SAMPLE Daily Equity KPI Estimates | 250+ Equities | 500+ KPIs | 12-Hour Lag | Daily Pre-Market ...

Oxford Data Plan's Daily KPI Estimates product delivers point-in-time, daily estimates across 250+ publicly listed equities and 500+ tracked KPIs. Built from a multi-signal modeling framework and powered by 95% proprietary or exclusive data sources, this dataset enables investors, analysts, and strategy teams to monitor underlying business momentum between earnings cycles, identify inflection points earlier, and benchmark companies against peers and sectors without relying on lagging financial reports.
Each KPI estimate is constructed from multiple independent data signals, selected based on their relevance to the specific business being tracked rather than applied uniformly across sectors. Models are validated through historical backtesting, cross-source consistency checks, and calibration against available reference benchmarks. There is no analyst overlay and no subjective adjustment. Every estimate is tagged with source lineage and backtest performance, giving compliance teams full transparency into the methodology. The sum of daily KPI estimates across a quarter closely correlates with reported company financials, making the product directly applicable to earnings modeling workflows.

Key Data Elements

Bloomberg ticker and company identifier
Date (daily frequency, point-in-time)
KPI value estimates (revenue, net sales, orders, customers, DAUs, ad spend, and more)
Region code (company-level and geographic breakdowns)
Publication date (for point-in-time reconstruction)
500+ KPI types across 244 unique metrics
Historical time series for backtesting

Coverage

250+ publicly listed companies across the US and Europe
Sectors include Digital Advertising, E-Commerce, B2B Software, Consumer & Grocery, Real Estate Technology, Airlines, Food Delivery, Outdoor Advertising, and more
Geographic coverage spans the Americas, UK, and Europe
Approximately 5 years of average history per ticker

Delivery

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

Primary Use Cases

Earnings forecasting and pre-earnings positioning
Real-time investment thesis validation and stress testing
Equity research and alternative data modeling
Competitive and sector performance tracking
Risk management and early warning on KPI deterioration
Quantitative strategy inputs and systematic signal generation
Idea generation and outperformer identification ahead of Street revisions