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March 17, 2023

Product Management KPIs & Metrics in AI Development


Edwin Lisowski

CSO & Co-Founder

Reading time:

11 minutes

Key product management metrics and KPIs are integral parts of any AI development strategy. They help project managers set goals, detect problems, and optimize their product development strategies. As IT technologies continue to evolve, the problem is no longer how to measure metrics but rather choosing which metrics to focus on. Misplacing your focus efforts in the current business atmosphere, particularly in AI projects, could leave you in a blind spot where you’re no longer capable of identifying potential weaknesses and bottlenecks in your project.

This article will delve into some of the key product management metrics and KPIs and their importance in AI project development.

What are product development KPIs and metrics?

Product management metrics and KPIs serve a similar purpose – helping AI product management specialists monitor their performance and ensure they’re working towards a successful product strategy. However, despite often being used interchangeably, these two strategies are actually quite different.

Product management KPIs (key performance indicators) measure the progress and performance of a project based on specific business objectives. Essentially, they only track aspects that are critically important to a business’s objectives. This way, they’re able to help project managers make informed decisions by acting as a roadmap that guides the project toward meeting specific business objectives.

What are product development metrics and KPIs?

On the other hand, product development metrics are quantitative measurements used to track specific business projects’ performance at an operational and tactical level. Like KPIs, metrics help to track and provide context to the performance of key business goals. However, metrics deal with a larger scope of business objectives, not just focusing on key objectives like KPIs.

Product management KPIs in AI product management

KPIs are an integral part of AI product development strategies. They provide an accurate indication of how effectively a development team is achieving its goals by measuring the product’s success and identifying areas for improvement.

The product management KPIs necessary for effective AI project development include:


Accuracy KPIs are a measure of the number of correct predictions made by an AI program. An AI program can make a:

  • True positive prediction: when a model correctly predicts a result as positive
  • True negative prediction: when a model correctly predicts a result as negative
  • False positive: when a model incorrectly predicts a result as positive
  • False negative: when a model incorrectly predicts a result as negative

Essentially, accuracy KPIs are a ratio to correct predictions to the total number of predictions. Project managers can use these KPIs to improve an AI model’s predictive capabilities and improve its performance.

Confusion Matrix

Confusion matrix KPI is a tabular representation of predicted values to the actual values of a specific dataset. These KPIs provide a clear understanding of a model’s results to avoid any ‘confusion’ in the model’s results.

Confusion Matrix

Precision and recall

Precision is a measure of the ratio of true positives to actual positives. It provides information on how often a model correctly predicts positive outcomes. Recall, on the other hand, is a cumulative measure of all positive predictions to actual positives (including false negatives. Precision and recall are essential measurements in determining the accuracy and quality of an AI model.


Speed KPIs are a measure of how fast an AI model makes predictions or performs its specific role. Generally, the faster the model, the more efficient it is.

User satisfaction

User satisfaction KPIs are essential KPIs for product management and development, especially when it comes to projects that involve user satisfaction. These KPIs measure the users’ level of satisfaction with the AI system and its performance. A higher user satisfaction rate typically indicates a higher probability of the model’s success.

Resource utilization

Resource utilization is one of the most important KPIs in AI project development. It measures the number of resources used by the AI model. This can include everything from computing power to memory usage. Resource utilization can provide important information, such as the model’s cost-effectiveness and energy consumption.

Business impact

Business impact is a measure of the impact of an AI system on business outcomes, such as increased productivity and reduced costs. It provides an accurate representation of the model’s success with core business objectives.


In the current business atmosphere, any AI system that’s not designed to scale will eventually fail and become obsolete. Scalability KPIs measure how well an AI model handles an increasing amount of users and data. Typically, the more scalable a model is, the higher its probability of success.

Product management metrics in AI product development

Project management metrics are crucial to the success of any AI product development project. They help project managers track and measure the performance of their projects, thus enabling them to make data-driven decisions about possible improvements and future developments. [1]

Here are some of the most important project management metrics you need to focus on in any AI development project.

User engagement metrics

User engagement metrics measure how users interact and use your AI project. [2] They include:

User engagement metrics

  • Active Users: Active user metrics measure the number of active users who engage with your AI project within a certain period of time. They serve as effective indicators of the number of people actively using your product and can be used to identify trends in usage.
  • Session Time: These metrics measure the average amount of time users spend interacting with your AI project. They can provide insights into user engagement and help identify potential areas that need improvement.
  • Frequency of Use: How often do users engage with your product within a specific period? Frequency of use metrics can provide a good indicator of how loyal your users are and how often they find value in your product.
  • User Behavior: User behavior metrics measure the specific actions users take within your AI project. This can include everything from clicks, searches, and interactions with certain features. When taken into account, these metrics can provide insights into the most popular features in your project and the ones that need improvement.
  • Time to Value: Time-to-value metrics measure the amount of time users take to gain value from your product. As such, they can help identify areas that need improvements to improve user satisfaction and engagement.

