In a world where success is defined by precision, perhaps the most valuable aspect of the PeerLogix offering is the level of granularity we are able to include in the taxonomy of our data sets. While competitors (and there are not many) offer the ability to target OTT viewers simply by the genre of programming they consume (action, adventure, comedy, family, etc…), our direct pipeline and position ‘in the stream’ allow us to create and identify content markers that offer far deeper audience insights.
Advertisers leveraging PeerLogix data are able to create completely custom data sets that target viewers not only by genre of programming they consume, but by the actual titles they watch, the channels those title originate from, the studio that produced the title and even by the actors/actresses that appear in the content. This means that, beyond simply creating a data segment to target viewers that tend to consume ‘action’ movies, an advertiser could target viewers of ‘Tom Cruise’ movies, or even those that have specifically streamed one of the ‘Mission Impossible’ installments.
Sample Taxonomy of Peerlogix OTT Data Library
Data: Deterministic & Non-Amplified
Another good signaler of quality for OTT data sets is the degree to which it is gathered ‘deterministically’ rather than ‘probabilistically’ and delivered in a raw ‘non-amplified’ deliverable.
Because PeerLogix’s data is gleaned from direct observance of actual streams of content, it is said to be deterministic. Every individual piece of data can be traced to a real action taken by a specific member of the target audience. It is not uncommon, however, for other data suppliers to leverage an underlying action to create ‘look-alikes’ based on probability.
For example, while every member of a PeerLogix ‘Modern Family’ data segment will have proactively streamed the show at some point, a member of a probabilistic ‘Modern Family’ data set may simply share other characteristics of those who have actually streamed the show. While a probabilistic data set is not inherently bad data, it is most certainly less valuable and it is, therefore, important for an end consumer of the data to understand its origin.
One reason that some data suppliers will utilize a probabilistic methodology is the supplement a true sample size that is otherwise too small to be valuable. In the case of TV data providers, one model that may result in small sample sizes is the practice of collecting data by including proprietary software within a set top box or streaming hardware device. While the raw data garnered from this process may accurate and deterministic, the ability to create real scale is limited by the provider’s ability to get its software in enough devices. It is not uncommon for OTT data providers that collect data in this way to create probabilistic models to be able to deliver more scalable data sets.
As previously discussed, PeerLogix pulls its data from within the stream, itself, so that every stream from an observed network is collected and cataloged, without reliance on business partnerships to stitch together scale.
The PeerLogix universe of over 180 million active streaming households, generated from real-time observation longtail streaming networks, creates a pool that is as large as an aggregate of the highest profile OTT networks
Probabalistc & Amplified
Targeted-able audience is 'expanded' by creating pool of 'look-a-likes' - users that share attributes of those actually observed exhibiting desired behavior. This practice is commonly used to 'amplify' a potential targeted audience, but makes audience member less valuable to advertisers.
Deterministic & Non-Amplified
All of Peerlogix's targetable data sets include only audience members that have actually exhinited a desired behavior - they have actually viewed a targeted movie, TV show or genre of content.
The Company incorporates a third-party geo-location service provided by Digital Element to determine authenticity of IP Addresses as well as their physical geographic location to an accuracy of a few hundred yards.
IP Addresses deemed to be virtual private networks (VPNs) or using an alternative masking service are flagged, giving the Company the ability to filter them out during later analysis steps, if deemed necessary. Information the Company is able to directly conclude about Over-the-Top households as a result of their IP Address are: Country, Region/State, City, ZIP/Postal Code, Internet Connection Type & Speed, Mobile Carrier (if applicable), Latitude/Longitude (approximate), Internet Service Provider, Home/Business, and Company Name (if applicable).
The Company’s data meets anonymity standards necessary to be classified as non-personally identifiable information (“Non-PII”). Thus, the Company’s data collection methods meet or exceed all current accountability and data collection standards of domestic and international government and regulatory agencies. PeerLogix’s data classification of Non-PII significantly reduces regulatory threats that the industry currently faces.
Data: Patented Methodology
U.S. Patent and Trademark Office has granted Peerlogix patent No. US10402545B2 that allowsthe company to track and collect data on streaming and downloading internet content such as movies, television series and music. The audience activity data can be aggregated and catalogued in any way relevant to making determinations on user interest.
This patent spearheads a defensible intellectual property portfolio covering all aspects of business applications relevant for the Peerlogix's unique business model, including advertising and analytics around streaming audiences.
PeerLogix's patent pending platform collects over-the-top data, including IP addresses of the streaming and downloading parties, production company, content title, media type, and genre/category of media watched. The platform then leverages licensed and publicly available demographic information and other databases to further filter the collected data and generate and append insights and consumer preferences. These combined data sets are used to create specific value to digital advertising firms, media companies, entertainment studios, investors, OTT platforms and others.