MetriCrowd
MetriCrowd, is a single-source, cross-media audience measurement solution. It uses a unique hybrid measurement approach that combines a representative panel and corresponding census measurements, covering the entire media consumption of the target population. In addition to its unrivalled cost/performance ratio, the solution integrates seamlessly into customers’ internal data processing channels.
The scope includes everything along the whole media journey of the target audience including total video & audio, anything connected to the Internet as well as offline content such as movie theatres, billboard, print, and even retail presence.
This result is made possible thanks to the open architecture of MetriCrowd, our single-source measurement solution that can be deployed as a whole or as independent components (e.g., only online radio & podcast measurement). It takes advantage of the best methods in each of its scope segments and combines the results in one easy-to-analyse output.
Content
Our solution works at content level natively. A content item can be a TV or radio show, a podcast, a Web page, or anything the broadcaster defines. Much of the metadata can be extracted by default by monitoring the content, and then used for measurements, dimensions, or visualization. We can automatically generate topics and IAB classification. Our solution is by default capable of accurately measuring the exposure of advertising, particularly online video. This enables us to produce comprehensive reports on cross-media advertising, including that delivered outside broadcaster-controlled content, such as that shared on social networks or even in movie theatres.
Context & Device
MetriCrowd is designed to be context-aware, capable of identifying not only the device used for listening, watching, or browsing, but also where and how the interaction occurs. But there's more: context also includes events—everything the user does on their devices when interacting with your content.
NeuroMedia is also developing technology that links all census-based consumption data with corresponding passive measurement panelists. NeuroMedia Software’s new solution aims to seamlessly integrate the two measurement methods, allowing one to enhance the other, both efficiently and cost-effectively. This project primarily benefits real-time panel enrichment with census-based audience consumption data, refining its accuracy and detail. The goal is to significantly expand the scope of audience measurement while optimizing costs.
In concrete terms, this technology will make it possible to enrich passive measurement data, obtained with audio matching, with the following elements:
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The exact nature of the devices: precise identification of the characteristics of the device broadcasting the content. The ability to detect (even without watermarking) the origin of the broadcast program (STB, Auvio web or mobile, Wi-Fi radio, etc.).
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Detailed behaviour: The level of attention (window visibility), details of listening or viewing behaviour (clicks or other action events) or even technical problems encountered (QoS).
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All online consumption graph can be taken into account, even if it is not measured or measurable by passive methods, e.g., very low-exposure content such as podcasts. Including, insofar as it can be attributed to the individual, all past consumption.
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Detection of listening with headphones or other contextual information.
Enrichment works the other way round, from panel to census. The main advantage is the donation of socio-demographic profiles from the panellist to the digital measurement. This advantage persists over time. In other words, since digital measurement can track a person using other techniques such as cookies, only one matching is needed to make this possible.
This also means that this technology will help overcome the disappearance of third-party cookies or cookie cleaning. Indeed, if it is possible to match a panellist with a first-party cookie (even if the browser frequently cleans it) on all the sites he or she visits, these cookies can of course be combined at a later date to reinforce census measurement. This provides server-side merge algorithms with much more reliable data.
As mentioned above, since there’s no need to modify anything on the device that consults the content, it’s also possible to measure consumption at work where on-device meter installation is prohibited.
