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AWS Solutions Library

Guidance for Advertising Agency Planning Management on AWS

Overview

This Guidance helps you centralize operations and set up seamless workflows among agency, advertiser, and publisher teams. It addresses key topics, including audience analysis, identity resolution, campaign insights visualization, personalized customer experiences, and media attribution. By following the best practices outlined, you can drive greater return on ad spend through improved collaboration between media planning, buying, analytics, and creative execution teams. The Guidance showcases how to leverage AWS for audience enrichment, data hygiene, democratizing performance data, and hyper-personalization.

How it works

Data flow

This diagram shows the data flow process for advertising agency planning management.

Download the architecture diagram
A data flow diagram illustrating the planning and management process for an advertising agency using AWS. The diagram outlines sequential steps including media plan brief definition, target audience determination, use of an advertiser asset library, campaign and budget structure analysis, creatives and audience generation, campaign execution, and campaign performance analysis. Color-coded flows differentiate roles between advertiser, agency planning management, and ad platforms.

Detailed architecture diagram

This architecture diagram shows how to modernize advertising planning management in detail.

Download the architecture diagram
Detailed architecture diagram illustrating the planning and management workflow for an advertising agency using AWS. The diagram includes modules for audience management, data collaboration, campaign analysis, creatives and media plan generation, and data management, utilizing AWS services such as Step Functions, Entity Resolution, Clean Rooms, Glue, Bedrock, DataZone, Redshift, Athena, QuickSight, IAM, and Lake Formation.

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

The Guidance uses Step Functions for workflow orchestration, enabling failure anticipation, source identification, and mitigation. The core services (Amazon Bedrock, AWS Clean Rooms, and AWS Entity Resolution) are fully managed, reducing operational burden. Step Functions interacts with these services using direct integration to perform business operations, monitor data flow, and anticipate failures.

Read the Operational Excellence whitepaper

Amazon DataZone streamlines data discovery and sharing while maintaining appropriate access levels. This service creates and manages IAM roles between data producers and consumers, granting or revoking Lake Formation permissions for data sharing. By using IAM, you can help ensure that policies have minimum required permissions to limit resource access, reducing unauthorized access risks.

Read the Security whitepaper

Step Functions orchestrates workflows by monitoring AWS Entity Resolution workflow status and direct service integration with Amazon Bedrock. Step Functions monitors workflows and automatically handles errors and exceptions with built-in try/catch and retry. It also automatically scales the operations and underlying compute to run the steps of the workflow in response to increase in requests.

Read the Reliability whitepaper

You can achieve business use cases in near real-time by invoking LLM models through API calls to Amazon Bedrock. AWS Entity Resolution allows for record matching, using rule-based or machine learning (ML) models on-demand or automatically. As fully managed services that reduce overhead from managing underlying resources, Amazon Bedrock and AWS Entity Resolution enhance performance efficiency through reduced operational burdens.

Read the Performance Efficiency whitepaper

S3 buckets for the campaign analytics module use the S3 Intelligent-Tiering storage class, reducing costs based on access patterns. By leveraging S3 Intelligent-Tiering, storage costs are reduced based on data access patterns.

You can review QuickSight author and reader account activity to identify and remove inactive accounts. Removing inactive QuickSight accounts minimizes the number of required subscriptions, further optimizing costs.

Read the Cost Optimization whitepaper

Athena's query result reuse feature reduces the usage of compute resources for running the same SQL queries on large datasets within a specific time period, returning the same results. This feature minimizes redundant compute resource usage, supporting sustainability efforts.

Read the Sustainability whitepaper

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.