Posts

The Hidden Hero of Data Analysis: The Mode (Part 1) : Moving Beyond the "Average Trap" to Read Your "Real Customers"

Image
In the world of data analysis, the Mean (Average) is often overrated, and the Mode is frequently undervalued. But the reality of business is driven not by the abstract 'average customer,' but by the 'actual majority of customers' right in front of us. This series is structured into three parts: Part 1 (This Article) : A deep dive into the statistical essence and business value of the Mode. Part 2 (Next Up) : Detailed practical DAX implementation methods for calculating the Mode in Power BI. Part 3 (Following) : A systematic guide to practical DAX implementation and visualization strategies using Power BI, complete with examples. Specifically, we’ll explore the 'structural truth of distribution' that the Mean and Median often conceal, and how this leads to superior strategies for CRM, Sales, and Inventory Optimization.   1. Redefining the Mode: The 'Real-World Mainstream,' Not the Mathematical Center In statistics textbooks, the Mode is simply defin...
Post a Comment
Read more

Power BI DAX Integrated Analysis to Fix the Average Trap (Part 1-3): Segment Efficiency Integrated Analysis: Strategic Decision Report Based on EI-M and EI-R (A Power BI + DAX Approach)

Image
This article provides practical insights utilizing Power BI and DAX to quantitatively assess the management efficiency and structural risk of customer segments, translating directly into strategic decision-making. Our goal is to move beyond simple sales volume and simultaneously understand the typical patterns and stability of customer behavior to optimize resource allocation and marketing strategy. Previous analysis used the Power BI Box & Whisker with Points chart to visualize purchasing behavior across segments and identified stable purchasing groups through the Median and IQR. However, that approach had limits in explaining the structural value and risk of extreme customer groups like VIPs and Regulars. (If you missed it, please check out below first) Power BI DAX Integrated Analysis to Fix the Average Trap (Part 1-2): Median-driven Segmentation Strategy using Power BI's Box and Whisker Chart Therefore, this report integrates an analysis of internal structural stability (...
Post a Comment
Read more

Power BI DAX Integrated Analysis to Fix the Average Trap (Part 1-2): Median-driven Segmentation Strategy using Power BI's Box and Whisker Chart

Image
1. Overview of Median Analysis In our previous discussion, we covered how the overall average can be distorted by a few high-value customers (Outliers). (If you missed it, please check out below first) Power BI DAX Integrated Analysis to Fix the Average Trap (Part 1-1): Using MEDIANX and MODEX to Find the 'Truth the Mean Hides' (And you can read next below) Power BI DAX Integrated Analysis to Fix the Average Trap (Part 1-3): Segment Efficiency Integrated Analysis: Strategic Decision Report Based on EI-M and EI-R (A Power BI + DAX Approach) Today, we will analyze strategies based on realistic purchasing behavior, focusing on the Median for each segment. We will also utilize Power BI’s Box and Whisker with Points chart to visualize each segment's median and variability (IQR: Interquartile Range) . 1.1 Summary of Segment Purchasing Distribution and Variability The table below shows the calculated Q1, Q2, Q3, and IQR results by segment. These metrics go beyond a simple compa...
Post a Comment
Read more

Power BI DAX Integrated Analysis to Fix the Average Trap (Part 1-1): Using MEDIANX and MODEX to Find the 'Truth the Mean Hides'

Image
The first step in data analysis is usually calculating the Mean (Average). This simple, intuitive figure is commonly used as a benchmark for setting KPIs (Key Performance Indicators) or strategic planning. However, this average has a fatal flaw: the 'Average Trap.' Because the mean is calculated by dividing the total sum of data by the count, its center can be significantly distorted by a few Outliers (extreme values). Especially in a business environment, a handful of VIP customers or one-off, huge transactions can unrealistically inflate the overall average, leading to the error of obscuring the realistic spending level of the majority of customers. To correct this average distortion and find the true center of the data, the following five key analysis methodologies are used: Median and Mode: Used to diagnose distortion by outliers and grasp realistic purchasing power levels (The focus of this analysis). Trimmed Mean: Used to correct the center by removing outliers. Standa...
Post a Comment
Read more