Pivot table for marketing data: analyse raw exports fast

A pivot table summarises raw marketing data without a single formula.

A pivot table is an interactive table that groups, filters and compares rows from an export.

You pull data from GA4, your ad platform or your CRM into Excel or Google Sheets.

Within a few clicks you see what’s happening per channel, campaign or period.

The verdict: for most SME analyses you don’t need an expensive dashboard platform. A pivot table is enough, as long as you set it up around leads and revenue, not pageviews.

What exactly is a pivot table?

It’s a tool that turns a long, messy list into the summary you choose.

Imagine an export of a thousand rows with sessions, channels, campaigns and conversions.

On its own, that list tells you nothing.

A pivot table turns it into an overview in seconds, for example the total number of leads per channel.

Raw data only has value once you can read it

Data is often called the new oil, but raw data only has value once you can read it (Wired).

That’s where the pivot table comes in. Microsoft’s own documentation calls it “a powerful tool to calculate, summarize, and analyze data” that reveals comparisons, patterns and trends.

Why it suits B2B

Your traffic is smaller than a webshop’s, your sales cycles are longer and every signal counts.

Good data analysis therefore doesn’t start with an expensive platform.

It starts with slicing the data you already have in a smart way.

If you’d like this fully set up and connected to your goals, our data analytics approach can help.

Which marketing data do you put into a pivot table?

Practically any export where each row holds one event or record. The most useful sources for B2B:

• GA4: sessions, users and conversions per channel, source or landing page
• Ad platforms: impressions, clicks, costs and conversions per campaign
• Your CRM: leads, deals and revenue per source, period or account manager
• Email: opens, clicks and sign-ups per campaign

For GA4, the traffic acquisition report is the usual starting export.

For Google Ads and LinkedIn Ads, export the campaign report; our guide to Google Ads reporting shows which columns matter.

Tag your links first

A pivot table can only group what’s labelled.

If your campaigns lack UTM parameters, they all land in one vague bucket.

The same goes for your CRM.

Our guide to lead source tracking in your CRM keeps the source on every lead.

The golden rule for clean exports

Make sure the first row holds clear column headers and every row underneath is one single measurement.

Microsoft puts it plainly: your data “should be organized in a tabular format, and not have any blank rows or columns.”

Messy source data is a bigger problem than any tool choice.

Our piece on data quality covers the usual culprits.

How do you build a pivot table step by step?

It works almost identically in Excel and Google Sheets. Here’s the step-by-step plan in Excel.

Step 1: Export and paste your data

Pull your CSV from GA4 or your ad platform and open it in a blank worksheet.

Make sure the column headers are at the top.

Step 2: Check the columns

Are amounts showing as plain numbers instead of currency?

Select the cells and pick the right format under the Home tab.

Delete empty rows and columns.

Step 3: Select your data

Highlight all cells including the headers. You don’t have to include every column, only what you want to analyse.

Step 4: Insert the pivot table

Go to the Insert tab and click PivotTable. Let it open in a new worksheet, which keeps things tidy.

Shortcut: Insert, then Recommended PivotTables lets Excel suggest layouts. Microsoft notes this is “available only to Microsoft 365 subscribers.”

Step 5: Drag the fields

You get four zones: filters, columns, rows and values. Put “Channel” under rows and “Conversions” under values, for instance.

Here’s what that field list looks like:

Step 6: Choose the calculation

By default Excel adds up (sum). Want an average or a count? Click the value field and adjust the setting.

What you get

Where you first had a thousand rows, you now see one line per channel with the matching totals, including a grand total.

For example, say your export holds a quarter of GA4 data. The pivot output could look like this:

Want to look at it per month instead? Untick the one field and drag “Month” into the rows.

The numbers recalculate immediately. You don’t have to start over.

The same in Google Sheets

Google’s help page uses the same logic: select the data, go to Insert, then Pivot table.

Then add fields to Rows or Columns and a metric to Values.

Google notes that “each column needs a header.”

