In my previous post, I introduced Adobe Sensei. Two of the examples I gave, of tools using this feature, were anomaly detection and contribution analysis. Last week one of my customers asked me about these features so I thought I would explain them in more detail in this blog.
When I started consulting for Adobe Analytics, I remember I had to explain a few times what the basic analytics metrics are. Once you get used to them, you do not realise it can be difficult to first understand them. So, if you are starting with your web analytics career and are still wondering what exactly a visitor, a hit or a visit is, this blog post is for you.
One trend that I have seen in the last few years is the interest of my customers in getting the raw data out of the Adobe Marketing Cloud. More and more corporations are hiring data analysts and these people want all the data they can get. Using various tools (R, Hadoop, Data Workbench…), it is possible to dig deeper into the data to uncover hidden gems or create more sophisticated reports. Today I will explain the raw data from Adobe Analytics, the clickstream data feed.
Processing rules are basic if-then-else statements to perform minor manipulations of the data. They were added mainly to map context data variables into Analytics variables. However, you can also use them in simple cases instead of a VISTA rule. With processing rules, you can concatenate, copy or set values in Analytics variables. They must not be confused with Marketing Channels processing rules, which are specific for Marketing Channels.
One of the main challenge, if not the most important, of digital marketing is to be able to identify real people. This is the key to marketing objectives like “360-degree view of the customers” or “single view of consumers”. The problem is that web analytics tools track only visitors, so we need to find a way to be able to perform this people identification. Let me explain what options do we have.
If you have followed my previous two posts, you should understand by now the basics of Analytics segments and its containers. I could write more about the creation of the segments, but today I want to explain one of the features that make the Adobe Marketing Cloud a multi-solution offering: share segments. With this feature, you can create a segment in Adobe Analytics and use it in other solutions, like Audience Manager or Target.
In my last post about the basics of Analytics segments, I briefly touched upon the segment containers: hit, visit and visitor. However, I remember how long it took me to understand them initially. And not only me; some of my clients did not find it easy to learn exactly how the different segment containers work. So, I have decided to explain it in so you can learn once and for all, if you still struggle with them.
Those of you who have been long enough in the Web analytics market, will remember that in old version of SiteCatalyst, there was no concept of segmentation. As an alternative solution, you could use ASI slots, DataWarehouse segments or VISTA rules. However, these solutions were clunky, rigid and, sometimes expensive. The Adobe Analytics segments as we now them today come from the release of SiteCatalyst 15. Initially, the tool was still immature, but over time, it has become more sophisticated and it is still evolving. In this post, I will be covering the very basics of segment creation in Adobe Analytics.
Adobe Analytics is very good at reporting on revenue. This metric can be used together with virtually any dimension. You get both granular and high-level views of revenue and you can even track multiple currencies, in case you sell in various regions with different currencies. However, there is one limitation: it is not possible to report on multiple currencies; the reports only show the report suite’s currency. But not all is lost; it is possible to get multiple currencies with a specific implementation, which I am going to show you next.