Sold by: PredictHQ
Deployed on AWS
Free sample dataset containing Live TV event data for a 1 month period for the state of California. Includes county level broadcast viewership data for sports events broadcast in that area. This expansive coverage gives insights into any potential drivers of demand. Pizza deliveries may skyrocket during football games, while demand for peanuts and beer may increase during baseball games.
Overview
PredictHQ’s easy-to-use global events API gives businesses direct access to a constantly updated and verified event database drawing on billions of data points daily. Events across 19 categories , from expos to concerts to college dates to natural disasters, are aggregated from hundreds of sources then cleansed, filtered, enriched and verified using our proprietary data pipeline .
Companies use this information to inform their demand forecasting and planning, and update their staffing, inventory and pricing strategies to match demand. Teams at Domino’s Pizza, Uber and Accor Hotels trust PredictHQ’s precise and scalable real-world events data to enhance demand forecasting models, improve workforce optimization strategies, aid dynamic pricing efforts, and much more.
The pandemic upended demand patterns and made them harder than ever to forecast. Companies are increasingly using verified external data to know what will drive demand up or down so they can make the most of every opportunity while mitigating losses. PredictHQ’s feature engineering, which includes applying a Predicted Attendance to each event as well as our Features API for easy integrations, means teams can quickly start driving value.
Documentation
Learn more about PredictHQ's real-world event data by visiting our Developer and Data Science Documentation . Our documentation includes a Quickstart API Guide to help you get up and running quickly as well as a full list of parameters for our Events and Places endpoints as well as our Broadcasts API for Live TV events. Finally, learn more about our Features API , which removes the friction of accurately aggregating events, enabling you to correlate event data to business demand quickly. Looking at individual spikes often leads to too small of a data set to correlate accurately. Data Aggregation enables data science teams to visualize enough volume of spike days to prove correlations of events to demand.
Getting started with PredictHQ's event data - A guide for Data Scientists
Join our Data Science Solutions Engineer to walk-through a Step-by-Step demo on Correlating Demand to Events . Our Data Science Docs will assist you and your team in getting started with our intelligent event data quickly. Our wealth of resources will enable you to analyze PredictHQ's event data, combine it with your historical demand data, and identify the relationship between the two. The end goal is to ensure you successfully identify correlations and properly feature engineer event data to incorporate it into your existing event data modeling. For additional information, check out our Dev Docs , chat with us live at PredictHQ.com , or email us @ awsdx@predicthq.com
Disclaimer: forward looking data subject to change as events are dynamic. Using PredictHQ’s APIs enables you to access the most up to date data.
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Dimensions summary
This listing uses a single pricing dimension: Product Access, measured in Units. It is offered at no cost, so you gain access to the product without a charge. Because there is only one dimension, there are no tiers, instance sizes, or usage add-ons to compare. The Units measure grants subscribers access to the live TV event broadcast data feed, which provides historical and predicted viewership information filtered by location.
Top-of-mind questions for buyers
A Unit grants a subscriber access to the live TV event broadcasts data feed. It maps to product access rather than to a fixed volume of records or API calls. Because the listing is offered at no cost, the Unit simply enables entry to the broadcast data feed.
The feed delivers historical and predicted TV viewership data for televised events, such as sports. You can filter by city or county rather than broad market areas. County-level viewership lets you forecast demand at a granular level, like individual store locations.
You can access the data through the provider's APIs, including a broadcasts endpoint for querying by location and date range. Data is also available through a secure data share and a cloud data exchange delivered over S3, letting you query and integrate it into your models.
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This product does not have a refund policy. For questions, contact awsdx@predicthq.com
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Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Additional details
You will receive access to the following data sets.
Data set name | Type | Historical revisions | Future revisions | Sensitive information | Data dictionaries | Data samples |
|---|---|---|---|---|---|---|
Live TV Events Dataset | Last 1 revisions | All future revisions | Not included | Not included |
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