- Family volunteering ftw #bloombergservice bloombergdotorg nyanimalrescue @ Sean Casey Animal Rescue instagram.com/p/BUUX2e4DOBi/ 2 days ago
- And inexcusably inane headline for an important article via @qz ow.ly/Ud9g30bDe9F 1 week ago
- The Power of Paper ethanmccarty.com/2017/05/05/the… https://t.co/Zlb9o4Mz8g 2 weeks ago
Digital strategy | Social business | People-centric biznology
Digital listening versus digital talking
September 26, 2011Posted by on
One of the things rattling around in my head these days is the emerging importance of the democratization of digital listening. Publishing (or, talking, if you’ll go with the metaphor) has been thoroughly democratized. That is, blogs & wikis & video-sharing & podcasts etc have been made so simple that just about anyone on the planet with access to a cheap PC can do it. Meanwhile, it seems that really sophisticated “listening” systems are still rare and/or expensive and/or delivered by a cadre of professionals who require significant training.
Somehow digital listening and analysis is still a specialized skill and therefore the business model for those who do it is intact.
But, this is going to change fast — just as self-publishing swept onto the scene (and disrupted the business models of those who owned great big expensive channels) so too will tools for digital listening sweep in and sweep out some established players. I mean this will happen when it is relatively easy for individuals to get significant insights from crowds of publishers at low or no cost.
We’re already seeing free systems emerge for establishing who one should pay attention to (for example, Klout, which is flawed, but at least they’re giving it a go.) And of course increasingly intelligent recommendation-engines built into feed readers like Google Reader etc are giving us better insights into what we should be paying attention to. Meanwhile, tools like ow.ly and bit.ly built into platforms like Hootsuite etc are giving us some sense of who is listening to us (well, basic traffic reports etc.)
The integrator who comes along and combines a decent set of these capabilities with some machine-based sentiment analysis (even English-only so long as it is somewhere north of 75% accurate) is going to have a hit on their hands.
Anyway, that’s all I got before before my 9am Monday morning conference calls. Cheers!