Each week on 5 Minutes With, our host Chase Gunnell talks to experts in the marketing industry. This week, Chase talks to Marty Agius from Semcasting Inc. Semcasting owns proprietary patented technology to build customer audiences for digital targeting. To learn more, visit https://www.semcasting.com
Tell me about Semcasting.
Semcasting’s background was indirect marketing. We owned the National Household database with over 700 data variables per household, hundred and forty million households. They found a way to overlay all of this data across the one point six billion IP addresses in the US essentially allowing us to introduce a product called Smart zones. I became one of the larger clients of Semcasting because I loved the product and was selling it to all my brand clients as well as competing agencies and they acquired my agency in 2014.
How difficult if at all was the transition from owning your own agency and then moving it to being acquired by Semcasting.
Well you know I’m pretty much a self starter. I had been working for another agency for a number of years and was laid off a week before Christmas. So deciding I didn’t want to work for another crazy agency owner I figured I might as well work for the biggest nut that I see in the mirror every morning. They supported me with some of the analytic work they do. So moving over to work for Semcasting was a pretty easy transition as a matter of fact I walked outside my agency and I changed the sign on the front door on November 1st of 2014 and I became Semcasting.
Can you tell me a little bit about why it is so unique.
You know the cookie for digital marketing was invented in about 1996 by Mosaic and it was used really as a tracking tool. And if you think about what your cell phone looked like in 1996, it be about the size of a brick now. So Semcasting thought there’s got to be a better way and as we’re all seeing right now in today’s environment, the cookie is dying. So Semcasting actually introduced a patent that allowed us to target households. we are targeting households based on the demographic attributes of the home. So for example if a bank wanted to sell a home we might go into our database and select people that have lived in their homes for five years or more, had a certain value of the home, and maybe even had children of college age who they were going to need some cash to help pay the tuition. Having the level of household data that we have is is really fantastic for our users.