The editor sometimes suggests a pivot table based on your data. You’ll find those under “Suggested”.

How do you use a pivot table to see what leads to customers?

Put conversions next to every channel, not just sessions. That’s the difference between collecting data and using it.

It’s tempting to look at the big numbers: which page got the most visitors, which post the most likes.

But those are vanity metrics.

For your business, what counts is what leads to leads and revenue.

Sessions and conversions tell different stories

Here’s a real example from our own GA4. We filtered the Traffic acquisition report on one source, chatgpt.com:

GA4 Traffic acquisition in our own property, last 90 days, October 2026.

That source brought 0.22% of sessions but 7.14% of key events. Put side by side, the gap is obvious:

Watch out: that’s one key event on 58 sessions. Small numbers swing hard, so treat it as a reason to look closer, not as proof.

Three set-ups that answer the real question

So set up your pivot table to answer “what brings customers?”. Concretely:

• Channel vs conversions: channels in rows, conversions in values, not sessions
• Campaign vs costs and leads: lots of clicks but few leads costs money
• Period vs revenue: compare months or quarters after a change

Optionally add a column with your conversion rate per channel.

Then you see not which channel is the busiest, but which one delivers.

Need a quick check on a single number? Our conversion rate calculator does it in seconds.

Filter with intent

Show only paid channels, or only one campaign, and watch how the numbers shift.

Divide costs by leads per campaign and you have your cost per lead in the same table.

Just as with setting up your marketing KPIs, the rule is: measure what brings you closer to a customer, not what happens to score well on a screen.

An honest analysis also dares to conclude that a channel delivers nothing.

Do you need a dashboard or an expensive tool?

Usually not, certainly not as a smaller team.

A pivot table in Excel or Google Sheets covers the vast majority of the analyses an SME needs.

You can group, filter, compare and even add a chart.

For a monthly review of your channels that’s more than enough.

How much data a pivot table handles

For most SME exports, size isn’t an issue. Excel’s specifications allow 1,048,576 rows per worksheet.

In practice you’ll notice Excel slowing down at hundreds of thousands of rows.

At that point a database or dashboard makes more sense.

When to scale up

A marketing dashboard or a paid platform only becomes interesting when:

• you rebuild the same analysis every week
• several people need to look at it at once
• you want to connect your sources automatically

Until then, a tool like that mostly costs money and setup time without better decisions.

Start small. As soon as manual exporting holds you back, that’s the signal to scale up, for example to a free Looker Studio report.

How do you present the result?

With one pivot chart per message. Numbers in a table convince no one in a meeting.

A pivot chart turns your analysis into a visual in a few clicks.

Click inside your pivot table, go to the Analyze tab and choose PivotChart.

A clustered column chart works well for putting channels or months side by side.

One message per chart

Keep it simple: one clear message per chart.

Good data visualisation makes a trend visible at a glance.

Your conversation is then about the numbers instead of about reading them.

Want ready-made options? Compare the data visualisation tools we’ve reviewed.

Frequently asked questions

Does a pivot table work in Google Sheets too?

Yes. Select your data, go to Insert and choose Pivot table. The zones have slightly different names, but the principle is identical to Excel.

How much data can a pivot table handle?

For most SME exports that’s not an issue. Only at hundreds of thousands of rows will you notice Excel slowing down; at that point a database or dashboard makes more sense.

Do I need to know formulas?

No. That’s exactly its strength. You drag fields and pick a calculation, the tool does the maths for you.

How often should I refresh my pivot table?

With a new export you right-click the table and choose Refresh, or you paste fresh data into the same range. Refresh All on the PivotTable Analyze tab updates every table at once. For B2B, a monthly round is usually enough.

Does this replace GA4 or my ad platform?

No, it complements them. You use the export from those tools and slice it your way, separate from the standard reports.

Get started with your own data

Start with one export, group it by channel or campaign, and steer on what leads to customers.

Would you like your tracking, analysis and reporting set up so decisions rest on numbers instead of gut feeling? Contact us and tell us which report you rebuild every month.