Project estimation is the backbone of proper project planning and execution. Using the right project estimation techniques helps managers predict the time, cost, and resources needed to complete a project. 

Although there are several project estimation techniques, a parametric approach is an excellent solution for managers who value accuracy. Parametric estimating in project management is a technique used to calculate the expected amount of financial resources or time required to complete a project or portion of a project. 

Parametric estimating strikes a balance between analogous and bottom-up estimation methods, providing reliable figures on which to base initial project estimates. It does not focus on every single task to be performed in a project but on the most important ones, allowing for faster estimating.

This article discusses what is parametric estimating, how it is used in project management, examples, benefits, and how you can start using the technique in your projects.

What is parametric estimating?

Parametric estimating is a statistical and accuracy-based technique for calculating the time, cost, and resources needed for project success. Combining historical and statistical data, parametric estimating uses the relationship between variables to deliver accurate estimations.

Often used during a project or in the project planning phase, parametric estimating applies a formula or algorithm for making these calculations, using the specific cost or time needed to implement and finish a project or task.

The parametric estimating formula looks like this:

E_parametric = A_old / P old x P curr,


E_parametric = parametric estimate

A_old = historical amount of cost or time

P_old = historical value of the parameter

P_curr = value of the parameter in the current project

Once you have determined your formula, you can use it as a template to model future project estimations, making adjustments to variables as needed in subsequent projects. 

Analogous and parametric estimating

Analogous and parametric estimating are both popular estimating techniques. In analogous estimating, you draw data from similar past projects to determine the duration, cost, and resources needed for a new or ongoing project. In this case, the emphasis is wholly on the historical data. 

Analogous project estimates are calculated by taking values from past projects with similar scope and applying them to the current project, hence the root word: analogy. People understand concepts and plans better with examples, so the analogous estimation technique relies on references from past project activities and outcomes.

On the other hand, parametric estimating goes beyond analogous estimation by being algorithmic and setting new quantitative parameters. This technique relies on historical data like analogous estimating, but it also considers differences between new and old projects through unit-wise calculations of cost, duration, and other variables.

The Project Management Institute describes it as using "a statistical relationship between historical data and other variables (e.g., square footage in construction) to calculate an estimate for activity parameters, such as duration, cost, and budget.”

In summary:

  • Analogous estimating focuses on historical data, e.g., Project B is the same size as Project A, so it will take a similar amount of time, cost, and resources to deliver Project B as it did to complete Project A
  • Parametric estimating considers new parameters, e.g., Project A required x amount of money and x team members to deliver x project scope. Based on the Project B scope, you calculate new project requirements in terms of the variables, money, and personnel

Parametric estimating example

The following examples help to create a clearer picture of parametric estimating techniques.

Example 1

In a previous agriculture project, it took one hour to prepare the land and plant thirty seeds. How many hours will it take to plant 120 seeds on the same land? 

Using the formula above:

  • A_old = 1 hour
  • P_old = 30 seeds
  • P_curr = 120 seeds

It will take 4 hours to cultivate and plant 120 seeds.

However, parametric estimating is often more complicated in practice, which is why we formulate algorithms.

Example 2

You're the project manager for a fundraising campaign to help girls in the Bronx embrace software development. You're in charge of advertising, donations, and organizing the fundraising event.

For advertising, you estimate the cost of flyers and how long they would take to be delivered. If you need five hundred flyers and they cost $.30 each, this adds up to a total cost of $150. A similar previous campaign prepared 200 flyers in five days. So, you estimate it would take ten days to receive the flyers for the current campaign.

The printer tells you it would take the same time to print 500 flyers as it would take 200. You then adjust the estimated time frame for the flyers to be delivered to seven days.

For donations, you note that it took eight weeks to raise $10,000 in a previous campaign using one fundraising channel. The goal this time is to raise $20,000 with multiple fundraising channels, including direct physical donations, Facebook fundraising, and an online donation platform for charities.

You calculate that since it took eight weeks to raise $10,000, this current project would take 16 weeks using the same parameters. However, because the fundraising channels are different, the current project timeline may be shorter than 16 weeks.

How is parametric estimating used in project management?

To use parametric estimating in project management, first break your project plan into sections. For example, a new product requires ideation, market research, business strategy, prototyping, market entry plans, and commercialization.

Follow up by establishing parameters, i.e., the unit of work or resources required for each section. Parameters like labor, supplies, and equipment will be needed for a construction project. 

Next, calculate the cost of each unit of work. You can use historical data from previous projects or get market or industry data from publications or public databases. Use reliable data to increase the chances of accurate estimation. 

After costing:

  1. Estimate duration by determining the time required to complete each task and unit of work.
  2. Map out correlations between data obtained and the current parameter values you set.
  3. Make inferences and get expert opinions on the different variables before creating your estimates.

Parametric estimating in complex projects

Parametric estimating can be used in complex and diverse projects. Depending on how meticulous and in-depth you want your project planning to be, you can perform a simple parametric estimation or develop algorithms for more complex estimating. This is done by performing a regression analysis.

