Category

Correlation Analysis

 

Introduction

Correlation Analysis in Lumenore offers valuable insights into the strength of relationships between selected Key Performance Indicators (KPIs) from your data. This statistical method gauges the degree of association between two or more variables, helping uncover how changes in one variable relate to changes in another. Consider the scenario where you aim to determine the correlation between discount and profit within a retail store dataset. Before initiating the configuration for correlation analysis, it’s crucial to contemplate the context, typically the Key Performance Indicators (KPIs) for which you seek correlation, and then proceed with the remaining process.

To conduct correlation analysis effectively, it is imperative to possess a dataset comprising a minimum of two Key Performance Indicators (KPIs) or metrics (e.g., Revenue, Cost, Discount, etc.) and associated dimensions (e.g., Agent, Customer Segment, or Product) to distribute the data accurately. This arrangement allows for a comprehensive assessment of the relationship between the chosen metrics.

 

Key Concepts:

  • Correlation Coefficient: A numerical value ranging from -1 to 1, indicating the strength and direction of the correlation.
  • Perfect Positive Correlation: Coefficient of 1, signifying both variables increase together.
  • Perfect Negative Correlation: Coefficient of -1, indicating one variable increases as the other decreases.
  • Zero Correlation: Coefficient of 0, suggesting no association between variables.

How to Perform Correlation Analysis in Lumenore

Step 1: After accessing “Do You Know”, select “Correlation”.

Step 2: Click on “Create New Insight Correlation”.

 Step 3: Choose a Schema and click “Next”.

Note: Ensure prerequisites (A KPI, Date, and Attribute) for predictive analysis are met.

Step 4: Select the following:

  • Select correlation metrics: Choose at least two metrics or KPIs for analysis.
  • Select data distribution attribute: Specify an attribute for data distribution (e.g., Date, Agent, Product).
  • Select correlation attribute/group: Choose an attribute for correlation plots (e.g., Product Category, Country, Region).
  • Remove outliers: Remove outliers from the selected KPIs.
  • Do you want to add filters?: Optionally add filters.

Click “Next”.

Note: If you wish to apply filter a window for creating filters appears. Establish filters by groups or conditions as needed.

Step 5: Customize insights narration to define variables used. Then, click on “Save”.

Step 6: Name the insight for future access (default suggestion provided) and save it.

Step 7: A new window appears; click “Execute Now” to generate insights.

 

Output (Insights)

For clarity, an insight for correlation analysis has been created using profit & sales (correlation metrics), country (data distribution attribute), and product category (correlation attribute/group). The chart displays various colored dots representing correlation.