Excel MODE Function: A Complete Guide to Finding Most Frequent Values (2025)

Published: December 11, 2024 - 3 min read

Ashley Lenz

The MODE function in Excel helps you identify the most frequently occurring values in your dataset, making it invaluable for analyzing trends and patterns in numerical data. Whether you’re analyzing sales figures, survey responses, or performance metrics, understanding how to use MODE effectively can streamline your data analysis process.

How to Use the MODE Function in Excel

The MODE function comes in two primary variations: MODE.SNGL for finding single most frequent values and MODE.MULT for identifying multiple modes. Let’s explore how to use each version effectively.

Finding Single Mode Values with MODE.SNGL

MODE.SNGL returns the most frequently occurring value in a dataset. If multiple values tie for the highest frequency, it returns the lowest value.

Basic Syntax:

=MODE.SNGL(number1,[number2],…)

Step-by-Step Example:

  1. Create a sample dataset:

Sales Amounts

100

200

100

300

100

200

  1.  
  2. Enter the formula:

    =MODE.SNGL(A2:A7)
  3. Result: 100 (appears three times)

Real-World Example:

=MODE.SNGL(SalesData!B2:B100)

To use this formula, follow these steps:

  • Create a new sheet named SalesData.
  • Enter the dataset in columns A and B of the SalesData sheet.
  • On another sheet (or in the same sheet), use the formula

This formula finds the most common sale amount in your dataset.

Calculating Multiple Modes with MODE.MULT

MODE.MULT returns an array of all values that appear most frequently in a dataset.

Basic Syntax:

=MODE.MULT(number1,[number2],…)

Important: MODE.MULT requires array entry (Ctrl+Shift+Enter in older Excel versions).

Step-by-Step Example:

  1. Create a dataset:

Scores

85

90

85

95

90

85

90

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  1.  
  2. Select multiple cells for output
  3. Enter the formula:

    =MODE.MULT(A2:A8)
  4. Results: 85, 90 (both appear three times)

Real-World Applications of MODE in Excel

Analyzing Sales Frequencies

Example 1: Finding Most Common Order Value

=MODE.SNGL(OrderValues)

Example 2: Multiple Price Points Analysis

=MODE.MULT(PricePoints)

Peak Performance Metrics

Step-by-Step Implementation:

  1. Collect performance data:

Performance Score

85

92

88

92

95

92

  1. Find most common score using the formula:

    =MODE.SNGL(A2:A7)

Combining MODE with Other Functions

MODE with IF Statements

Example: Conditional Mode Calculation

=MODE.SNGL(IF(Region=”North”,Sales,””))

Array Formulas with MODE

Complex Example:

=MODE.MULT(IF(Region=”North”,IF(Sales>1000,Sales,””)))

Essential MODE Function Tips

Handling Special Cases

  1. Blank Cells:
    • MODE ignores empty cells
    • Text values are ignored
    • Zero values are included
  2. Large Datasets:
    • Use named ranges for cleaner formulas
    • Consider performance impact with MODE.MULT
    • Filter data before applying MODE

Format Considerations

  1. Number formatting:
    • Keep consistent decimal places
    • Use appropriate number formats
    • Consider rounding when necessary

MODE Function Best Practices

  1. Data Preparation:
    • Clean data before analysis
    • Remove outliers if necessary
    • Verify data types are consistent
  2. Error Handling:
    • Use IFERROR for robust formulas
    • Validate input ranges
    • Document assumptions

Next Steps

Now that you understand how to leverage Excel’s MODE functions, you can apply these techniques to your own data analysis tasks. For more advanced data analysis capabilities, consider upgrading your Excel workflow with Coefficient’s real-time data integration.

Ready to supercharge your Excel data analysis? Get started with Coefficient to connect your spreadsheets directly to your business systems and ensure your MODE calculations always use the most up-to-date data.

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Ashley Lenz Product Researcher @ Coefficient
As a product researcher at Coefficient, Ashley taps into the power of data to create intuitive solutions that save users valuable time. By working closely with users, Ashley helps to uncover key insights that shape product features, enabling teams to streamline workflows and boost productivity. Her passion for data-driven research and optimizing user experiences fuels her work, ensuring the product delivers maximum efficiency and value.
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