Receiving raw data from Adjust can be a huge leap forward for marketers wanting to optimize marketing and campaign performance.
In fact, we often see that once a client starts receiving data in a raw format, it’s not long until they realize they’ve gained an incredibly versatile learning tool. The number of ways they learn to use raw data then increases exponentially.
Since the value of raw data is a vast and complex topic (particularly because there isn’t a ‘one size fits all’ approach) let’s go back to basics and take a look at what raw data is, how it’s handled by Adjust, and why it has immense potential as a learning tool for all verticals.
What is raw data?
When we talk about ‘raw data’, we’re discussing information that hasn’t been autonomously or manually processed. This means that it can be ingested and analyzed however an analyst or marketer requires.
But how do you know when you’re ready to receive raw formats? For starters, you’ll need a sufficient BI system or servers that can process this data, and a clear outline of what you want to learn.
With this in mind, let’s take a look at what can be learned by implementing this process.
Data integration with raw data from Adjust
A major factor contributing to why clients receive raw data from Adjust is that it can be integrated with other data sets at their disposal. This is because data points for the same user can be tied together for various purposes, such as:
- Retargeting users based on their previous behavior/location/etc.
- Information needed for efficient troubleshooting
- Connecting a user’s desktop and mobile activity
- Connecting cross-platform information (such as purchase events)
With this in mind, let’s take a look at two different examples of how clients are integrating raw data.
Using raw data to connect in-store and in-app activity
Firstly, let’s take a look at a common problem for companies that offer in-store and in-app purchases. Although it’s likely that their app users are also purchasing in-store (and vice versa), they aren’t able to independently connect this activity and attribute it to a single user.
This is where data integration can offer a solution.
Let’s say a client has a store with an eCommerce app. To better understand and reward users who are loyal to their app, they could decide to offer users a loyalty card. Once users claim their loyalty card, event callbacks for that app (such as ‘added to cart’ and ‘purchase’ events) can be connected to a user’s in-store activity. This is because you can integrate this raw data with other data (gathered from loyalty cards used any time a user makes a purchase).
This presents numerous benefits:
- Gain a more accurate calculation of your users’ revenue value
- Learn whether users who abandoned items in their cart made an in-store purchase instead.
- Understand user behavior relevant to your app (ie. do users prefer to purchase in-store or in-app?)
- Gain insight into your app users’ needs and optimize retargeting campaigns
- Reward users for their loyalty (with discounts, special offers etc.)
This is just one example of how data sets can be integrated for various advantages. Now, to show the versatility of data integration, let’s take a look at an entirely different example.
Troubleshooting with callback parameters
“I don’t see [Adjust] as an attribution system, I see it as a system to connect other things together.”
As the App Store's #1 singing app, detecting streaming issues as soon as possible is imperative. But issues could occur in a number of ways, and Smule needs to identify the source of the problem. Here’s how they solved this issue by receiving raw data from Adjust:
“Through Adjust’s SDK, we [can] send the events through Adjust. We put on a unique ID, and we send that ID to our internal system using callback parameters. That allows us to match data on the backend. For all those events that fall within a 10-second range, we can interpolate and assign IP information. That way, we can troubleshoot much more quickly when we see certain events.”
This example goes to show the versatility of integrated data, and you can find more examples of Smule’s use of Adjust’s callback parameters here. If you’re interested in knowing more about Adjust’s callback parameters, you can also check our documentation on GitHub.
Although metrics such as average revenue per user (ARPU) can be useful for marketing strategies (especially as it’s needed to calculate LTV), averages don’t give you the bigger picture. This is precisely why granular visibility is so valuable to marketers.
If you have one hand in a bucket of ice-cold water, and the other hand in a bucket of boiling hot water, the average temperature would appear to be fairly comfortable. However, granular visibility becomes the equivalent of looking at the temperature of each finger, informing an actionable response (ie. seek medical attention!).
Now let’s apply this to mobile marketing: receiving raw data means you can map the user journey in your BI system and gain granular visibility into user behavior. So, rather than learning that 5% of your users make a purchase each month, you could look into why other users may be losing interest.
In this case, granular visibility presents an opportunity to improve your app’s UX and UI design and increase ARPU.
There’s a simple but effective logic that can be applied to the use of raw data: greater insights lead to better predictions. From retargeting to referrals, raw data presents multiple ways to better understand the value of your users and develop knowledge-driven strategies. For example, optimizing strategies by estimating a more accurate user lifetime value (LTV).
With regular attribution reporting, you can already determine your users’ LTV: all you need is churn rate, revenue and number of users. So even without raw data, you probably rely on LTV calculations to some degree. But with the ability to integrate data and gain granular visibility, you can gain an unprecedented understanding of these measurements and use it to your advantage.
For example, ingesting raw data means you could analyze LTV by time zone, region and demographic at device-level. With this, you could discover that LTV is exceptionally high for young males in Germany, or that your global female audience shows the most app loyalty. This can then inform your budgeting with long term goals in mind.
Because LTV is in a constant state of change, fresh data will better inform your LTV as time progresses. So the longer you’ve been receiving raw data, the more accurate your LTV calculations are going to be.
Integration, granular visibility and knowledge-based strategies are just a few examples of how mobile marketers are currently making use of their raw data. But regardless of how you want to utilize this information, both parties have a responsibility to ensure that user privacy is maintained to the highest standards.
GDPR compliance and user privacy
At Adjust, we take user privacy seriously. We’ve been GDPR compliant since before these regulations were introduced, and have been awarded the ePrivacy seal of approval. This is because any device-level data we store (such as Advertising IDs) is either:
- Stored with a non-reversible hash
- Removed after 28 days
- Contains identifiers such as IDFA or GAID (gps_adid), which can be altered at the user’s discretion
When it comes to receiving raw data from Adjust, it’s up to the client to ensure that they are handling this data in compliance with GDPR. This applies to all companies with users inside the EU.
Once you’ve decided what you want to learn and ensured the way you’ll handle raw data is fully GDPR compliant, here’s how to get started.
How to receive your raw data from Adjust
With Adjust, raw data can be sent to our clients in one of two ways:
- CSV exports to an Amazon S3 Bucket or Google Cloud Storage
- Via callbacks from Adjust that sync raw data (in real time) to client’s servers or business intelligence (BI) systems
Particularly when looking to gain a complete view of the user journey, clients receiving raw data may also be interested in our new offering, Adjust Multi-Touch. With the launch of Adjust Multi-Touch, clients can now gain an even greater view of the path to conversion. By including additional data from self-attributing networks (SAN), Adjust Multi-Touch offers the tools to go beyond the last engagement and enhance mobile marketers’ learning potential.
If you’d like to start receiving raw data from Adjust, please get in contact with your Account Manager or our support team. To find out more about Adjust Multi-Touch, take a look at the Adjust blog and our product update announcement.