacton’s CPQ-embedded analytics gives product managers and engineering leaders direct visibility into how configuration choices impact win rates, margins, sales velocity, and portfolio complexity. 

Engineering-led metrics connect product design decisions to commercial performance, helping teams identify which variants to scale, which to simplify, and where complexity slows growth. Configuration-level data equips stakeholders to strengthen both portfolio strategy and execution. 

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