A winding path to marketing and analytics
Tom Snyder is an agency content marketing manager at Hot Dog Marketing in Austin, Texas. His creative roots in copywriting and his passion for analytics are good examples of the kind of all-around game marketers need to bring to the evolving digital playing field. It also brings needed perspective to a question everybody with a tight budget is asking: How do you know when you have enough data? Is it the right kind of data? We caught up with Tom to hear what he had to say.
What led you to marketing?
Like most, I would say I have a pretty winding career path. After I graduated college, I was a high school math teacher for a couple of years and then I owned my own small business for a few years. I bought a failing coffee shop and to turn it into a success I had to learn how to market, connect with an audience and build a brand. So that’s really where I got my first taste of what marketing is. And then I sold the coffee shop since I got a little tired of running it. It’s exhausting. I got a job as a copywriter at a small digital marketing agency near Austin to get my foot in the door.
I’ve been working there for the past few years and was promoted to content marketing manager. I lead all the brand messaging projects, strategy marketing, strategy projects, and manage the content team.
What do you enjoy most about marketing?
So what I like and actually what I really like is presenting to clients and doing discovery meetings and some of those strategic pieces and communication pieces. But I also really enjoy the analytical side of it, like data analysis. I have some chops in data analytics and I like finding and pulling insights from those sorts of things.
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When it comes to analytics, do you feel like you have too much information or not the right information or what?
Not enough information or not the right information. Typically the really valuable information is the stuff that just costs a lot of money. And so I don’t have the resources to tap into that information. It’s not just publicly available.
One of the issues that I’ve felt is that as many excellent insights as I could gain from buying a data set, like I just can’t ever get approval on the purchase. You know what I mean? So, that’s tough. And if I’m not going to purchase the data set, then starting a proprietary data set is one of the things that I really want to move more into. I’ve done a little bit, but that’s also been just a hard sell to convince clients to do that. But there’s so much value in it, I think it’s incredibly useful. I can even prove the value. But it’s a tough sell because it’s expensive. It takes a long time to show results.
Is there a particular piece of jargon or buzzword that bothers you?
Machine learning is one that I find is misused. Most people don’t need machine learning at all. They just need data analysis. They don’t actually need modeling, it would be overkill. Like trying to cut a birthday cake with a chainsaw.
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