How to Analyze a Pivot Table in Excel

A fresh pivot table is like a blank canvas - it summarizes your data, but the real power comes from how you analyze it. Beyond simply creating a summary, you can use a pivot table to ask questions and find answers hidden in your spreadsheet. This guide will walk you through the most effective ways to analyze a pivot table in Excel, turning your raw numbers into clear, actionable insights.

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A Quick Refresher: The Anatomy of a Pivot Table

Before diving into analysis, let’s quickly review the four main areas of a pivot table's field list. Understanding these is essential for manipulating your data effectively. We'll use a simple dataset of quarterly sales as our example, with columns for Region, Product, Quarter, and Sales Amount.

• Rows: Fields you place here appear as rows on the left side of your pivot table. For example, dragging the Region field here will list each region in its own row.
• Columns: Fields here appear as columns across the top. Placing the Quarter field here would create a column for Q1, Q2, Q3, and Q4.
• Values: This is where the numbers go for calculation. Dropping the Sales Amount field here will calculate the sum, count, or average of sales.
• Filters: This area lets you apply a high-level filter to your entire report. For instance, you could place the Product field here to filter your entire table to show sales for just "Laptops."

Once you're comfortable with where to place your fields, you can move on to the actual analysis.

1. Start with Sorting and Filtering

The simplest yet most powerful first step in pivot table analysis is sorting and filtering. This lets you immediately identify top performers, outliers, and specific segments of your data.

Sorting to Find the Top and Bottom

By default, your pivot table might display data alphabetically. To find immediate insights, you’ll want to sort based on your values.

Let's say your pivot table shows Regions in rows and SUM of Sales Amount in the values. To find your top-performing region:

1. Right-click on any number in the SUM of Sales Amount column.
2. Go to Sort.
3. Choose Sort Largest to Smallest.

Instantly, your table reorders to show the highest-grossing region at the top. You can just as easily sort smallest to largest to identify underperforming areas.

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Using Filters to Focus Your View

Sometimes you only need to see a slice of your data. The filters built into the row and column headers make this simple.

• Row/Column Label Filters: Click the small filter dropdown icon next to your Row Labels or Column Labels header. From here, you can uncheck items one by one or use "Value Filters" to show items based on criteria (e.g., "Top 10" or figures "Greater Than" a certain value).

Pro Tip: Use Slicers for Interactive Filtering

Slicers are user-friendly buttons that make filtering faster and more visual. They are especially useful if you plan to share your report with others who might not be experienced with pivot tables.

To add a slicer:

1. Click anywhere inside your pivot table to activate the PivotTable Analyze tab in the ribbon.
2. Click on Insert Slicer.
3. A dialog box will appear. Check the box for the field you want to filter by (e.g., Product).
4. Click OK.

Now you have a floating menu of buttons for each product. Clicking a button on the slicer will instantly filter the entire pivot table, making it much easier to explore your data.

2. Change Calculation Methods for Deeper Insights

Excel defaults to SUM for the Values field, but that's just the beginning. You can change this calculation to answer different types of questions.

To change the calculation:

1. Click the dropdown arrow on the field in the Values area of your pivot table field list.
2. Select Value Field Settings.
3. In the Summarize value by tab, choose a new calculation type.

Here are a few useful options and the questions they help answer:

• Count: Answers "How many?" Instead of summing up sales, it counts the number of transactions. Example: How many individual sales did each region have?
• Average: Answers "What is the typical value?" Example: What was the average sale size per product category?
• Max: Finds the highest single value. Example: What was the single largest sale in each quarter?
• Min: Finds the lowest single value. Example: What was the smallest deal we closed?

By simply switching from SUM to AVERAGE, you might discover that your region with the highest total sales actually has a very low average sale size, meaning they rely on volume, not high-value deals.

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3. Use "Show Values As" to Analyze a Pivot Table with Percentages and Ranks

This is where real analysis happens. "Show Values As" transforms your raw numbers into relative figures like percentages, allowing you to understand proportionality and contribution.

You can access this feature in the same Value Field Settings window, but this time, you click the Show Values As tab.

Common "Show Values As" Options

• % of Grand Total: This is perfect for seeing how each part contributes to the whole. For example, if you have Products in your rows, this will show what percentage of total company revenue each product line is responsible for.
• % of Column Total / % of Row Total: This is useful for more drilled-down views. If you have Regions in rows and Product Categories as columns, % of Column Total would tell you which region is most important for a specific product category.
• Difference From: This option lets you compare data against a specific baseline. You could compare every region's sales to a "base" region like your headquarters to see how they stack up.
• Running Total In: Excellent for analyzing cumulative growth over time. If your columns are months, this setting will show the year-to-date sales total in each month, helping you track progress toward a quarterly or annual goal.

4. Group Data to Uncover Trends

One of the most valuable features for analysis is the ability to group raw data into meaningful categories. This is particularly useful for dates and numbers.

Grouping By Dates

If you have a column of daily sale dates, your pivot table might be too long and messy to read. Excel lets you group these dates automatically.

1. Drag your date field into the Rows or Columns area.
2. Right-click on any of the dates in the pivot table itself.
3. Select Group.
4. A dialog box will appear, letting you choose to group by Days, Months, Quarters, and/or Years. Select Months and Years and click OK.

Your pivot table will transform from hundreds of individual dates into a clean summary showing performance by month and year, instantly highlighting seasonal trends or year-over-year growth.

Grouping by Numbers

You can also group numerical data to create custom "bins." This is great for customer segmentation or price-point analysis.

Imagine you have a list of individual order values and want to group them into price brackets:

1. Right-click on the numerical field in a row or column of the pivot table.
2. Select Group.
3. Enter a starting value, an ending value, and the "By" value (the size of each bin). For instance, start at 0, end at 1000, and group by 100 to create brackets like 0-99, 100-199, etc.

This reveals where the majority of your sales transactions are clustered. Are they small, everyday purchases, or large, infrequent deals?

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5. Add Calculated Fields for Advanced Metrics

Sometimes, your source data doesn't have the exact metric you need, but you can create it directly within the pivot table using a Calculated Field without adding a new column to your raw data.

For example, if you have columns for Sales and Units Sold but not Average Price Per Unit, you can calculate it.

1. Click inside your pivot table, then go to the PivotTable Analyze tab.
2. Click on Fields, Items, & Sets, then select Calculated Field.
3. Give your new field a name, like "Avg Price."
4. In the Formula box, create your formula using your existing fields. It should look like this: ='Sales Amount' / 'Units Sold'. You can double-click the field names in the list to add them to the formula.
5. Click OK.

Your new calculated field will appear in the Values area, giving you an entirely new metric to analyze without ever touching your source data.

Final Thoughts

As you can see, a pivot table is much more than a static reporting tool. By combining sorting, flexible calculations, grouping, and calculated fields, you can turn your intimidating spreadsheets into a dynamic environment for discovery and decision-making.

While Excel push-ups are rewarding, the manual work of exporting CSVs from different platforms like Google Analytics, Facebook Ads, and Shopify can consume hours before you even get to build a pivot table. We built Graphed to solve this by connecting directly to all your data sources. Instead of wrestling with spreadsheet formulas, you can simply ask, "What was my cost per acquisition by campaign last month?" and Graphed builds a live, interactive dashboard for you in seconds - no exports or pivot tables required.