Retention Metrics

Retention metrics measure the number of users who continue to use your product over time [3]. They include:

  • Repeat Usage: These metrics measure the percentage of users who continue using your product after their initial engagement. This can serve as a good indicator of user loyalty and your product’s capability to provide value.
  • Churn Rate: Churn rate metrics measure the rate at which users stop using your product over time. A high churn rate typically indicates the presence of negative issues with the product that needs to be addressed.
  • Customer Lifetime Value: Customer lifetime value is a measure of how much revenue individual users generate by using your product over their lifetime. This metric can help identify your most valuable users and provide insights into marketing and user acquisition strategies.
  • Retention Rate: Retention rate is a measure of the percentage of users who continue to use your product over time. A high retention rate typically indicates that users find value in your product, and they’re most likely to keep using it.

It might be interesting for you: How To Use Data for Better Customer Experience, Customer 360

Conversion Metrics

Conversion metrics measure the percentage of users who complete a desired action, such as signing up for a subscription after using your AI product. [4]

  • Conversion Rate: Conversion rate is a measure of the total number of users who take a desired action after engaging with your product. A high conversion rate typically indicates a high degree of user satisfaction and your product’s suitability for its intended purpose.
  • Cost Per Acquisition (CPA): CPA metrics measure the cost of acquiring a new user. They can serve as an indication of the effectiveness of your marketing strategies.
  • Average Revenue Per User: These metrics measure the average revenue generated by each user. They can serve as effective guidelines for optimizing marketing strategies.
  • Funnel Conversion Rate: Funnel conversion rate is a measure of the number of users who complete each stage of the conversion funnel. They can help identify challenging stages in the funnel that need improvement.

Customer satisfaction metrics

As the name suggests, customer satisfaction metrics measure how much users are satisfied with your AI product, essentially providing insights into how users perceive it[5]. They include:

1. Net Promoter Score (NPS)

NPS metrics measure the likelihood of users recommending your product to other users. This metric is typically calculated through user ratings.
Here, users are divided into three categories including:

  • Promoters (rate the product 9 or 10)
  • Passives (rate the product 7 or 8), and
  • Detractors (give a rating of 6 and below)

Net Promoter Score (NPS), product development

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Project managers can then determine their product’s NPS by subtracting the percentage of detractors from the percentage of promoters. A high NPS typically indicates a high satisfaction rate.

2. Customer Satisfaction Score (CSAT)

CSAT metrics measure users’ level of satisfaction with your product on a scale of 1 to 10. A higher score typically indicates greater satisfaction.

3. Customer Effort Score (CES)

CES metrics measure your AI product’s ease of use. You can calculate it by asking users to rate how easy it is to complete a specific task with the product on a scale of 1 to 10. This way, you are better able to determine areas that need improvement.

Time to Market

Time to market is a measure of the amount of time it takes to launch a new AI product, from the initial concept to the time it is released into the market. This metric is vital to the development of any AI project due to the market’s increasing competitiveness.

In most cases, launching a product ahead of your competitors gives you a foothold in the market. Additionally, taking too long to launch a product may render it obsolete by the time it lands on the market.

Product developers typically measure time-to-market metrics by measuring the time it takes to complete each milestone in the product’s development. They can also get a rough estimate by comparing how much time it took for competitors to develop and launch similar products in previous developments.

Benefits of using project management metrics and KPIs in AI development

Project management metrics and KPIs are essential in AI development because they provide measurable goals and an effective framework for tracking AI projects [6]. Some of the most notable benefits of using KPIs and metrics include:

Providing measureable goals

KPIs and metrics provide measurable goals that can be used to track progress and evaluate success. This helps project managers work towards a clearly defined set of objectives and measure the impact of their work.

Data-driven decision making

By evaluating data from KPIs and metrics, project managers can better understand the areas within the project that need improvements, make necessary changes, and optimize development efforts.

Increased efficiency

KPIs and metrics can help identify inefficiencies and potential bottlenecks in the development process. For instance, by measuring the time and resources spent on different tasks throughout the development process, project managers can identify areas where they need to streamline processes and increase efficiency.

Greater accountability

Metrics and KPIs help improve accountability within the development team. By setting goals and tracking progress, team members are more likely to take accountability for their actions and work more effectively towards meeting their goals.

Improved customer satisfaction

KPIs and metrics can be used to track customer satisfaction with AI products. By keeping track of various metrics like conversion and user engagement metrics, project managers can determine whether the product is meeting customer needs and expectations. They can then adjust the product accordingly to meet user expectations, which ultimately leads to improved customer satisfaction.

Final thoughts

Product management metrics and KPIs are vital elements of any AI product development process. The intricate nature of AI product development, coupled with the ever-changing market dynamics and customer expectations, necessitates effective systems to set objectives, track progress, and measure the product’s impact.

By leveraging product management metrics and KPIs, project managers can make informed, data-driven decisions and optimize their development processes. With that said, the type of product development KPIs and metrics you choose to use ultimately comes down to the nature of your project and your business’s goals. Also, see our AI consulting services to find out more.


[1] Product Metrics Matter. URL: Accessed March 14, 2023
[2] Customer Engagement. URL: Accessed March 14, 2023
[3] Customer Retention Metrics. URL: Accessed March 14, 2023
[4] How to Measure Conversion Performance. URL: Accessed March 14, 2023
[5] Customer Satisfaction Metrics. URL: Accessed March 14, 2023
[6] Don’t Overlook the Importance of KPIs in AI ML Projects. URL: Accessed March 14, 2023


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