Although it may seem that finding the **weighted average** is not an important need, it is useful in different situations and fields. Let’s learn more about the weighted average and how to obtain it.

First, we should know that the weighted average refers to the value we get from a series of numbers representing different quantities. These values are also called weights.

## What is a weighted average?

**The weighted average is the average result of a series of numbers in which some data are given more value than others**. Not all numbers will have the same value to obtain the average.

Weighted averaging is commonly used in situations such as the need for statistical analysis. It is also widely applied to averages in a portfolio of stocks, etc.

Its use is considered important when accounting for differences in data, valuing results, or checking that data is real.

Its use can be perfectly applied to something as important as calculating the cost of a good or item. A weighted average calculation is very effective when the volume of items in a company or industry is very large and difficult to count.

In the case of such a calculation, we can obtain the real cost of the products or goods to be sold.

## Calculation of weighted averages

There are different **formulas for a weighted average calculation**. The first thing to know is that our data will not have the same importance or weight. That is basic since the result of the average is based on being able to discriminate what is the real significance of each asset.

Therefore, the first step in calculating a weighted average is to obtain the importance (weight) of each piece of data or number.

## Determine the weight of the data

While it is relatively easy to calculate the weight of a piece of data in a group of a few numbers, this becomes more complicated when it comes to companies.

To give you an idea: in large statistical sets, it is **necessary to apply random data trees to know the weight of the data**. The fundamental reason for this is that it is needed to ensure that the data is processed in an unbiased way. Bad analysis of the weight of the data eliminates the reliability of the average.

Automated computer programs perform such operations.

## Multiply the weight of the data and obtain the average

Once the weight of each of the numbers is known, it is multiplied by the weight for each unit of data. Subsequently, the weighted value is added up, and the average is obtained.

That could be applied perfectly well, for example, to an academic year in which four exams are given. The first three exams may be offered a certain value, but the fourth, the final exam, may have a higher value.

A higher number of values would be identified at each point to reflect this higher value.

For example:

**The first three exams would score 1 point per a tenth of a point obtained in the exam result.****The last exam would score 2 points per tenth obtained in the exam result.**

There may be a situation in which the calculation is performed on other variables where no other biases are applied—for example, frequency.

If you imagine the frequency of time with which you perform an activity, you can determine the actual weight that each unit of time has and the actual average that applies.

**For example, suppose with one customer you speak for 10 minutes, with another customer you talk for 12 minutes**, and with the last customer, you speak for only 7 minutes. In that case, you can try to give different variables to obtain an average and the real weight of your conversations with customers.

This calculation, for example, is very useful when it comes to commercial analysis: frequency of customers, type of sale, analysis of commercial speech, etc. But, it can also apply in other areas, such as any calculation where the unit is not exactly referenced to one.

For years I have studied American finance regulations. All the information in this blog is sourced from official or contrasted sources from reliable sites.

Salesforce Certified SALES & SERVICE Cloud Consultant in February 2020, Salesforce Certified Administrator (ADM-201), and Master degree in “Business Analytics & Big Data Strategy” with more than 13 years of experience in IT consulting.