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IAS (Integral Ad Science) API Connector

Updated over 4 months ago
Integral Ad Science - The MarTech Summit Sponsor

🔹 Summary

This connector imports campaign-level quality assurance metrics from IAS, including viewability, fraud, brand safety, and overall impressions. Data is grouped by campaign and placement granularity.

  • Source Feed: integral

  • Job Label: IAS Data Import

  • Import Frequency: Daily

  • Lookback: 5 days

  • Main Uses: Ad quality assurance, performance benchmarking, invalid traffic monitoring

Granularity: Team → Campaign → Placement level
​Purpose: Track media quality metrics such as viewability, invalid traffic, brand safety, and impression counts across placements for performance validation and optimisation.
​Refresh Cadence: Daily
​Update Modes: Append (for all data tables)

For more information on this integration and use cases please use the following link.

🔌 Connection

Required Options

Key

Type

Required

Description

integral_teams

text

✅

IAS Team ID

integral_username

text

✅

IAS platform login (email or user ID)

integral_password

text

✅

IAS platform password

integral_secret

hidden

✅

API secret

🛠️ Job Configuration

Field

Value

Job Label

IAS Data Import

Feed

integral

Status

1 (active)

Lookback

5 days

First Run Hour

10 AM

Poll Frequency

Every 24 hours

Lookback Window

0

Update Mode

append

🎯 Targets (Tables)

All targets use the camp:plac (Campaign : Placement) granularity and have bright_custom_date as the date column.

#

Table

Method

Description

Priority

1

ias_data_overview

overview

High-level metrics

1

2

ias_data_viewability

viewability

Viewability-specific metrics

2

3

ias_data_brandsafety

brandsafety

Brand safety metrics

3

4

ias_data_fraud

fraud

Fraud/Invalid Traffic metrics

4

🧭 Dimensions

Name

Result Fields (used across all tables)

Placement Name [IAS]

ias_data_brandsafety.placementName, ias_data_fraud.placementName, ias_data_overview.placementName, ias_data_viewability.placementName

📊 Base Metrics

Name

Formula

InView Impressions [IAS]

SUM(ias_data_viewability.totalNetInViewImps)

OutofView Impressions [IAS]

SUM(ias_data_viewability.totalNetOutOfViewImps)

Measured Impressions [IAS]

SUM(ias_data_overview.totalNetMeasuredImps)

Unblocked Impressions [IAS]

SUM(ias_data_fraud.unblockedImps)

Passed Impressions [IAS]

SUM(ias_data_brandsafety.passedImps)

Brand Safety Imps [IAS]

SUM(ias_data_brandsafety.totalEligibleForBrandSafetyImps)

Failed Impressions [IAS]

SUM(ias_data_brandsafety.failedImps)

📈 Composite Metrics

Name

Formula

Round

Suffix

Integral Measured Rate [IAS]

{Measured Impressions [IAS]} / {Unblocked Impressions [IAS]} * 100

2

%

Viewability [IAS]

{InView Impressions [IAS]} / ({InView Impressions [IAS]} / {OutofView Impressions [IAS]}) * 100

2

%

Invalid Traffic Rate % [IAS]

({Failed Impressions [IAS]} * 100) / {Brand Safety Imps [IAS]}

2

%

Passrate % [IAS]

({Passed Impressions [IAS]} * 100) / {Brand Safety Imps [IAS]}

2

%

🔗 Joins

  • Datasource Joins: None configured

✅ Example Use Cases

  • Evaluate viewability of served impressions across placements

  • Identify and analyze invalid traffic or fraud rates

  • Assess brand safety violations and pass/fail performance

  • Track total measured vs. unblocked impressions

  • Calculate viewability rate and pass rate over time

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