A proprietary, point-in-time panel of the global food-delivery market — 8 platforms, ~430,000 venues across EU-27, the UK, the Gulf and LatAm, rebuilt weekly with 3 years of history. We convert public merchant surfaces into aggregated, ticker-mapped signals that track order volume, supply and pricing quarters ahead of reported revenue.
We continuously observe the same venue universe on every major platform and archive each observation with a timestamp, so every metric is reconstructable exactly as it stood on any past date — no survivorship, no look-ahead. Coverage spans the four regions where the covered issuers concentrate revenue and growth.
Each signal is delivered as a clean, aggregated time series at the geo × platform level, pre-mapped to the tickers whose delivery businesses it informs. Built for backtesting and nowcasting — not raw feeds.
Public review velocity (net new reviews per venue per month) tracks order volume tightly at the aggregate level and moves ahead of reported revenue. Aggregated across the panel, it nowcasts platform GMV growth by geo before it prints.
Active-venue counts, gross openings and closings per platform × geo per week. Net merchant growth is the supply side of the flywheel — expanding selection precedes GMV, and accelerating churn flags share loss to a rival app.
A matched-basket index of listed menu prices, rebased to 100, tracking food-away-from-home inflation at the platform level. Feeds take-rate and gross-margin models and doubles as a real-time regional CPI-adjacent read.
Share of venues running active promotions, median delivery fee and minimum-order thresholds by geo. Rising promo intensity signals competitive spend and margin pressure; falling fees flag a land-grab in a contested market.
Every platform is normalized to one schema and mapped to the listed equity whose economics it drives, so a portfolio manager can pull a single geo-blended read per name.
The dataset is engineered to pass a fund's data-diligence: point-in-time integrity, transparent aggregation and a compliance posture that keeps it usable across the desk.
Every observation is archived with its capture timestamp. Series are reconstructable exactly as they stood historically — no restatements, no survivorship, no look-ahead bias in backtests.
We deliver geo × platform aggregates and indices, mapped to tickers. Relative growth and share dynamics are the product — we do not claim absolute GMV, only the direction and rate of change that leads it.
Derived only from publicly observable merchant surfaces. No personal data, no consumer identities, no order-level records — nothing material and non-public. GDPR-aligned and DDQ-ready.
An illustrative slice: the review-velocity order-volume proxy for the German Lieferando/Just Eat panel, aggregated monthly and indexed. Delivered as a machine-readable time series with confidence bands and ticker mapping.
| Month | Active venues | Reviews / venue | Vol. index | MoM |
|---|---|---|---|---|
| 2026-03 | 60,180 | 16.9 | 124.3 | +1.2% |
| 2026-04 | 60,540 | 17.3 | 127.2 | +2.3% |
| 2026-05 | 60,910 | 17.6 | 129.4 | +1.7% |
| 2026-06 | 61,020 | 17.9 | 131.6 | +1.7% |
| 2026-07 | 61,240 | 18.4 | 135.3 | +2.8% |
| 2026-08* | 61,410 | 18.6 | 136.8 | +1.1% |
Request a point-in-time sample — a geo × platform slice with full history and ticker mapping. We'll send a data dictionary and a walkthrough of the backtest.
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