The data normalization process takes place at the very beginning of your experience with Libring, mainly during the trial. However, normalization is an ongoing process, as you want to make sure that you are constantly retrieving meaningful data to your business. Although it may seem tricky sometimes, it’s good to remember that translating data into an organized list, getting rid of invalid records and standardizing naming conventions will make your life much easier.
So we have come up with a few tips and tricks to keep you on top of the normalization process and stay ahead of your incoming data.
1. Use simple names you can remember
Every company is different. Maybe it’s easier for you to put down the full app name in order to organize everything, but maybe you have a nickname you use for a specific app that has a really long name. By using names and terms you can easily identify, the normalization process becomes a lot easier to understand.
2. Avoid multiples by ensuring everything is the same
This could be as simple as making sure every name is capitalized where it needs to be. Nothing more annoying than having half your data coming in as “snake” and the other half coming in as “Snake.” There is also the fact that many times apps have sequels to them. The data could come into the platform as Snake, Snake 2 and Snake v2. You first need to figure out if Snake 2 and Snake v2 are the same game, after you figure out that you need to change one of them so that all your data will input under one basic term.
3. Use the Pivot Table to help identify irregularities
By dragging and dropping “App” and “Connection” into the pivot table, you can easily identify which apps are not normalized and the connection generating it. This makes it easier to set the rules you want and change information within networks so your data come in normalized.
4. Get rid of the platform description at the end of your app name
You can make the distinction between the platform iOS and Android in the column Platform, so you don’t need to keep the specification in your app name. By doing this, the system will summarize your metrics accordingly and you can easily analyze the performance by app and platform separately.
5. Set rules to help normalize incoming data
Rules allow you to save the logic behind your name convention structure. That means that after you’ve created a rule all the new items coming into the platform will be automatically adjusted according to that rule. Keep in mind that the order of the rules can impact how the aliases are applied.
For Example: Imagine that you have two original items one with “snk ios” and another one with “snk 2 ios”, you want to set their app names respectively as Snake and Snake 2. A good strategy would be to start by first creating a rule for the more generic case and then to create a rule for the more specific one. So that’s what you should do:
• Rule1: Search for “snk” then make app=Snake
• Rule2: Search for “snk 2” then make app=Snake 2
If you do the opposite then all your “Snake 2” line items set by the first rule will be wrongly replaced into ”Snake” by the second rule. The search for “snk” the second time will select all your “snk 2” lines because there is a “snk” word inside it and will convert them as well.
6. Custom columns allow you to have more granular groups
Use the 4 custom columns to add extra useful information to your data so you can extract more insights. By adding info like “Rewarded ”, “Priority Number”, “Position of your ads,” etc. you are able to use this info in the filtering option and on the PivotTable reports more easily.
7. No need to normalize Geo/Country info
Libring deals with Geo/Country normalization internally so you don’t need to worry about it. It doesn’t matter the way this information is formatted. Our smart data collection structure will organize everything automatically for you.
8. Remember to apply our Smart Filters
Smart Filters can be applied if you use the following syntax:
app: – Restricts search to App Name field
platform: – Restricts search to Platform field
ad_type: – Restricts search to Ad Type field
ad_format: – Restricts search to Ad Format field
custom_1: – Restricts search to Custom 1 field
custom_2: – Restricts search to Custom 2 field
is:new – See new rows that haven’t had any Alias applied to them
is:enabled – See only enabled rows (removes filtered rows from the list)
is:disabled – See the disabled rows (removes enabled rows from the list)
is:normalized – See the rows that previously had one or more Alias applied to them
is:selected – See only the rows that had been previously selected
connection: – See you will see only rows for a specific connection.
not: – Specify what it isn’t and take it out of the viewable data
Last but not least, don’t hesitate to contact us if you have any doubts about normalization or any other feature.
What’s New @Libring
We now get data from Attribution partners and correlate them with the appropriate User Cohort so you can dig deeper into your User Acquisition performance.
Read more here