Overview
JamLoop’s Platform gives customers flexible options to access and analyze campaign data in the way that best fits their reporting, attribution and BI workflows. Customers can either configure custom aggregate reports directly in the platform, or set-up dynamic data transfers via Snowflake Share.
Summary of Export Types
There are three main types of data transfer:
Aggregate
Grouped and summarized views of campaign performance across chosen dimensions
May or may not include attribution
Log-Level Delivery (*Snowflake Share only)
Granular, impression-level reporting showing every ad delivered.
Can be used to calculate metrics such as household reach and frequency
Log-Level Attribution (*Snowflake Share only)
Granular conversion-focused reporting that ties conversions back to attributed impressions
Each export type is designed for different levels of transparency and analytical depth. By aligning the transfer type with the customer use cases, JamLoop ensures brands can validate performance, power internal modeling, and integrate seamlessly into BI environments.
Delivery Methods
Direct Download (*aggregate reports only)
Accessible directly from JamLoop’s reporting interface.
Best for ad hoc or smaller-scale exports
Custom Reports are emailed (aggregates only) and include a download link
Reports can be sent directly to customer inboxes
Useful for lightweight, human-readable exports and when multiple team members need regular access.
Exports with more than 100k rows are not supported by an email export
Snowflake Share
Aggregate & Log-Level options
Data delivered directly into the customer’s Snowflake environment
Eliminates file transfers and enables near-real-time access for advanced analytics teams.
Requires customer to setup access ahead of time
In some cases, JamLoop may support direct integrations with a BI tool so long as the BI system has an integration and/or can receive data from Snowflake’s cloud-based warehouse
Scheduling & Frequency
Frequency Options
Custom Reports
Daily
Weekly
Monthly
Snowflake Share
Dynamic / Real-Time
Timing Cadence
Exports are recommended to be delivered during off-peak hours (e.g. early morning UTC/local time) to capture completed activity from the prior period, and/or account for attribution which processes daily between 5:00 - 7:00AM Eastern Time.
Report Date-Range Rules
Aggregates: based on impression timescale
Log-Level Delivery: all impressions that occurred within the report range.
Log-Level Conversion: all conversions in the conversion time-scale, but impressions may precede the range depending on attribution window. (e.g. a conversion on 1/25/2026 may be attributed to an impression occurring 1/5/2026)
Date Timezone Consideration
Users can choose the timezone to be used for all date fields included in the reports
Defaults to UTC
Timezones need to be provided with regional information to account for DST
When a timezone is chosen that has DST, the report that spans the change to or from DST will skip an hour or contain multiple rows for the same hour respectively
Delayed Data Consideration
In certain cases, impression data from Display or specialty publishers may be reported with a delay. If a campaign includes these publishers, exports may initially reflect incomplete data until reconciliation is complete. To account for this, customers can either:
Use rolling report ranges (e.g., a rolling 7-day window) to allow late impressions to be captured.
Schedule exports on a lagged cadence (e.g., reporting on the prior week after a short delay) to ensure reconciled impression counts.
File Format
CSV
dynamic table via Snowflake
Locale handling
All numbers will be formatted following US-EN locale formatting rules
Numbers will use a period character as the decimal separator, with no thousands separator
Dates will be delivered using the ISO 8601 standard format YYYY-MM-DD (year, month, day) or YYYY-MM-DDTHH:mm:ss (with time)
Export Types Detail
Aggregate Export (with or without attribution)
Use Cases
High-level campaign performance reporting
Executive-friendly views of delivery and outcomes without handling large data files
Flexible grouping by dimensions such as date, campaign, line, publisher, product, etc.
Ideal for teams that want topline metrics for dashboards, reconciliation or benchmarking
Considerations
Attribution-enabled aggregates may appear incomplete if the window exceeds the report range. For example, a weekly file may exclude conversions that occur outside that 7-day window.
To mitigate this, rolling or lagged reports are recommended:
Rolling: Sending a 30 day report each send, with the most recent day (previous day) added, and the earliest date (31 days prior) dropped
Lagged: 30-day lag to align with a 30-day lookback window so that today’s report shows data from 31 days ago.
Reach is not dynamically calculable across multiple dimensions in aggregated files
Schema / Example Fields
Dimensions
Date
Campaign Name
Line Item Name
Media Name (Publisher)
Product Type
Creative Name
Zip Code
DMA Name
DMA Code
Screen Type
Metrics
Spend
Impressions
IP Reach 1 day
IP Reach 7 day
Frequency
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Completed Views
VCR (video completion rate)
Clicks
Conversion(s)
Responses
+ any pixel events
Log-Level Delivery Export
Use Cases
Granular impression-level data for customers with internal BI or data science resources.
Enables full flexibility in slicing, filtering, and modeling performance data.
Required for brands that want to dynamically calculate reach across any permutation of dimensions
Considerations
File sizes can be very large, requiring storage and processing infrastructure.
Exports include every impression, even those with no conversions
More technical expertise is needed to integrate, analyze, and interpret the data compared to aggregate files
Schema / Example Fields
Impression Timestamp
Impression Date
Impression Time
IP Address (SHA256 Salted Hash)
Impression ID
Campaign Name / ID
Line Item Name / ID
Media Name (Publisher)
Product Type
Creative Name
Zip Code
DMA Name
DMA Code
Screen Type
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Completes
Clicks
Log-Level Attribution Export
Use Cases
Detailed conversion-level reporting for validation and attribution analysis
Analyze attributed events’ custom value-parameters passed through even pixel (e.g. order_id)
Enables advertisers to understand exactly which impressions contributed to each conversion
Supports both Last-Touch Attribution (LTA) and Multi-Touch Attribution (MTA) methodologies
LTA each conversion is tied to a single impression
MTA each conversion may appear multiple times, with all contributing impressions listed
Considerations
File sizes are smaller than delivery logs but still require technical resources to manage.
Depending on attribution methodology, conversion rows may be duplicated (MTA) or limited to one per conversion.
Reports are based on the conversion timescale: all conversions that occur within the report start and end dates are included. However, attributed impressions for those conversions may fall outside the report period (earlier than the report start date), depending on the lookback window.
Reach cannot be directly calculated unless paired with delivery logs
Schema / Example Fields
Conversion Timestamp
Conversion Date
Conversion Time
Conversion Event Name
Conversion IP Address (hashed SHA 256)
Pixel ID
Event ID
Page URL
Referrer URL
Revenue (macro)
Order ID (macro)
User ID (macro)
Product (macro)
Custom (macro)
Impression Timestamp
Impression IP Address (hashed SHA256)
Impression ID
Campaign Name / ID
Line Item Name / ID
Media Name (Publisher)
Product Type
Creative Name
Zip Code
DMA Name
DMA Code
Screen Type