Overview
Raw Data Exports provide access to event-level data exactly as it is received by Mapp Engage. The exported data is neither aggregated nor interpreted, allowing you to perform independent analysis in external systems such as BI platforms, CRM environments, or data warehouses.
Because the data is unprocessed, multiple events can appear for the same recipient and message. This behavior is expected and reflects how events are captured by the system.
For a complete schema description of all available fields and parameters, see Raw Data Export - Field Reference.
When to Use Raw Data Exports
Use raw data exports when you need full analytical flexibility or want to process Engage data outside the platform.
Typical use cases include:
Event-level tracking
Custom attribution modeling
Cross-channel analysis
Data warehouse ingestion
Advanced reporting beyond Engage dashboards
If you only require aggregated metrics that count the first occurrence of an event, consider using Simplified Data Exports instead.
Key Characteristics
Contains unfiltered, event-level data
No aggregation or interpretation is applied
Designed for processing in external analytics tools
Supports large-scale exports
Can be generated manually or on a schedule
Understand How to Interpret Raw Data
Working with raw data requires an understanding of how events are recorded and exported. The following behaviors are normal and should not be interpreted as system errors.
Apparent Duplicate Events
Raw Data Exports record each event exactly as it is received by Mapp Engage. Because events are not deduplicated, multiple records for the same event can occur.
Timestamp Granularity
Event timestamps are stored with a granularity of one second. Sub-second arrival times are not captured.
If multiple identical events — such as reads or link clicks — are received for the same recipient and message within the same second, they can appear as identical records in the export.
Why This Happens
A frequent cause is automated security scanning performed by mail providers. These systems often preload images and follow links to check for malicious content.
Since these scans intentionally imitate real user behavior, the generated events cannot be reliably distinguished from genuine interactions and are therefore recorded as received.
How to Interpret This Data
Identical records do not indicate an export error.
They represent separate events received from external systems.
The number of records reflects how many events were captured within that second.
If deduplicated reporting is required, use Simplified Data Exports.
Automated Activity
Automated processes such as spam filters, security scanners, and email clients may trigger opens or clicks without direct user interaction. These events are recorded because Engage cannot reliably differentiate automated activity from genuine recipient behavior.
When analyzing raw data, consider that a portion of engagement metrics may originate from automated systems.
Timestamp Precision
All event timestamps are recorded at one-second precision. Events occurring within the same second may therefore appear identical even when they were triggered separately.
This limitation should be considered when building attribution models or performing sequence-based analysis.
Attribute Value Timing
Attribute values in the export reflect the state of the attribute at the time the export is generated, not necessarily the value at the time of message sendout.
If an attribute changes between sendout and export, the exported value may differ from the value originally used for the message.
How Exports Are Generated
Raw data exports are configured using a guided wizard.
Define the export type, name, and description.
Select the time frame for the export.
Choose the events and attributes to include.
Select the file format and configuration settings.
Generate the export manually or schedule it for automated delivery.
The availability of certain data points depends on your system configuration and user permissions.
Best Practices
Always include a stable contact identifier such as User ID or Email Address to support downstream analysis.
Define a clear event scope before exporting to avoid unnecessarily large files.
Validate exports in a staging environment before integrating them into automated pipelines.
Use external tools to aggregate and interpret the data according to your reporting needs.