> ## Documentation Index
> Fetch the complete documentation index at: https://moengage.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How to Use Pivot Tables to Answer Common Business Questions

> Pivot table layouts for ecommerce, banking, insurance, investing, media, and marketing teams, built on Behavior and Session and Source analyses.

Use these examples to build pivot tables that answer common business questions. Each example lists the analysis to run and where to place each column in the pivot. The event and attribute names are illustrative, so replace them with the ones your workspace tracks.

For how pivot mode works, see [Pivot Tables](/docs/user-guide/analyze/analytics/getting-started-with-analytics/overview#pivot-tables).

## Prerequisites

* The events and attributes in each example, or their equivalents, are tracked in your workspace.
* You're familiar with [Behavior analysis](/docs/user-guide/analyze/analytics/behavior/behavior-analysis), including **Split by** and **Group by user property**.

## Ecommerce: Find Which Categories Sell on Which Platform

A merchandising team wants to know whether some product categories sell better on the app than on the website, so they can decide where to promote each category.

**Analysis:** In Behavior, select the **Order Placed** event, split it by **Category** and **Platform**, choose **Total events**, and set the duration to the last 7 days.

| Zone | Place |
| :- | :- |
| **Row Groups** | **Category** |
| **Column Labels** | **Platform** |
| **Values** | The dates of the week, with **sum** |

**Read the result:** Each row is a category, and each date is split into one column per platform. The **Total** row shows each platform's daily orders across all categories. A category with high web orders and low app orders is a candidate for an app-only campaign.

To compare order value instead of order count, change the analysis option to **Aggregation**, select **Sum** of the **Order Value** attribute, and keep the same layout.

## Banking and Lending: Track Applications by Product and Customer Tier

A lending team wants to see how loan applications trend week over week for each loan product and customer tier.

**Analysis:** In Behavior, select the **Loan Application Submitted** event, split it by **Loan Type**, add **Customer Tier** as a **Group by user property**, choose **Total events**, set the granularity to weekly, and set the duration to the last 12 weeks.

| Zone | Place |
| :- | :- |
| **Row Groups** | **Loan Type**, then **Customer Tier** |
| **Values** | The weekly date columns, with **sum** |

**Read the result:** Collapse each loan type to see its weekly applications, then expand it to see which customer tiers drive the trend. A drop in one tier while the others stay steady points to a targeting or eligibility issue rather than a seasonal dip.

## Insurance: Compare Policy Purchases by Plan and Acquisition Channel

A growth team wants to know which acquisition channels bring in buyers for each insurance plan.

**Analysis:** In Behavior, select the **Policy Purchased** event, split it by **Plan Type** and **UTM Source**, choose **Unique users**, set the granularity to monthly, and set the duration to the last quarter.

| Zone | Place |
| :- | :- |
| **Row Groups** | **UTM Source** |
| **Column Labels** | **Plan Type** |
| **Values** | The monthly date columns, with **sum** |

**Read the result:** Each row shows the plan mix a channel brings in each month. A channel that brings many buyers for low-cost plans but few for premium plans may need different creative or landing pages for the premium segment.

## Investing and Fintech: Compare Trade Size by Asset and Platform

A product team at an investing app wants to compare the average trade size per asset on each platform.

**Analysis:** In Behavior, select the **Trade Executed** event, split it by **Asset** and **Platform**, choose **Aggregation** with **Average** of the **Trade Amount** attribute, and set the duration to the last 7 days.

| Zone | Place |
| :- | :- |
| **Row Groups** | **Asset** |
| **Column Labels** | **Platform** |
| **Values** | The dates of the week, with **avg** |

**Read the result:** Each cell shows the average trade size for an asset on a platform on that day. Large differences between platforms can indicate that high-value traders prefer one platform, which helps you decide where to launch new trading features first.

<Note>
  Pivot isn't available when the analysis aggregation is median or percentile, because aggregating those values again across rows produces misleading numbers. Use sum, minimum, maximum, average, or distinct count instead.
</Note>

## Media and Entertainment: Compare Viewing by Genre and Subscription Plan

A content team wants to see which genres each subscription plan watches most, and how that changes day to day.

**Analysis:** In Behavior, select the **Video Played** event, split it by **Genre**, add **Subscription Plan** as a **Group by user property**, choose **Total events**, and set the duration to the last 14 days.

| Zone | Place |
| :- | :- |
| **Row Groups** | **Subscription Plan**, then **Genre** |
| **Values** | The dates you want to compare, with **sum** |

**Read the result:** Collapse each plan to compare total viewing across plans, then expand a plan to see its genre mix. If free-plan users watch a genre heavily, that genre is a good hook for an upgrade campaign.

## Marketing: Compare Traffic by Source and Medium

A marketing team wants to compare session volume across traffic sources and mediums.

**Analysis:** In Session and Source, choose **Session count**, select **Source** and **Medium** as the source properties, and set the duration to the last 7 days.

| Zone | Place |
| :- | :- |
| **Row Groups** | **Source** |
| **Column Labels** | **Medium** |
| **Values** | The dates of the week, with **sum** |

**Read the result:** Each row shows how a source's daily sessions split across mediums, such as email, cpc, and social. Run the same layout with **Conversion count** to see which source and medium combinations convert.

<Note>
  Avg. Session Duration, Avg. Sessions / User, Avg. Conversions/Session, and Bounce Rate are ratios, so they can't be pivoted. To compare these metrics, use the standard table.
</Note>

## Share the Results

After you build a pivot, you can share it in two ways:

* **Download it:** Click **Download Table** and export it as CSV or Excel. The file keeps the pivoted layout.
* **Pin it to a dashboard:** Pin the table to a custom dashboard so stakeholders see the same layout. To change the layout later, edit it in the source analysis.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.