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19 Oct Marketing and Analytics – Part 3: Analytics is the Future

Recently, I sat down with one of our newer clients, the customer intelligence director for a global hospitality brand, to find out more about the data challenges faced by his organization. In Part 1, we discussed the chief data challenges faced by his organization, and in Part 2 we discussed the value of data quality more generally.   Q: I’d like your sense of what’s next for the Future structure of the marketing organization. A:  I started saying this about five years ago: “Give it 10 to 20 years, and we won’t be talking about analytics anymore, we’ll just be talking about business.” I think that’s where we’re slowly going, and where we need to go -- a world where intelligence drives business. The purpose of analytics is to know our customers better, so we can serve them better. And really, that should be everyone’s goal. We just use different tools in pursuit of that.
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16 Oct Marketing and Analytics – Part 2: The Value of Data Quality

Recently, I sat down with one of our newer clients, the customer intelligence director for a global hospitality brand, to find out more about the data challenges faced by his organization. In Part 1, we discussed the chief data challenges faced by his organization, and the disconnect between knowing that things are “trackable,” and knowing what that actually means.   Q: In defense of marketers who are struggling to keep up: the marketing/tech landscape is moving very quickly. A lot of marketers are feeling like they’ve been caught with their pants down, with all they’re expected to suddenly know. A: Things have moved really fast, especially in the tracking space. The funny thing is, we take all this tracking for granted now. But if you actually look around at the number of companies that are effectively using tracking, and how long they’ve been doing it, there aren’t many companies that are truly using tracking codes the way they’re intended to work. They’re not truly using tracking code data, they’re using these long strings instead.
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11 Oct Marketing and Analytics – Part 1: Are we there yet?

Recently, I sat down with one of our newer clients, the customer intelligence director for a global hospitality brand, to find out more about the data challenges faced by his organization. Our conversation covered a range of topics. It’s a candid picture of the current marketing/analytics landscape, and we present it here in three parts.   Q: Thanks for taking the time to talk! Let’s get right into it. What are the major challenges you face, with regard to marketing data analytics? A: The biggest challenge right now is data integration, which has two main aspects. There’s an issue with being able to track data accurately across time -- in other words, setting things up so that newer data can be consistently compared to older data -- and there’s the chief challenge, being able to integrate the data generated by various platforms: DCM, AdWords, Facebook.
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27 Sep We are All Publishers Now

I spent 15 years working in the online publishing industry. One of the last professional events I attended while still on the media was an IAB-affiliated event about the “future of media and advertising.” I recall one of the speakers ignoring the publishers in the room completely. He directed his remarks toward the vendors and big brand players in the room. His advice to them was “work toward a future where your organization produces content that is so good, you’ll put the Industry Trade Magazine in your market right out of business...” Since I worked for a big B2B trade publisher at the time, I found this idea kind of out there. But it definitely stuck with me.
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14 Sep Tracking First’s New Product Release Goes Live

In September 2017, Tracking First released its first major product enhancement, a connector that allows clients to port data from any source to any target. I sat down with founder Craig Scribner to learn more.   JR: What was the impetus for the recent product development? CS: More and more our clients are having a less hands-on experience with the tracking codes produced inside their analytics systems. Back in the old days you could manage all your tracking codes with spreadsheets, and you could have different breakdowns, from the most granular to the most abstract, that were comprehensible for a human.
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08 Sep When your trusted tools become antiques, or …

One sure way to situate a movie scene in the mid-20th century is to stage a young man holding a wrench in the act of leaning over a car engine, happily tinkering. In those days, many of the processes going on under the hood of a car were accessible to anyone willing to get their hands greasy and spend some time. My last mechanic taught himself how to fix cars in Guatemala several decades ago. Even my mom used to have a little trick she would perform under the hood (you might call it easily reversible sabotage) that let her disabuse people of the notion that women are helpless around engines. By comparison, today’s automotive systems are much more complicated, much more opaque. It’s hard even to see what’s going on or how to approach repairs without sophisticated computer diagnostic gear.
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28 Aug Marketing Performance Measurement: Where Are We?

Over the last couple of years we’ve heard a lot of noise about marketing becoming a “revenue center, instead of a cost center.”  I first heard this particular soundbite in 2014, skillfully laid out by a very engaging and data-savvy presenter. I was attending one of those huge annual marketing conferences in Las Vegas. It was a great event -- I learned a ton. But there was also a pep rally feel to some of the keynote talks. While I, and many others, walked away with a new understanding of the Accountability Horizon that the digital marketing industry is moving toward,  in retrospect our celebrating seems premature. It was as if someone shouted, “Hey, we’re Marketers -- in Las Vegas... Woo-hoo, we see the Digital Future! Let’s party like we’re already there!”  And we did. The hangover has lasted three years.
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16 Aug How much of your marketing spend goes untracked?

How much of the money your company spends on advertising and marketing goes untracked? Or suboptimally tracked? Do you know? Does anyone in your organization actually know? The digital marketing industry is suddenly awash in new technologies that allow companies to optimize their marketing spend by refining attribution, to the point where each step along the customer journey (in somes cases including analog or offline touches) can be measured and assessed for their impact on revenue. But the best optimization and attribution tools in the world can’t help you derive real insight if the initial tracking data you used is messed up. Likewise, if the tracking data you’re using can’t be looked at holistically -- for example, if you can’t directly compare performance from one platform to another because your Facebook campaigns don’t use the same tracking conventions as your DCM campaigns...your ability to derive real insights for your organization is limited, at best.
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09 Aug Adobe’s Classification Rule Builder and Tracking First

Every once in awhile, we get a question about how Tracking First is different from Adobe’s inbuilt Classification Rule Builder (CRB). It’s a good question, and a pretty easy one to answer. The simplest and most important thing to say is this: use of Tracking First and Adobe CRB is not an either/or kind of a thing. In fact, a best practice would be to use both. The main dIfference is whether your tracking codes will be prepared and described before the campaign goes live (using Tracking First), or only after the campaign goes live (using just Classification Rule Builder). If you’re going to use Adobe CRB, it’s important to keep in mind that you need to take care that your ‘regular expression’ (aka your RegEx) does not overmatch. In other words, you must not use too broad a brush in your matching logic. If you do, you run the risk of inadvertently destroying or overwriting your previous rules -- creating values for classifications that never should have been created. In designing your rules, you have to think not only of what data matches your logic, but of what might accidentally match, and of what doesn't match. You can ruin good data accidentally by using a logic expression that isn’t constrained enough.
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