Using Data Science to Profitably Scale Cross Channel Marketing Campaigns (E135)

Jake CookCEO of Tadpull Show Notes: RFMRecencyFrequencyMonetaryMaster KeyAlign dataSocial Media Slot MachineGoogle AnalyticsSpeed to buyIf people buy in the first day, run heavy retargetingScalingDoes not have good RolesBad cash flow cyclesNot knowing marginsQuarterly GoalsHave a process that's repeatable Sponsors: Drip – Get a free demo of Drip using this coupon code!Spark Shipping – Dropshipping Automation Bio: Jake is a forever curious entrepreneur that works across digital marketing, analytics, and AI. Jake started out as a lost physics major that stumbled into online marketing and eventually design thinking and data science. Helping clients and students use empathy to unlock growth at the intersection of art and science are where he loves to spend his days. Links: https://ondigitalmarketing.comhttps://www.tadpull.com/https://benchmark.tadpull.com Transcript : Charles (00:00): In this episode of the business. E-Commerce I talk with Jay cook about using data science to profitably scale cross channel e-commerce marketing campaigns. This is a business of eCommerce episode one 35. Charles (00:21): Welcome to the business. E-Commerce the show that helps eCommerce retailers start launch and grow their eCommerce business. I'm the host Chelsea [inaudible] and I'm here today with Jake cook. Jake is a founder of tadpole company. He started in 2013 where they use data to help e-commerce brands, profitably scale, their marketing campaigns, as joke in the show today. Talk about how you can use data science to scale your marketing campaigns. So, Hey Jake, how are you doing today? I'm wonderful. Thanks for having me on. Yeah, I am awesome to dig into this topic a little bit. We're talking before the show about, but a few proposed topics and the Xi learning and data science. I feel like this is a new we're in 2020. There's a lot of new things going on, but this is kind of the new hotness right on using with we've now pulled all this data. Charles (01:08): Right. And I feel like everyone's kind of getting a sense of yeah, you can like get all this stuff and now it's like, what do we do with this? So, yes. Yeah. So what do you guys, so first you, so your product, right? Usually don't go into the product the beginning, but it's just kind of interesting right on you guys basically hook into a lot of different pieces of data and start predicting what customers basically gonna want before they know they want it. It's that kind of a, yeah, it's it's we work a lot with mid market companies. And so, you know, there's lots of data and we always say, it's kind of like the kitchen drawer or, you know, the junk drawer you have in your house where you open it up and there's like birthday candles and bandaids and all sorts of junk. Charles (01:51): And that's kinda how their data is structured. And so a lot of what we do is kind of suck into a, we call it a data pond. I mean, there's like Lily pads of data, if you will. So you have Google analytics, data or Shopify data inventory date, and we're sucking all into one place. And when you can kind of get all organized and neatly put together, you start to find some interesting insights. So for example, you might find that customers out of email by, at 30% higher than people that come through Facebook or products that you know, don't have great margins, but they turn like really quick turn out that their total cash cows. And so, you know, well, how do you find the customers that buy those products that turn quickly? Well, now we're trying to map the inventory to the customer data. Charles (02:32): So a lot of that has kind of just drawn those correlations and help them customers see what's possible off these assets. They already kind of already own. Yeah. I feel like a lot of this, even I was looking at something that I did with, ah, just like ad attribution and there's still, it's really tough, right? Because there's the same,