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.
If you are working with a DMP like Adobe Audience Manager, I am sure you have come across the following problem: you want to target your visitors on site, immediately after they log in, using on-boarded data, even on the first visit. This last statement is, precisely, where the problem is. The way AAM processes on-boarded data is as follows:
- You upload your CRM data to AAM, either to an SFTP location or an S3 bucket
- Every 12h, AAM reads all on-boarded data and processes it, converting the signals into traits
- The traits are stored in the core servers
- A visitor logs in for the first time
- Since the communication between the browser and AAM is done through the edge servers, these servers have at this moment in time no on-boarded information for that visitor
- The edge servers where this visitor activity has happened, request the on-boarded traits to the core servers
- In a batch process, core servers send to the edge server the visitor’s on-boarded information
Initially, when we think of segments (or clusters) for ad segmentation, we think of ever-growing groups of cookies. Simple use case like purchasers, visitors to our website, subscribers to a newsletter or owners of a device fit in this model. However, advanced (and not so advanced) use cases do not work well with this model, where we have visitors entering and leaving regularly a segment, so a segment can shrink in size:
- Retargeting dropped baskets: the moment someone places an order, you do not want to retarget him again
- Customers of a mobile operator: it is very common nowadays to switch to a different provider frequently
- Age group: ever day, visitors enter one particular age group or leave it, as people grow older
My job as an Adobe Analytics consultant has involved, very often, bridging the gap between these two worlds. I have seen myself many times as a translator: getting a message from the marketer, translate it into technical terms and communicating it to the developers; and vice-versa. My developer background has helped me a lot in this case. In quite a few cases, I have been requested to join meetings just to make sure that the IT team understood what the marketer wanted. It does not help either the fact that web analytics is not considered as important as it should be.