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It is designed to be used for triggering content within a 3rd party application, such as a CMS.
Different modes are available to address different use cases:
• Watchers and OTS API: this API (previously called “Cloud API”) exports audience and traffic data, down to the content level. The time granularity can be set from one month down to one second.
• Monitoring API: this API outputs some monitoring values (such as percentage of light, CPU usage, presence of camera, etc) and is meant to be used in alert workflows.
• Clip Metadata API: this API makes it possible to complete the content ID captured by the Content API with other metadata (such as the name of the content), in order to make Audience per content reports easier to read.
The API gives access to :
The Audience Per Content API is used by CMS to pass along the ID of the content they are playing, so that Quividi can provide a deep understanding of the performance of each content and campaign in VidiCenter.
The most recent version of this API ensures that when the CMS notifies VidiReports it has ended playing a content, the audience detected during its playtime is returned to it, split by demographics, in order for the CMS to report on the effective impressions through its own programmatic pipeline.
The Predictive API provides a short term prediction of the audience of this screen, split by demographic (2 genders x 7 age groups), so that DSPs or SSPs can decide how to price that slot.
Quividi is the pioneer and World’s #1 in AI & Computer Vision based platforms providing real-time audience measurement & shopper engagement for Digital Out-of-home (DOOH) & Retail.
DOOH and Retail Media publishers leverage Quividi’s first-party, real-time audience impressions to trade their inventory using traditional and programmatic (pDOOH) channels. Retailers and Brands use Quividi’s high-fidelity footfall and engagement analytics to optimize conversion at key touch-points along the physical shopper journey.
Quividi’s platforms are Data Protection by design and support large-scale deployments, analyzing billions of anonymous shoppers monthly across tens of thousands of screens and points of purchase (POP).
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