When you start using Adobe Analytics for the first time, Adobe's different variable structure might confuse you. Misunderstanding this infrastructure is a major cause of technical setup and analysis errors. Analysts transitioning from Google Analytics to Adobe Analytics often find Adobe to be very complex, leading to challenges.
Here we need to pause and recall what analytics is.
Analytics is the transformation of available data into information through various sources and technologies, and turning this information into action for the growth, profitability, and other goals of your organization. If analytics were so simple, everyone could easily turn data into action.
To get ahead in competition, it's necessary to solve difficult and complex concepts and simplify them.
Your application and website generate a lot of data, but the technology to autonomously transform this data into actionable insights hasn't been created yet. The goal of data science and machine learning is to achieve this to some extent, but behind all these codes, machines, and algorithms, there is HUMAN.
Data science is labor-intensive and requires mastery over multiple disciplines. If you are not a data scientist, making sense of the data you have is entirely up to you. To get ahead in competition, if you can't create unrivaled products through innovation like Apple and Google, you need to identify opportunities and problems through analytics.
Modern marketing demands this.
Adobe Analytics and Google Analytics are not competing software, but different products. Adobe serves larger enterprises better with its unique variable structure, while Google, with its simpler structure, may be more suitable for companies new to analytics. Particularly in Google Analytics, creative and advanced results can be achieved with the help of SQL and Java Script. But since Google's core product and service don't support such advanced applications much, you need to be advanced in analytics software and development.
I know intelligent people who use this software in this way, but it's not an easy skill that can be spread to everyone. Creating these features through Google is much harder, on the contrary, Adobe Analytics offers these advanced features in its interface with its different variable structure.
To understand Adobe Analytics, you also need to thoroughly understand this variable structure and how the variables are used in analysis. For this, you need to know how the data is created and how the implementation is done. If you don't understand how data is created, you limit your analysis possibilities. The best way to understand this is to work at the stage of data creation. Especially problems encountered while reporting and analyzing arise from the lack of information here.
The concepts I will explain below will be very meaningful if you are currently using this product. It's not very possible to learn these software without practice.
Variable and metric types are divided into standard and custom.
Standard variables are variables and metrics that have meaning at the base of Adobe's infrastructure.
For example, the “pages” report and the “revenue” metric are used only for this purpose in Adobe Analytics and are related to other variables while retaining their meanings. Custom variables are variables that you can use as you wish and assign meaning to. You can assign whatever value you want to these variables, but Adobe cannot understand the content and purpose of this variable. The consultant who will do the technical setup needs to design these variables in the first place. The purpose of the different and complex-looking variable structure is to relate different parameters from different angles. By different angle, I mean:
All the data your customers generated within a specific period.
2-Session (visit) data
The data your customers generated within a specific session. Evar is generally used for this.
3-Hit (page-based) data
The data your customers generated at a single moment. Prop is generally used for this.
The data your customers generated on a product basis. Custom evar called merchandising variable is generally used for this.
Being able to access data with many features based on the ID of the product the customer looked at. This is called classification. It's enough to take only the ID on the page. Later, you can use all classifications in your database through this ID. The price, color, model of the product, etc..
Let's take a closer look at these variables now.
1-Conversion variables (eVar)
Conversion variables (technical name eVar) help you measure conversions and can take any string value. With eVar you can get the answers to the following questions: -What is the impact of on-site campaigns on revenue?
-Which banners were effective in getting registrations on the site?
-How many times was the on-site search made before placing an order?
There are 250 special conversion variables. In addition to 250 evars, there are also standard variables.
Examples of standard variables include campaign, merchandising, list variables. Evars can be associated with events, metrics, and segments.
With the merchandising variable, you can see other features of a product added to the cart on a product basis. This is a variable especially used for retail companies. For example, let's assume that the customer added 3 different products to the cart. Besides the product ID, if we also want to see the product name and category, we need to look at this data on a product basis.
X1 ID, size 36 red dress, fashion category
Y2 ID, brand A baby food, baby products category
Z3 ID, author C's book A, book category
If you set this data at the session level, not at the product level, all these data will mix with each other. Therefore, using merchandising variables is a very vital variable.
List variables help you list a variable that takes multiple values later in Adobe individually. For example, let's assume we show 3 banners to the customer on a page. We may want to know what all these 3 banners are. To understand how different banner combinations affected the conversion.
banner1|banner2|banner3 30 pageviews
banner1|banner2|banner4 30 pageviews
banner1|banner5|banner4 30 pageviews
But when reporting, we may also want to look at these banners individually. I separated the different banners above with a long straight line. Later, with the help of Adobe configuration, list variable gives me the chance to see these banner names individually under a different variable. I can also look at the report above like this: banner1 90 pageviews banner2 60 pageviews banner3 30 pageviews banner4 60 pageviews banner5 30 pageviews
2-Traffic variables (prop)
Traffic variables -prop- are like evar, the only difference is that it doesn't connect with other variables in the session. This variable is also called non-persistent, non-sticky variable. It relates to hit, not session. It only counts as a counter and can only be associated with variables in the same request. When breaking down a variable with another variable, you may want to look at the relationship for only a certain click. Such as breaking down the error name with the page name. The traffic variable gives you this opportunity. Also, the traffic variable is used to turn the variables you want into a path report. For this reason, sometimes we may have to set a variable that we set as evar as prop as well. We can set 75 props. With the new workspace analysis infrastructure, there isn't much need to create props, but it is still usable.
“Revenue”, “order” are standard Adobe Analytics events. Besides, you have the right to set 1000 events. Events are used to count conversions and actions. But besides counter events, you can also set “numeric” and “revenue” events. You can set numbers you want to sum other than income with currency and numeric events. The concepts I explained above can only be understood through constant use of the software and understanding the technical setup. Therefore, if you have access to the software, I recommend that you spend 1-2 hours a day trying to understand the software.
Sibel Akcekaya https://www.linkedin.com/in/sibelakcekaya/