This is the second article about Marketing Channels that I started two weeks ago, with an introduction to Marketing Channels. Today, I will go, step by step, through the process of setting up this tool. Initially, I thought about explaining in one single article how to create the channels and how to configure them. However, I prefer to write shorter posts with more concise information rather than very long articles. So, in in this post, I will concentrate on the initial steps with Marketing Channels.
With this post, I am starting a mini-series on Adobe Analytics Marketing Channels. I will be explaining what they are, how to set them up and how to use them in your reports. A quick Google search shows a number of results, including the official documentation, but I want to give a more comprehensive view on them. In this first post, I will get into the fundamentals of Marketing Channels.
One of the buzzwords in the Adobe Marketing Cloud environment for the last year or so has been “Analytics for Target” or A4T for short. It basically means using Adobe Analytics as the reporting tool for Adobe Target activities/campaigns. Why so much excitement about it?
If you are optimising/personalising the website with Adobe Target and you have presented your reports to other people in your organisation, and these other people have access to Adobe Analytics, I am sure you have received the following question: why does the visitor count not match between the two tools? Typically, the first answer that comes to mind is that Adobe products are broken. I wonder how many Adobe customers have raised a ticket through client care. The answer requires a bit of understanding: each tool counts the visitors differently and there is a reason for that.
There is an emerging role in the digital marketing landscape: the multi solution architect, or MSA for short. You have probably heard of these forecasts about the labour market of the future, when X% of the job titles do not exist today. I would say that the MSA is one of those new jobs. Two years ago, nobody talked about it. Last year, I could see some need for it, at least in Europe. Now, some of my customers explicitly request one. I can even state that I received a job offer a few months ago for exactly this role.
In my previous post, I stated that you, as a Web analyst, should know the details of the web analytics implementation. It is not enough to just understand how to navigate through the Adobe Analytics UI; you also need to know how the data arrived there in the first place. In this post, I am going to show three different ways to debug an Adobe Analytics implementation.
Although this post might look a bit too technical, I believe that everybody involved in web analytics should read it. I know that, too often, we tend to categorise ourselves in “technical” or “business”, but some areas are common to both of these categories and what I am going do describe in this post is one of those areas.
As I mentioned in my previous post about the differences between intent and success, today I am going to talk about my point of view of what a web analyst needs to know regarding the implementation.
A few years ago, I was talking with an intern in my office. While talking about her tasks in the company, she told me that she had not followed the official Adobe Analytics training and, instead, she had taught herself the tool. That sentence surprised me a lot and I did not know what to say. I have devoted 5 years to Adobe Analytics and there are still some areas in which I could learn more. How could an intern learn in a few days or weeks enough of Adobe Analytics to use it with confidence, without proper training? Over time, I have been thinking a few times about that comment she made.
Most of you will agree with me that the main task of a web analytics is to analyse the success metrics under different conditions. In general, this means analysing these metrics against different dimensions, segments, dates… This is precisely where Adobe Analytics excels. I will get into more details in a later post, but, as an analyst, you need to know how the data has been collected. One of the typical examples I give to my customers is when I explain them the different between intent and success.
Have you ever received a request to track and detect online shopping cart abandonments in real time? If you have, then you are not alone. This is a typical request we get from our clients and I have seen too many times. The theory is very simple: if we can detect that a user has added something to the basket but has not purchased it, then we need to persuade him to finish the process. However, the reality is more complicated than just that. Let me explain what I usually discuss with my customers and what options do we have.
According to some news, all major Internet players are now focusing on chatbots. I have never used one but it looks like with the progress in artificial intelligence, we will all be using them in the not-so-distant future. Facebook even claims that thousands of developers are creating right now chatbots. If this is true, then we should be ready for them.
While I read this news, I thought, how about using Adobe Analytics to track the conversations? Would it make sense to large corporations, which already have Adobe Analytics, to use the same tool as with website and apps? I know some people will contend that my idea is wrong, that chatbots will need a different reporting tool. However, I would then reply that, a few years ago, it was not clear whether Adobe Analytics would be used for apps; now, all my clients, want to integrate Adobe’s SDK in all their apps.