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The Customer Segments Comparison report lets you analyze every customer segment side by side using key performance indicators. Use it to spot patterns across audiences, see which groups behave differently from each other, and figure out which engagement strategies work best for which customers. You can find this report under Analytics → Core → Customer Segments Comparison.

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What the report compares

The report compares the composition of selected segments across the following parameters:
  • Age
  • Number of purchases
  • Total purchase amount
  • Gender
  • Average order value (AOV)
Each parameter is visualized as a distribution across the segments you choose, so you can see at a glance how one audience differs from another on a given dimension.

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How to read the report

1

Select a parameter

Pick the metric you want to analyze — for example, gender distribution, AOV, or total purchase amount. The report will redraw the comparison around this dimension.
2

Select the segments to compare

Choose which customer segments you want to put side by side. You can compare two segments or several at once.
3

Check statistical significance

Validate whether the differences you see between segments are statistically significant or could just be noise. Only act on differences that pass the significance check.
4

Review the metric definitions

Use the glossary below to confirm exactly how each metric is calculated before drawing conclusions.

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Practical use cases

Segments like iOS users and Android users often have different average order value distributions. Once you see the AOV gap, you can target each group with product recommendations at the price point they actually buy at — premium picks for one audience, value picks for the other.
A “browsed category X” segment and a “purchased category X” segment usually differ noticeably in age, gender, and AOV. Comparing them tells you who is window-shopping vs. converting, so you can adjust email copy, creative, and paid media targeting accordingly.
Compare loyalty program members against non-members on purchase frequency and total purchase amount. If members aren’t outperforming non-members among your high-value customers, the program isn’t pulling its weight — and the report shows you that gap directly.
Before you trust the results of a test, use the report to confirm that the control and test groups are evenly distributed across age, gender, and purchase behavior. If the groups are skewed at baseline, your test results will be skewed too.

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Metric glossary

  • Age — the distribution of customers in each segment across age brackets, based on the date of birth stored in the customer profile.
  • Number of purchases — how many completed orders each customer in the segment has placed. The report shows the distribution of customers by purchase count.
  • Total purchase amount — the lifetime sum of all orders per customer, in dollars ($). The report shows how customers in each segment are distributed across spend tiers.
  • Gender — the share of male, female, and unknown-gender customers in each segment.
  • Average order value (AOV) — total purchase amount divided by number of purchases, per customer. The report shows the distribution of customers across AOV buckets.
Customers with missing data on a given parameter (for example, no date of birth or no gender on file) are grouped into an “unknown” bucket so the totals across segments stay consistent.
Always confirm statistical significance before you act on a difference between segments. A visible gap on the chart doesn’t automatically mean the segments behave differently — small segments in particular can show large swings that aren’t meaningful.