How leading QSRs integrate big data
Data integration is a crucial step for quick serve restaurants to achieve operational efficiency – and to streamline collaboration with external organizations and business partners such as suppliers or delivery driver networks.
In order to ingest event data and make forecasting real-world aware, you’ll need to understand how to incorporate external data into your existing tech stack, or what additional tools you might need to add to your tech stack.

A tech stack typically consists of tools used to find data and make it useful, such as a database, programming languages and frameworks, front and back-end tools, and applications connected via APIs. Companies leverage these tools to connect to third party data and combine it with their own, store the data, build models, visualize data, and use some form of geo-location.
Download the guide, Big data integration for QSRs, for a case study of how one North American fast casual chain integrates event data, detailing how to:
Download the free guide
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Demand Forecasting: Incorporating events into models for smarter forecasting.
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Labor optimization: Ensuring the right number of drivers and store staff to meet demand.
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PredictHQ has supplemented incomplete data sets, including unticketed events and cancelled events, to power pricing strategies and operational decisions.
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Demand forecasting: Incorporating events into their Antuit-built models to better understand demand across 9600 stores.
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Amazon Alexa's "Events Near Me" feature uses PredictHQ data to inform users about local events.
Blog Demand forecasting: Getting drivers in the right place ahead of time to improve pick-up times.
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Event visibility: Available in Zartico’s platform enabling deeper insight and smarter decisions.
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Pricing: A key source of intelligence for the OTA Insights platform, enabling smarter pricing.
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Labor optimization: Enriching the Legion Technologies platform, used by retailers to reduce labor inefficiencies.
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