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Articles by Craig Scribner
The Challenge of Digital Campaign Tracking
Have a look at this image. Sound familiar? Web analytics has held out the elusive promise of being a set-it-and-forget-it kind of thing. “Set up your reports, and the data will fill itself in.” That promise has largely held true — for every part of web analytics except Marketing. That’s because with marketing, the web page you have today isn’t the one you had yesterday. There’s constant change: new information, new deals, new parameters. What everyone wants is a system that runs itself.
Test-Driven Development in Analytics
In the 2017 world of IT and systems engineering, Test-driven development (TDD) is quickly becoming the new mantra. No one writes a line of code these days without the intent to have that code check/test itself. If there is bug in that code, it gets caught and fixed before it goes live, reducing any risk of breakage. This kind of system has never been deployed on the analytics side.
Why I Love Data
Anyone who knows me knows that I work in data analytics because I enjoy it. For people whose brains work differently, this can be hard to understand. Occasionally someone will ask me, “What intrigues you so much about this field?” I look at it like a game of chess. There are problems to be solved, and questions to be answered. The analyst’s challenge begins as a blank slate, with every conceivable route to data reporting available.
Google Analytics vs. Adobe
So many people have written about the pros and cons of Adobe versus Google Analytics (GA). A quick search on the comparison will bring in a huge number of opinions. As a tracking code solutions provider with a considerable interest in the debate, what fresh perspective can we offer? Let’s start with a bit of background. GA caused a huge wave throughout the industry. GA’s biggest disruptive element was that it was a free solution.
The Problem that Motivated Me to Start a Web Analytics Company
The problem that motivated me to start my company is the same one that motivated me to leave my analyst roles and become a consultant. I spent time early in my career as an analyst and account manager at Omniture, but instead of providing the analytics insights I was ostensibly hired for, I constantly found myself in the position of offering grief counseling to clients who weren’t getting what they expected and needed from their campaign tracking.
3 Reasons Why Digital Ad Campaigns Fail
I’ve worked in digital media for almost 20 years, with 15 of those years working for an online news publisher supported by advertising. I can tell you that 15 years of watching thousands of ad buys of all sizes and campaign launches of all types, and seeing the subsequent click results, gives you a sixth sense about which ad campaigns are going to generate a response and which will miss the mark entirely. Below are some of the typical traps that vendors fall into in their digital marketing.
A Marketing Analytics Look Under the Hood, Parts 3 and 4: Link Assembly & Data Capture
Tracking codes are typically appended to landing pages in the query-string parameter section of the URL. Any time you click on a sponsored ad anywhere on the internet, you’ll see not just your destination’s domain in the URL, but usually a question mark, and a long, unintelligible character string. Somewhere in that morass you’ll find the tracking code, but its placement is something that companies configure independently of one another.
A Marketing Analytics Look Under the Hood, Part 2: Code Classification
Task 2: Classify Codes According to your Own Organization’s Standards Classifications assemble your tracking codes into channels, groups, families, etc. They let you report on the high-level successes, failures, or fall-out of your campaigns, without sacrificing your ability to monitor the individual initiatives when you need to. Classifications actually provide a vital clue in identifying the major ingredients of your tracking codes, as well as what they mean.
A Marketing Analytics Look Under the Hood, Part 1: Code Composition
Task 1: Generate a Unique Tracking Code for New Campaign Initiatives This part looks easy on the surface, but it carries some hidden complexities. Generating a random string for every new initiative is surely no challenge for a computer, and robust tracking systems such as Adobe Analytics would have no difficulty marshaling all of those random codes on the reporting side. But very rarely will you see companies using that method.
This is Not a Job for Humans
The very last week that I worked as a web analyst for a Kansas City agency, a toxic email was thrown over the wall from the marketing side to ours. It was quickly passed like a hot potato until it landed in my lap. The ask: could we run a quick check on the campaign links they had prepared for a big release, just days away? The total number of links needing a “quick” check: 8,472. Situations like this are actually pretty common in an analyst’s line of work, but this one was special.
Taming the Last Frontier in Analytics and Marketing
Not that many years ago, the field of data analytics was like the wild west, with stakeholders making their claims without considering how their interests should be governed within a wider community. But now, maverick analytics implementations are a dying breed. Tag Management Systems are here to provide order, allowing us to lay out standardized tags across our digital properties like so many row houses.
SAINT Myth No. 1: Classification Reports Always Mean What They Say
Adobe Analytics clients are genuinely surprised to learn how far off the mark their own reports have strayed. SAINT Classification technology isn’t rocket science—anyone who has used a Pivot table or a Lookup function in Excel is capable of getting their head fully around the deep magic involved. I could tell people a thousand times that their reports might not bear scrutiny, but instead let me make the case with four pictures that will do the job better.
Why Does Everyone Hate Sub-Classifications in Adobe Analytics?
Since their inception, Sub-Classifications (aka “Nested Classifications”) have come to represent a hiss and a byword among most Adobe Analytics/Omniture veterans. Flat Classifications (left) are, as a reliable rule, better than Sub-Classifications (right) In one of his past OmnitureCare mailbag blog posts, Adobe Analytics guru Ben Gaines was asked about weirdness in classification reports.
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