---
title: Derived columns
slug: analytics/docs/derived-columns
docTags: 
createdAt: 2026-07-20T15:45:45.582Z
---

A derived column is a computed property linked to a dataset. You define it within Analytics, and it is not present in the source data. You can include various calculations in a derived column, such as joins with other datasets, filters, and aggregations, as long as the result applies to each record in the target dataset.

For example, if you have a *users* dataset and an *events* dataset with a boolean column named *vote*, you can define a derived column in the *users* dataset that counts the total number of *true* votes per user.

The configuration for a derived column is saved within Analytics and accessed through the [Catalog](docId\:eD4_X8KtD2cLqebOWR7Fl). Like other columns in the dataset, you can use a derived column anywhere the catalog is accessible.

A derived column has the following configuration:

- **Dataset** – Associate the derived column with exactly one dataset, which is selected when defining it. It displays in the **Catalog** like any other column, and you can use it for any analysis with catalog access. You cannot move a derived column to another dataset.
- **Name** – Ensure the derived column's name is unique across columns (including derived columns and cohorts) within its dataset.
- **Definition** – Specify the computation for a derived column using the block editor.
- **Default aggregation** – Set a default aggregation type, such as count or sum, for contexts where you use the derived column as a measure.

## Create derived columns

1. Click **+** and select **Derived Column**.
2. Choose one of the templates to get started.
   - [Aggregation/First/Last](docId:1wi6-v9k1O0weCWNUPCsS) – Create an aggregate quantity for each actor.
   - [Formula](docId:1wi6-v9k1O0weCWNUPCsS) – Write custom logic using the NetScript formula language.
   - [Conditional on Property](docId:1wi6-v9k1O0weCWNUPCsS) – Assign values based on logical conditions.
   - [Custom](docId:1wi6-v9k1O0weCWNUPCsS) – Calculate values by composing multiple blocks.
   - [Conditional on Aggregate](docId:1wi6-v9k1O0weCWNUPCsS) – Calculate an aggregated quantity per actor and assign each actor to a bucket based on the aggregate value.
   - [Previous/Next Value](docId:1wi6-v9k1O0weCWNUPCsS) – Look up the value of the temporally previous or next event for each actor, optionally filtering events.
   - [Time Since](docId:1wi6-v9k1O0weCWNUPCsS) – Calculate how long ago a timestamp property is.
3. Enter a name and description.
4. Select a dataset for the derived column in the **Column of** field.
5. Decide if you want the derived column to be a measure or attribute.
6. Set the **Default Aggregation**.
7. Use [blocks](docId\:bX_YvRDHmNihcjmYwv0RS) to define the computation for your derived column. Ensure that the resulting value is at the grain of the selected dataset or can be grouped by the selected dataset.
8. Click **Run** to preview the computed values.
9. Click **Save**.

The derived column can now be used anywhere, such as inside exploration templates and the block editor.

![oa-dc-1.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/EOf76gUgq9emX2r2TdMe7-20260720-144927.png)

## Types of derived columns

Analytics offers several templates to help you get started. Choosing a template is the first step in creating a derived column, and you can change it at any time without losing your work.

### Aggregation/first/last

Calculate an aggregate quantity for each actor. For example, you can create a derived column to calculate the signup date.

1. Go to **+ > Derived Column > Aggregation/First/Last**.
2. Enter a name and description.
3. Choose a dataset in the **Column of** field. For this example, select the **Users** dataset.
4. Decide if this is a measure or an attribute. Choose **Measure** for this example.
5. Select the default aggregation from the drop-down list. Choose **avg**.
6. Select the aggregate block's **Aggregator** and a value from the drop-down list. For example, select **min** as the aggregator and set **event\_ts** as the value. Group the results by **Users**.
7. Click **Run**.
8. Click **Save**.

![oa-dc-2.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/zAbLMvLDfDT5sXiLqXPMl-20260720-144927.png)

### Conditional on property

Assign values based on logical conditions. For example, you can create a derived column to determine whether users like or dislike the website's content.

1. Go to **+ > Derived Column > Conditional on Property**.
2. Enter a name and description.
3. Choose the **Content** dataset in the **Column of** field.
4. Select **avg** from the **Default Aggregation** drop-down list.
5. Configure the blocks using an **If/Then/Else** block. Set the condition as follows: if **vote\_avg >= 7.5**, assign the value **Thumbs Up**; otherwise, assign **Thumbs Down**.
6. Click **Run**.
7. Click **Save**.

![oa-dc-3.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/rAF0WrInZ9ez_Td9kqsaf-20260720-144927.png)

### Formula

Write custom logic using the NetScript formula language.

1. Go to **+ > Derived Column > Formula**.
2. Enter a name and description.
3. Choose the **Campaigns** dataset in the **Column of** field.
4. Choose **Attribute** in the **Measure or Attribute** field.
5. Keep the **Default Aggregation** drop-down list unselected.
6. Enter the following formula to calculate the **Numeric ID**:

```auto
id = Data.Campaigns.id;
if not(isnull(id) or strlen(id) == 0) then to_int(id) end
```

7. Add a bin block to categorize types using **Numeric ID** as the value. Create the following three bins:
   - **Bin 1** – Social Media, where x \< 151.
   - **Bin 2** – Organic Search, where 151 \<= x \< 501.
   - **Bin 3** – Paid Search, where x >= 501.
8. Enable the output toggle for this block.
9. Click **Run**.
10. Click **Save**.

