Ride-hailing data scrapes require contextual handling for accurate pricing models

Ride-hailing price data scraped from competitor apps becomes stale within minutes and can vary based on user account, location, and other factors. Treating these scraped prices as simple, clean facts can lead to inaccurate pricing or forecasting models. A more effective approach is to preserve the full context of each data point, including timestamp, user persona, location, and the status of the request itself. The article suggests structuring raw data in a detailed schema and applying freshness weights to ensure recent observations carry more influence than older ones in any analysis.