*Updated January 8, 2018

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.

We have come up with a few tips and tricks to keep you on top of the data normalization process and stay ahead of your incoming data.

1. Use 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 data normalization process becomes a lot easier to understand.

2. Avoid multiples by ensuring everything is the same

When is comes to standardizing naming conventions, it is important to make sure everything is exactly the same (from capitalization to spacing and numbers). Nothing more annoying than having a single apps data pop up in separate line items because half the data is coming in as “snake” and the other half as “Snake”.

What if your apps have sequels to them? Again, naming is key. Here is an example: you have data coming in from three sources: Snake, Snake 2, and Snake v2. Now here is the questions, is Snake 2 and Snake v2 the same game? Once you figure out that you need to make the necessary changes so that all your data will input under one line item.

3. Use the Pivot Table to help identify irregularities

One of our favorite recommendations is using our Pivot Table feature to spot 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 comes 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 analyze the performance by app and platform separately making for easier analysis down the road.

5. Set rules to help normalize incoming data

Rules allow you to save the logic behind your naming convention structure. After you’ve created a rule all new items 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: You have two original items, one with “snk ios” and another 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.

• Rule 1: When App=”snk ios” change to Snake
• Rule 2: When App=“snk 2 ios” change to Snake 2

Be careful when applying conventions to rules as well. If you just type App=”snk” you could change all of the line items that include “snk”, even though they are not the same app. In these cases, the order of the rules matter. A rule to differentiate “snk 2” should come before a rule for just “snk”.

6. Custom columns allow you to have more granular groups

Use the 4 custom columns to add extra information to your data to extract deeper insights. By adding more info at the normalization stage you are opening the door for the use of this info in the filtering option of other features and on the PivotTable for easier reports.

7. No need to normalize Geo/Country info

Libring deals with Geo/Country data 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
connection= See you will see only rows for a specific connection
connection:id= Filter the rows by the matching connection ID
connection:original= Filter the rows by the matching Connection Name
connection:alias= Filter rows by matching Connection Given Name
connection:segment= Filter the rows by matching segment (ad network, mediation, attribution, etc.)
id= Filter the row by the line item’s matching unique ID
enabled= all row enabled
disabled= all rows disabled

Last but not least, don’t hesitate to contact us if you have any doubts about data normalization or any other feature. You can also view this video, to see the process within the platform.


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

Libring Interviews

Camilo Fitzgerald Headshot

Check out our interview with
Camilo Fitzgerald, game analyst at Futureplay Games. Camilo talks about How Futureplay Nailed Game Monetization and more.

Read More here


Like it? Share it...
Email this to someone
Share on Facebook
Tweet about this on Twitter
Share on LinkedIn