There are more than 20 media channels in real life and consumers are constantly bombarded with these channels from different companies.
How do consumers sift through these channels to benefit from these media bombardment to make their decisions to re(search), become sticky to a contact channel allowing the manufacturer/retailer to reach out, and use specific channels for buying products and getting services, given their interests in life and their life stage they are in. This is a constant problem among marketers to figure out these details to be more meaningful and respectful of consumers in reaching out and getting the best possible reponse for their reach out activities.
The technology is here for us to capture quite a lot of these details and also methodological principles and computations that can benefit from these large number of channels to figure out the solution for the above problem.
The engine that runs this application is the reverese of the old days of automated looms, where threads (media channels) come together for a beautical colorful cloth(music). Here we are given a cloth or music and we want to parse out the media channels. This engineering is important so that we can redesign a cloth of certain type which is more useful under some conditions vs. other conditions; certain music or advertisement that will have the right receivable impact at the consumer for right effect.
A good media separator engine will consider the following:
Have procedures, under the demanding current structures data and data collection, to feed the data into consumer media channel separators, to identify and extract the media separators, and media mixers, their signals, adjusting for the impact of channel tails(ad-stocks) in the life-cycle media influence, amplifying signals to noise ratio, to produce quality fidelity with maximum possible signal to noise ratio to attribute what channel contributed to what aspect of (re)search, contact, and buying activities of consumers.