Data & Analytics for Direct Mail Marketing [Video]

Data & Analytics for Direct Mail Marketing

Join COO Liran Kapoano and List Manager Jeff Gonnello as they discuss the interesting aspects of tracking and analyzing direct mail data.

3 minute read

Direct Mail Marketing, videos

Video Transcript:

0:00:00 – 0:00:23

LIRAN: You know, one of the things that we hear often from people who’ve never done direct mail before: “So how do you track it? How do you know where if it actually worked or not?” And it’s like that to me is a crazy question. Every advertising we send out has its own phone number, sometimes just based if we use a different color on the post, on a postcard or on an envelope or whatever.

0:00:24 – 0:00:48

We’ll assign a different phone number or a different URL or a different QR code just to track any little change that we make. So everything is obviously trackable and we look at these analytics all the time. What would you say, Jeff, would be your biggest, you know, most interesting thing that you’ve learned from analyzing things like response rates and conversion rates and things like that when it comes – as it pertains to direct mail?

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0:00:49 – 0:01:09

JEFF: Sure. When you – looking at the data to me, I kind of look at it like chess or like a big puzzle that you have to figure out. Some things fit in, some places, some things don’t. I just find it interesting how certain products work better in different demographics in terms of like age or states, even just based off the zip code or the zip code, town to town, it could be drastically different.

0:01:10 – 0:01:49

I find it interesting to connect all those dots and see what the best outcome could possibly be.

LIRAN: Yeah, that’s interesting because, you know, that’s another thing that we also are very hyper focused, I would say, on the regional variances at the household level though, right? What kind of car do they drive? Where do they shop? Do they shop at Walmart? Do they shop at Macy’s? Do they shop at Nordstrom? They shop at Target? All that information is available at a household level and crucially at – comparing someone who lives in the 08816 zip code who has all of those things in common. Right?

0:01:50 – 0:02:13

And has all of those characteristics verses someone who lives in the 10057 zip code might be very different because in 10057, that’s Manhattan, right? And making $100,000 or $50,000 is not going to be the same as someone who lives in New Jersey. So even if you have all those things in common, and even if you live in the same spot or you live in different areas, that’s a differentiation.

0:02:14 – 0:02:13

And with postage being what postage is, and that’s always our number one expense. Right? The less we can send out on a mailer basis, the less mailers we can send out. While not focusing on the ‘wrong person’ for that product, we’re able to eliminate, you know, potentially 80, 90% of the mail that would never be opened and never read and never be opened and read and just not resonate. And that’s, you know, that’s another thing that I think sets us quite apart from a lot of our – a lot of other people in the space who primarily do things like targeting by age and income. When you’re only looking at a narrow set of characteristics, you’re not really doing very much. There’s very little value in that.

0:03:02 – 0:03:05

JEFF: If you want to learn more: visit dksmo.com

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