Data standardization: How to keep your analysis effective

By Jillian Gogel
data standardization effective analysis

In this age of highly competitive app marketing, where new apps spring up every day and the level of savviness among marketers grows higher and higher, there can be no room for error. 

As a foundation for it all is the need for data standardization across all your data sources.

Marketers have to take responsibility for the data sent by ad networks on attribution links, making sure that data is aligned with their analytics needs and at the desired granularity.

Additionally, they must ensure that the inputted macros (dynamic placeholders on the ad network side) match the attribution link’s parameters (dynamic placeholders on the MMP side), or that data can be recognized by the MMP in the first place. 

Without consistency in naming conventions and macro-parameter matches, there can only be, as the saying goes, “garbage in, garbage out.” 

What can you do to prevent discrepancies in your data and keep your measurement accurate?

Here are four of our strongest recommendations. 

1. Always use networks IDs; don’t hardcode your parameter values

One reality when working with ad networks is that the network will always send data with its own naming conventions and macros, regardless of the set up in your own dashboard. Therefore, instead of hardcoding your own macros and possibly creating many discrepancies between the network’s data and your own, stick with the provided macros. 

This data gap results in a no-match, since, if the network reports data in their ID while you use your own IDs, you won’t be able to join performance, spend, and attribution data at all.

data standardization

2. Keep it simple

In order to achieve certain levels of granularity, it may be tempting to load up the attribution link’s parameters with many layers of macros.

However, the moment there is more than a single macro per parameter, the ability to accurately match data significantly decreases. Instead of overloading your data, place each macro in its unique placeholder to reduce the possibility of error in your analyses.


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This data gap results in an incorrect, or incomplete, match in the best case, as some or most of the complex parameter may not be recognized, or a no-match, in the worst case.

data standardization

3. Standardize your macros

On your end, it is also important to use the same macro across the corresponding parameters in different networks. Since we see that networks often send different macros than other networks, the given set of macros you may receive from your networks may be varied, even if they are all intended for a single parameter.

Although standardization requires a time investment to sort through these different macros and change to your own macro name, your analyses will be much better off for it, providing complete and unified granular analysis of your marketing performance, spend, and ROI.

This data gap results in a wrong match, since your attribution link’s parameter, af_ad, may be consistent for the macros you send, but the macro names themselves differ between networks and thus may not always match your parameters.

data standardization ad networks

4. Ensure macro meaning

It may sound simple, but make sure your macros mean what you think they mean. As we can see from the example above, the naming conventions networks use are not always aligned, so {adname} from Network 1 may not have the same meaning as {adname} from Network 2 and should most accurately be mapped to a different attribution link parameter.

For that reason, always double check to make sure you’ve understood your networks’ terminology before setting up your link’s parameters. 

This misalignment means that you will be looking at a parameter as one dimension when it actually reflects multiple dimensions, therefore leading you to make decisions based on incorrect or incomplete data.

data standardization macros

Bottom line

We’ve listed above some of recommendations you can integrate into your data analysis routine, but at the end of the day, it’s up to you to put them into practice.

Still not convinced? Think of it like this: not only does data standardization help you organize complex data sets, but it simply makes analysis easier – slice and dice by multiple dimensions, and, most important, transform your raw data into actionable insights. 

At the end of the day, while the mobile industry is fragmented as a whole, and efforts are being made to standardize the complexity, it is still ultimately in the hands of marketers to own their data and make sure that they enforce the consistency they need to keep their analyses effective.

Jillian Gogel

Jillian Gogel is the content marketing manager at AppsFlyer. With a background in conflict analysis and resolution, she combines creative analysis and a strategic mindset to solve complex communication problems. She is passionate about building sustainable relationships between partners, marketers, and customers with data-driven content, and plans to take content strategy to the next level.

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