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Backwell Tech — Causal fuel-retail pricing dataset: per station × per product-line (8 lines) daily optimal-price recommendations with the full causal factor graph behind each decision — competitor station prices and a Granger-causality classification of each rival as price leader / follower / neutral, market landscape and demand forecasts, internal conditions, and scenario-simulation outputs. The byproduct asset is a labelled set of price-change experiments and their measured demand/margin responses at 1-3 cent granularity. Early-stage boutique: ~20 employees, Berlin HQ, no disclosed funding,…
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Causal fuel-retail pricing dataset: per station × per product-line (8 lines) daily optimal-price recommendations with the full causal factor graph behind each decision — competitor station prices and a Granger-causality classification of each rival as price leader / follower / neutral, market landscape and demand forecasts, internal conditions, and scenario-simulation outputs. The byproduct asset is a labelled set of price-change experiments and their measured demand/margin responses at 1-3 cent granularity
From 3 yearsCoverage Energy · Information TechnologyAsset class Equities · Commodities
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Backwell Tech — Causal fuel-retail pricing dataset: per station × per product-line (8 lines) daily optimal-price recommendations with the full causal factor graph behind each decision — competitor station prices and a Granger-causality classification of each rival as price leader / follower / neutral, market landscape and demand forecasts, internal conditions, and scenario-simulation outputs. The byproduct asset is a labelled set of price-change experiments and their measured demand/margin responses at 1-3 cent granularity. Early-stage boutique: ~20 employees, Berlin HQ, no disclosed funding,…
Backwell Tech offers (Alternative, Price, Sentiment, Supply Chain) — Modest but dense: order 10^4-10^6 rows (est.: tens of stations up to a few hundred per network × 8 product lines × daily recommendation/outcome records over the operating period, each row carrying a multi-factor causal explanation far richer per record than a price scrape).
Training causal (not merely correlational) price-elasticity and dynamic-pricing models for fuel retail; competitive price-leadership network graphs for market-power and collusion-screening analysis; European fuel inflation nowcasting at station granularity; counterfactual/simulation validation sets for retail pricing RL agents; margin-benchmarking panels across EU fuel networks
The data is with 3 years of history.
Coverage spans US, Europe, Other; Oil & Gas Refining and Marketing, Application Software; alternative, price, sentiment, supply_chain; equities, commodities.
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