By Adam Parker, Chief Executive, RealWire
To date the use of technology to solve the issue of irrelevant or badly targeted PR content has been relatively limited. Database structures used for press release targeting are generally based around categorisation or perhaps keywords. Depending on the level of granularity this can often result in a poor match of a particular press release to individual journalists or bloggers.
Recently new language analysis based databases have started to be released that look at a journalist or blogger's output in order to try and identify those who talk about a particular topic the most. This improves the intelligence of the approach for the sender if they use such tools effectively.
But even tools such as these do not address the issue from the individual journalist or blogger’s perspective. They don’t allow the recipients themselves to decide how relevant something must be to get their attention. Meanwhile spam filters or rules based inbox systems are often crude or time consuming to manage.
At RealWire we asked ourselves - what if we could build a system to provide relevant releases to individual journalists and bloggers across thousands of releases a day from multiple sources?
So after months of development, in a bold experiment to both demonstrate our new outbound targeting technology and as a potential solution to the issue of irrelevant PR we have built PRFilter.
We believe PRFilter is something different.
Like the language analysis databases, PRFilter’s active interest technology™ builds a profile of a journalist or blogger’s interests from their own, or their publication’s, published articles. It then refines and updates this profile as new articles are published.
But then it flips things on their head and applies this profile to an inbound aggregated stream of press releases from multiple sources, presenting the individual journalist or blogger with the releases it thinks are most relevant to them - in a given time period, in selected geographies and even on a certain topic.
The user can then set their own personal relevance threshold and subscribe to alerts which pass this test. They can even train the system to improve its predictions by providing feedback on when it is right and wrong.
Making finding relevant stories a quicker and easier task and ensuring that senders of PR know that when their releases are indexed by PRFilter they will be seen by the most relevant media.
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