Regression analysis is used to establish the statistical relationship between two variables to determine if one variable can predict another dependent variable. The stronger the relationship between the two variables, the greater the accuracy in predicting their relationship. 

For example, you might predict a connection between how many people donate and the season of the year or the relationship between software updates and in-app activity. Regression analysis helps to quantify the relationship used to develop algorithms.

The Ultimate Guide to Parametric Estimating in Project Management 2
Photo by Towfiqu barbhuiya on Unsplash

The advantages of parametric estimating

Parametric estimating is beneficial for several reasons. As an accuracy-based technique, it considers many factors when developing estimates and wields analytical power.

Below, we share some advantages of adopting parametric estimating techniques in project management.


When it comes to estimating financial resources and time, parametric estimating is highly accurate. It considers historical data you can validate and is more reliable than analogous, bottom-up, and top-down estimating techniques. Parametric estimating takes many factors into consideration, making it flexible yet precise.


The beautiful thing about parametric estimating is how you can reuse it for similar projects and activities. If the project is to be run frequently, parametric estimating is great for repeatedly calculating your task-level costs and time. Data quality improves with each project, as the forecast formula and algorithm learn continuously, creating a reliable model.

Stakeholder involvement

The algorithmic nature of parametric estimating helps to get stakeholder involvement and support. Parametric estimating’s accuracy lends credibility that often leads to budget approval and stakeholder buy-in. In addition, the scalability of algorithms and calculations creates confidence in the project team.

Applicable throughout the project life cycle

While parametric estimating works best in the planning phase of a project, it can work well at any project stage. Parametric estimating is efficient and reduces the need for the granular detailing of the bottom-up approach. It gives coherence to a project at the start and throughout the life cycle, especially when it gets complex.


Another advantage of parametric estimating is how it accounts for historical and current data differences. Manual adjustments to the calculated results to account for differences between the historical and current projects can help address the weaknesses of a model.

What are the disadvantages of parametric estimating?

Despite the many advantages, parametric estimating has its challenges.


Parametric estimating can be time-consuming and costly, especially when planning or running a complex project. 

Quality of data

Availability or quality of historical data may be a problem, as external data can be skewed and difficult to verify. False data gives a false sense of accuracy, which can be damaging to a project.

Difficult to account for certain variables

If certain variables are not managed and measured, it's impossible to rely on the data. Variables such as customer and employee experience, new hires' learning curves, or even environmental factors may not reflect accurately. 

No room for creativity

Parametric estimation is not recommended for tasks that produce intangible outputs. For instance, using parametric estimation for coding may be inaccurate, as each line of code may require vastly different amounts of thought and creativity.

To negate the disadvantages of parametric estimating, combine it with other estimating techniques. Define which sections of your project you have trouble estimating accurately. Time? Learning curve? Cost? Then apply another estimating technique to resolve that problem.

How to improve your parametric estimating techniques

To improve your parametric estimating technique, ensure that the components that make up your technique are correct and that the determinants fed into your estimate model are accurate.

You have a good foundation for your estimate when sufficient historical data is available and a proven correlation exists between the parameters and their estimated values. Look out for the authenticity and accuracy of the following components:

  • Historical data: Historical data is crucial to making parametric estimates. It is essential to get historical data from the right sources. Use data from previous projects handled by your team or organization. Another historical data resource can be the parametric values of projects external to your organization or public datasets. Ensure you confirm the context of any external data so you don't use unreliable data. 
  • Parameters: Identify the parameters by which each unit of work will be measured. Determine each unit to be produced and compare it with the resource usage of a similar past project. To improve your parametric estimating techniques, match your current parameters with historical data to calculate the required estimates.
  • Variables: What are the variables to be considered in your project execution? These variables connect your historical data, set parameters, and the resulting estimate. Variables, e.g., cost per 30-seconds of radio advertising, are needed to calculate an estimate for activity parameters, such as cost, budget, and duration.

Parametric estimating shines when the right input data, variables, and parameter values are used in estimating. Get these right, and you get precise estimates that help you produce the best project outcomes.

How Wrike helps with parametric estimating in project management

A project estimation technique like parametric estimating helps build a project management model that can be used repeatedly. You can also adopt project management software like Wrike to track project estimates and assumptions against actuals, getting your job done more efficiently.

Wrike provides a secure, centralized workspace for all your projects, making it easy for managers and teams to retrieve, evaluate, and make decisions based on historical and current data. 

Managers can dig into team analytics to estimate how long it takes for team members to complete a task and plan future projects with precise data points on the team, resources, and finances. You can communicate and share these numbers with invited team members and stakeholders within Wrike. 

Having a good grasp of what you need to achieve and replicate successful project outcomes is crucial for success as a project manager. Parametric estimating provides an excellent approach for creating effective project plans, maximizing resources, and achieving success. 

Sign up for a two-week free trial of Wrike to set up your system and gain clarity and confidence in your project estimation, planning, and execution.