Tableau Tutorial - Top 6-10 LOD Expressions - Practical Examples (Part 2 - LOD calculations)

Introduction

This tutorial provides a comprehensive overview of the top six to ten Level of Detail (LOD) expressions in Tableau, building on practical examples to enhance your data analysis skills. Understanding these LOD expressions allows you to perform more advanced calculations and gain deeper insights from your data.

Step 1: Comparative Analysis

• Use Case #6 focuses on comparing values across different categories.
• To perform this analysis:
  • Create a calculated field using the FIXED LOD expression to aggregate data at the dimension level you want to compare.
  • Example formula:

    { FIXED [Category]: SUM([Sales]) }
    

  • Visualize the results using a bar chart to display the comparative sales figures across categories.

Step 2: Average of Top Values

• Use Case #7 allows you to calculate the average sales of the top N products.
• Steps to execute:
  • Create a calculated field that ranks products based on sales.
  • Use the RANK function:

    RANK(SUM([Sales]))
    

  • Then, apply a filter to include only the top N ranked products.
  • Finally, calculate the average of these filtered results.

Step 3: Actual vs Target

• Use Case #8 helps identify which products did not meet sales targets.
• To analyze this:
  • Create two calculated fields: one for actual sales and one for target sales.
  • Use a conditional statement to flag products that did not meet their targets:

    IF SUM([Sales]) < [Target Sales] THEN "Below Target" ELSE "Meets Target" END
    

  • Display results in a table for easy comparison.

Step 4: Sales for First and Last Day of Month

• Use Case #9 focuses on analyzing sales data for the first and last days of the month.
• Steps include:
  • Create calculated fields to extract the first and last day of the month:

    DATE(DATETRUNC('month', [Order Date]))
    DATE(DATETRUNC('month', [Order Date]) + INTERVAL '1' MONTH - INTERVAL '1' DAY)
    

  • Aggregate sales data for these specific dates.
  • Visualize the difference in sales using a line chart or bar graph.

Step 5: Return Purchase by Cohort

• Use Case #10 examines customer retention by tracking purchases over time.
• To implement this:
  • Define cohorts based on customer acquisition dates.
  • Create a calculated field to track returning customers:

    IF DATEDIFF('month', [First Purchase Date], [Order Date]) > 1 THEN "Returning" ELSE "New" END
    

  • Use a cohort analysis chart to visualize how many customers return over time.

Conclusion

In this tutorial, we covered the implementation of key LOD expressions in Tableau, focusing on comparative analysis, average calculations, actual versus target comparisons, sales analysis for specific dates, and cohort return purchases. Each example provides a practical application of LOD expressions that can enhance your data analysis capabilities. For further learning, consider exploring the accompanying workbook and related video tutorials.