Mean Absolute Deviation

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What is a Mean Absolute Deviation in E-commerce?

Mean absolute deviation (MAD) is a forecasting method used in e-commerce to measure the difference between each data point and the mean. MAD is used to understand how close the data points are to the mean, which can give insights into the data distribution. It is also often used to compare different datasets, as it is more robust to outliers than other measures of variability.

Significance of Mean Absolute Deviation in E-commerce Platform 

Mean absolute deviation (MAD) is a useful tool for measuring the performance of an e-commerce platform. It measures the variability of data from its mean and can help identify outliers, which can be indicative of unusual trends in the data. The benefits include-

  • MAD is considered to be more accurate than other methods as it takes into account all the data points, allowing a more precise prediction.
  • It is helpful in detecting outliers that may affect the accuracy of the results.
  • Mean absolute deviation is easier to compute than other methods as it involves simple calculations.
  • MAD can be used to compare results between different e-commerce platforms. This is helpful in understanding which platform offers the best prices.

Prerequisites to Calculate Mean Absolute Deviation and How It Works

The prerequisites to calculate MAD are –

1. Data of customers who have purchased from the e-commerce store.

2. A calculator or spreadsheet software to calculate the mean and absolute deviation of the data.

3. A good understanding of basic statistics and concepts related to mean absolute deviation.

Formula to calculate MAD 

MAD = Σ |x – μ| / n


x = the individual data points 

μ = the mean of the data set 

n = the total number of data points 

Σ = the sum of all the absolute deviations from the mean

Use Case With Mean Absolute Deviation

Using mean absolute deviation, companies can know the accurate difference between actual and fitted data. For instance, there is a set of 5 numbers: 2, 6, 4, 8, 10

The mean of the data set is 6 

The absolute deviations of each data point from the mean are: 

2 – 6 = -4 

6 – 6 = 0 

4 – 6 = -2 

8 – 6 = 2 

10 – 6 = 4 

The mean absolute deviation (MAD) is 

MAD = Σ |x – μ| / n

          = 0 |30- 6| / 5

          =  4.8 

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