![oa-dc-4.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/xOLwdVrPkTBu1D4a6rD7O-20260720-144927.png)

### Custom

Calculate values by composing multiple blocks. For example, you can create a derived column to determine the *incremental campaign revenue*.

1. Go to **+ > Derived Column > Custom**.
2. Enter a name and description.
3. Choose the **Product Events** dataset in the **Column of** field.
4. Choose **Measure** in the **Measure or Attribute** field.
5. Select **avg** from the **Default Aggregation** drop-down list.
6. Add an aggregate block to calculate the number of Ads Viewed. Choose **count** as the aggregator and select the **Product Events** dataset. Group the result by **Campaigns**.
7. Add a formula block to calculate campaign revenue. Use the following formula:

```auto
"Ads Viewed" * Data.Campaigns.cpm / 1000
```

8. Click **Run**.
9. Click **Save**.

![oa-dc-5.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/xnmlJrQalwvBofXxx4LNv-20260720-144927.png)

### Conditional on aggregate

Calculate an aggregate quantity for each actor and categorize them into buckets based on that aggregate value. For example, you can identify high-value app users who actively engage by making purchases or playing content.

1. Go to **+ > Derived Column > Conditional on aggregate**.
2. Enter a name and description.
3. Choose the **Users** dataset in the **Column of** field.
4. Choose **Measure** in the **Measure or Attribute** field.
5. Select **avg** from the **Default Aggregation** drop-down list.
6. Use the **If/Then/Else** block to categorize high-value users. Set the following conditions: if **event\_type** is **Play Content** or **Make Purchase**. Name this block **High Value Users**.
7. Add an Aggregate block. Choose **count** as the aggregator and use the **If/Then/Else** block as the value.
8. Click **Run**.
9. Click **Save**.

![oa-dc-6.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/WprNykk1PPwyE1774Sb7J-20260720-144927.png)

### Previous/next value

Look up the value of each actor's previous or next event, optionally including only certain events. For example, you can find the previous value of the last campaign type in the product events time series.

1. Go to **+ > Derived Column > Previous/Next Value**.
2. Enter a name and description.
3. Choose the **Product Events** dataset in the **Column of** field.
4. Choose **Attribute** in the **Measure or Attribute** field.
5. Keep the **Default Aggregation** drop-down list unselected.
6. Choose **previous** from the drop-down list and choose **Product Events** as the value in the **Previous Value Lookup** block.
7. Choose **Type** ([a custom derived column](docId:1wi6-v9k1O0weCWNUPCsS)) as the value in the **output specific property** field.
8. Add **for each** conditions if necessary. For example, add the **Users** dataset.
9. Click **Run**.
10. Click **Save**.

![oa-dc-7.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/0ttdY6Lb7tLs1FXZr3KSd-20260720-144927.png)

### Time since

Calculate how long ago a timestamp property was. For example, you can calculate the time since the last signup.

1. Go to **+ > Derived Column > Time Since**.
2. Enter a name and description.
3. Choose the **Users** dataset in the **Column of** field.
4. Choose **Measure** in the **Measure or Attribute** field.
5. Select **avg** from the **Default Aggregation** drop-down list.
6. Choose the **Sign Up Date** derived column using the drop-down list in the **Timestamp** block.
7. Enter the logic in the formula block:

```auto
now - Timestamp
```

8. Click **Run**.
9. Click **Save**.

![oa-dc-8.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/SFcDbGIW6an08-tCQR8SV-20260720-144927.png)

## View derived columns

### Derived columns filter

1. Click **Home**.
2. Select the **Derived Columns** checkbox.
3. Use the search bar to find individual derived columns by name.

You can find a paginated listing of derived columns in the system, pooled across the datasets. Each entry contains the following information:

1. Name (with description, if any).
2. Created by.
3. Updated by.
4. Updated at.
5. Type (refers to the template used when defining the cohort).

![oa-dc-9.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/lHVgw2XklJ2ik_qrYTcR8-20260720-144927.png)

### Dataset page

Alternatively, click **Derived Columns** inside the dataset page to access the derived columns for a given dataset.

![oa-dc-10.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/RLXciVtc1NWYjqX8miY_p-20260720-144927.png)

## Edit derived columns

1. Click **Home**.
2. Select the **Derived Columns** checkbox.
3. Identify the derived column you want to edit and click the **Name**.

Alternatively, click **Derived Columns** inside the dataset page to access the derived columns for a given dataset and click the **Name**.

## Delete derived columns[​](https://app.netspring.io/documentation/docs/Model%20Behavioral%20Semantics/other_actions_mbs#delete-a-derived-column)

1. Click **Home**.
2. Select the **Derived Columns** checkbox.
3. Identify the derived column you want to delete and click **More (⋮) > Delete**.
4. Click **Yes** to confirm.

Alternatively, click the **Name** to open the derived column and click **More (⋮) > Delete**.

You can also go to **Data > Datasets** and click the **Name** to open the dataset. Click **Derived Column > More (⋮) > Delete**.