ADVPERF04-BP01 Choose a data management strategy that matches your availability, latency, and access requirements - Video Streaming Advertising Lens
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ADVPERF04-BP01 Choose a data management strategy that matches your availability, latency, and access requirements

Customers need to have a clear data management strategy for their advertising workload datastores. The factors to consider are latency needs, availability needs which will help them chose the right AWS data service

Implementation guidance

The following are the most common data stores available in adtech:

  • User data: Demographic data (age, gender, and location), behavioral data (browsing history, interests, and purchase history), and device data (device type, operating system, and browser).

  • Audience data: Segmentation data (personas and target audiences) and geo-location data (IP addresses and GPS coordinates).

  • Campaign data: Ad creative data (like images, videos, and text), ad placement data (websites, apps, and platforms), and campaign performance data (impressions, clicks, and conversions)

  • Inventory data: Publisher data (website or app details and traffic data) and ad space data (ad sizes, formats, or placements)

  • Pricing and bidding data: Bid data (bid prices and bid strategies) and auction data (bid landscape and winning bids).

  • Third-party data: Data from Data Management Platforms (DMPs) and data from data exchanges or marketplaces.

  • Analytics and reporting data: Conversion data (sales, leads, and actions), attribution data (tracking user journeys), and engagement data (view-through rates and dwell times) 

For latency, consider the following:

  • Low-latency data (real-time or near real-time): This data needs to be processed and acted upon within milliseconds to ensure optimal ad delivery, real-time bidding, and accurate tracking of user interactions.

    • Bid (bid requests, bid responses, and auction data)

    • User (device data, location data, and contextual data)

    • Ad impression (ad requests and ad responses)

    • Real-time campaign performance (clicks, impressions, and conversions)

  • Medium-latency data (near real-time or batch processing): This data can be processed in near real-time (within minutes or hours) or in batches, as it is used for audience targeting, campaign optimization, and attribution analysis.

    • User behavior (browsing history and interests)

    • Audience segmentation

    • Campaign optimization (performance metrics and engagement data)

    • Attribution (user journeys and conversion paths)

  • High-latency data (batch processing or offline): This data can be processed in batches or offline, as it is typically used for analysis, reporting, and long-term decision-making rather than real-time ad delivery or optimization.

    • Historical campaign

    • Detailed analytics and reporting

    • Third-party (from DMPs or data exchanges)

    • Ad creative (images and videos)

Resources