According to Gartner, “one in every $6 spent by CMOs is invested in innovation, despite doubts in the skills and capabilities available to support these programs.” One of the most important tasks for new product development is to select the right metrics.
We all understand that metrics enable you to better understand your performance. Selecting the right measures and metrics requires critical and strategic thinking. The point of capturing and reporting them is to help with making key business decisions and managing risk. If a decision and an action are not a result of the metric, then you really don’t have a good metric.

Let’s explore the role metrics play in creating effective Product Development strategies and processes. New Product Development (NPD) performance is multifaceted. Companies measure NPD outcomes in many different ways, such as profitability, product quality, and research and development investment efficiency. In NPD and product development, there are hundreds of measures in use, resulting in what could be called “measurement clutter.” As a result, it may seem difficult to land on just a few key metrics and performance indicators. We recommend that you select no more than five measures for any given product development and management arena.
Four key principles to help you select metrics for NPD or any other part of your Marketing function:
1. Select metrics that relate innovation to enterprise performance. This will require that you understand your company’s business model, that is, the assumptions that describe how your organization fulfills its mission.
2. Select metrics that will foster action. This will require you to answer the question of “who must understand what the metric means and act upon the information?” Good metrics serve as more than an accounting artifact.
3. Measure effectiveness first and efficiency second.
4. Balance leading and lagging indicators.
In the product development and management arena, metrics tend to be finite, historical, and accounting-based. Common metrics for NPD include tracking Engineering Change Orders and cycle time.
There are more valuable metrics. For example, “customer desirability for a new feature” might provide better insight into how likely and quickly the market will accept the new product. If you’re uncertain about the right measures and metrics for your effort, seek expert advice.

Consider these factors when developing your NPD metrics:
1. Determine who will be consuming the information. Who is the audience for the information? What do they need to know to make a decision or take action?
2. Define the performance targets and the performance indicators. What outcome is expected and what will indicate that progress is being made to realize this outcome? These will serve as the basis for your metrics.
3. Identify the enablers, those things that will enable you to achieve the performance.
4. Develop your “dashboard”. How will you report the metrics and how frequently?
5. Assess your infrastructure. Do you have the infrastructure to support capturing the data?
6. Assess your team. Are the people available to support the effort and do they have the right skills?
AQ:
A: Because metrics are decision tools, not reporting artifacts. They help leaders understand performance, manage risk, and make informed tradeoffs. If a metric does not drive a decision or action, it is not a good metric.
A: NPD performance is multifaceted (profitability, quality, R&D efficiency, adoption, etc.), and hundreds of measures can be tracked. This creates clutter that obscures priorities. Best practice is to select no more than five key measures for any given product development/management arena.
A:
- Relate innovation to enterprise performance: Tie innovation measures to the business model and how the firm creates value.
- Foster action: Define who must understand the metric and what they will do differently as a result.
- Measure effectiveness first, efficiency second: Confirm you are building the right things before optimizing how you build them.
- Balance leading and lagging indicators: Combine forward-looking signals with outcome confirmation.
A: Because they tend to be finite, historical, and accounting-based (e.g., Engineering Change Orders, cycle time). These can be useful operationally, but they may not indicate market acceptance or customer value.
A: “Customer desirability for a new feature.” This can provide better insight into likely adoption speed and market acceptance—linking development choices to customer value and commercial outcomes.
A:
- Audience: Who will consume the information, and what decisions/actions must it enable?
- Targets and indicators: What outcomes are expected, and what signals progress toward those outcomes?
- Enablers: What capabilities or conditions must be in place to achieve performance?
- Dashboard design: How will metrics be reported, and at what cadence?
- Infrastructure readiness: Can you reliably capture the required data?
- Team readiness: Do you have the right people and skills to execute the measurement and improvement work?
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