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What Is a Dataset?

Datasets are a way to upload and manage content components in bulk. Once a dataset is created, the components can be referenced in any message where the dataset is selected. Content in a dataset changes infrequently. For real-time message-content, consider integrating your CMS.

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When Should I Use Datasets?

Datasets in Aampe are a way of managing messages with a large number of specific inputs. They are also helpful when managing combinations of message components
  • Your message directs users to one of many blog articles. Each variant includes the article title and the corresponding url.
  • You want to refer to, say, a city and state together in a message. Obviously these inputs can’t be combined arbitrarily. Datasets will ensure separate column values only appear together.

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How To Create a Dataset

Within the composer, Message Settings > Datasets
  • Click Create Dataset
Dataset Create Pn This will present you with two options
  • Create a dataset in Aampe
  • Upload a CSV

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Create a Dataset in Aampe

This option is ok for smaller datasets.
  • Specify the columns, add values, and insert each new row
  • Save

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Upload a CSV

This option is ideal for larger datasets
  • Upload the csv
  • Inspect the results and save

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How To Use Datasets In a Message

Once a dataset has been created you can add any subset of that dataset to a message.
  • When creating a new message, click the Dataset icon near the bottom of the content panel
Add Dataset To Message Pn
  • Choose the dataset you wish to add
Select Dataset Pn
  • Filter down to the specific rows you want to include. Click Add.
Filter Dataset Pn
  • To insert elements from the chosen dataset rows into the message, click the dataset icon
Insert Dataset Column In Message Pn
  • Select a column to include in the message
Choose Column Pn
  • The message will now have a variant for every row in the dataset. You can create additional variants by adding message components. You can also add other columns in the dataset.
Message With Dataset Added Pn Nice work! You saved a lot of time by using Aampe Datasets to power your content library.

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Advanced Dataset Features

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Alternates and Labels

Dataset Labels let you designate columns within a dataset as Alternates and apply corresponding Labels, so they can be included in agent learning. This unlocks insights from dataset-driven content, bringing it into learning and analytics.

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How to create a Dataset with labels

Adding A Label Column
  • Within a dataset, open the Dataset Actions menu and select Assign Component Type.
  • Choose a Component type and select the column you want to use as Alternates.
    • If you already have a column containing Labels for those Alternates, select it here.
    • Otherwise, you have the option to add a blank column.
    • Note: Only existing Component Types and Labels can be used.
Once configured, the Alternate and Label columns are grouped together. You can assign or modify Labels either manually or by bulk-selecting rows and applying Labels. Modify Labels

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Using Dataset Labels in Messages

Inlcuding dataset content in a message continues as usual. Use In Message
  • If a dataset is updated, any Messages using that dataset are updated automatically.
  • Labels applied to dataset Alternates are visible wherever those Alternates appear in Messages.
This ensures that dataset-driven content stays consistent across your system.
Important Considerations when using labels in datasets
  • If a Label on a dataset Alternate is changed, the change applies everywhere that Alternate is used.
  • A dataset column must have a Component Type assigned before Labels can be applied to its cells.
  • If you unassign a Component Type from a dataset field that’s already being used within Live messages, agents will stop learning on those fields. This can affect how agents choose to schedule or send these Messages.
This shared behaviour ensures consistency, but also means changes should be made thoughtfully.

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Start and End Dates

If a dataset contains columns namedstart_date and end_date, the message will only send within that date window. Suppose you want to start sending messages one or two days ahead of a holiday. Your dataset would look like the following. 
name, start_date, end_date
Canadian Thanksgiving, 13/10/2025, 13/10/2025
American Thanksgiving, 25/11/2025, 27/11/2025
Note the DD/MM/YYYY date formating. Any other date formats will fail.

Connect your message to the dataset as shown above and bring elements into the message text. 
Happy {dataset.name}!
Aampe agents would send message variants with “Happy Canadian Thanksgiving!” on Oct 13. On the dates between Nov 25 and Nov 27 (inclusive), agents would send messages with “Happy American Thanksgiving!”