SAMPLE Digital Ad Daily KPI Estimates | 10 Tickers | 52 KPIs | 12-Hour Lag | Daily Pre-Market ...

Oxford Data Plan's Digital Advertising KPI Estimates product delivers daily, point-in-time revenue estimates for the world's largest digital advertising platforms, including Google (Alphabet), Meta, Snap, Amazon, TikTok (ByteDance), Trade Desk, Reddit, and others. Built from a proprietary advertising agency panel that tracks $12-15 billion in annual spend across 8-10 agencies ranging from the largest global networks to specialist boutiques, this dataset gives equity investors a ground-level, near real-time view into platform-level ad revenue momentum that cannot be replicated from public data or traditional vendor sources.

The underlying panel is purpose-built to be representative across advertiser size and type. The panel composition spans approximately 45% enterprise advertisers, 37% mid-size, and 18% small and startup advertisers, ensuring that estimates reflect the full market rather than being skewed toward large brand budgets. By spend type, the panel is approximately 65% performance advertising and 35% brand advertising, reflecting the true distribution of digital ad budgets and providing a balanced read on platform revenue trends across both direct response and awareness-driven campaigns. No single vertical exceeds 20% of total panel spend, with the top three being retail at 16%, leisure at 14%, and CPG at 11%.

Each agency in the panel shares current and upcoming advertising spend data at the brand and platform level. ODP applies a bottom-up weighting methodology to aggregate agency-level inputs into platform revenue estimates, with weights calibrated per product and KPI to maximize out-of-sample accuracy. This approach allows ODP to track daily shifts in advertiser demand across platforms and translate them into actionable revenue signals for equity investors modeling names like Alphabet, Meta, Snap, and The Trade Desk.

Estimates are delivered daily with a 12-hour lag pre-market US EST, giving clients a continuous read on digital advertising market conditions rather than waiting for quarterly earnings or sell-side research updates. Every KPI is tagged with source lineage and backtest performance, and there is no analyst overlay or subjective adjustment at any stage of the modeling process.

Key Data Elements

Bloomberg ticker and company identifier for covered ad platforms
Date (daily frequency, point-in-time)
Advertising revenue KPI estimates (absolute and year-on-year growth)
Platform-level and regional breakdowns
Spend type composition (performance vs. brand)
Advertiser vertical mix
Publication date for point-in-time reconstruction
Historical time series for backtesting

Coverage

Major digital advertising platforms including Google, Meta, Snap, Amazon, TikTok, Trade Desk, Reddit, and more
Panel covers $12-15bn in annual ad spend across 8-10 agencies, from global networks to specialist boutiques
Spend composition: 65% performance, 35% brand
Advertiser mix: 45% enterprise, 37% mid-size, 18% small and startup
No single vertical exceeds 20% of panel spend
Approximately 5 years of average history

Delivery

Daily delivery pre-market US EST, with a 12-hour lag
AWS S3 and API access for systematic and quantitative workflows
Web dashboard with visualization and CSV download
Point-in-time data structure, fully compatible with backtesting pipelines
Customizable coverage: clients subscribe only to the platforms they need

Primary Use Cases

Pre-earnings positioning on digital advertising platform stocks
Real-time tracking of ad market acceleration and deceleration
Platform share shift analysis across Google, Meta, Snap, Amazon, and others
Modeling the impact of macro conditions on digital ad budgets
Quantitative signal generation on ad revenue inflection points
Thesis validation for long and short positions in the digital media sector