What is raw data?
What is raw data?
Raw data, often called primary or source data, is unprocessed information collected directly from its original source. In mobile marketing, it offers an unbiased view of user behavior and forms the foundation for meaningful analytics.
What are the different types of raw data?
Each type of raw data provides critical insights into app performance and user behavior. Raw data can be categorized into several key types:
- Install data: Records of app installs, including campaign source, user device information, and timestamp.
- In-app events: Data on user actions within the app, such as purchases, registrations, and level completions.
- Session data: Information on user sessions, including start times, durations, and engagement metrics.
- Uninstall data: Records of app uninstalls, including timing and possible reasons
- Metadata: Information that provides context about other data, such as timestamps, device types, and user demographics.
Additionally, raw data can be classified as:
- Real-time data: Information that is collected and available immediately, allowing marketers to respond promptly to user behaviors and market trends.
- Historical data: Archived data that reflects past user interactions and behaviors, useful for identifying long-term trends, patterns, and the effectiveness of previous campaigns.
Why is raw data important?
For mobile marketers, raw data uncovers hidden trends and delivers insights that go beyond standard dashboards. It enables precise attribution and informed decision-making. Raw data also forms the basis for machine learning models that predict user behavior and optimize campaign strategies.
By revealing user interactions from the first click to conversion, raw data helps identify key touchpoints and detect fraudulent patterns. It also provides transparency by enabling marketers to verify results and ensure accurate reporting, fostering accountability and trust in their analytics.
How is raw data processed?
To extract actionable insights from raw data, marketers need to process it through several essential steps:
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- Data collection: Gathering relevant data from various sources, such as user interactions, transaction records, and social media.
- Data cleaning: Removing errors, duplicates, and inconsistencies to ensure data quality.
- Data transformation: Converting raw data into a structured format suitable for analysis, often involving normalization and aggregation.
- Data analysis: Employing statistical methods and tools, such as SQL or data visualization platforms, to interpret the processed data and uncover meaningful patterns.
- Data visualization: Presenting the analysis results through charts and graphs to facilitate understanding and decision-making.
- Insight generation: Deriving actionable recommendations based on the analysis to inform marketing strategies.
Following best practices, such as leveraging machine learning for data transformation or creating comprehensive reports, further enhances the value of raw data.
Raw data and Adjust
Adjust provides mobile marketers with direct access to raw, user-level data through tools like raw data exports and Datascape. These solutions automate data retrieval, streamline reporting, and deliver tailored insights to meet specific business needs.
How Adjust's raw data export works
Adjust collects and processes data from campaign links or its SDK, delivering it either aggregated in dashboards or as raw, unaggregated data. Marketers can monitor various key activities, including installs, in-app events, SKAdNetwork (SKAN) events, ad revenue, subscriptions, and even erased users (in compliance with GDPR). Placeholders can be used to enrich the raw data for more customized insights.
Methods for receiving raw data
Adjust provides two primary methods to access raw data:
- Server callbacks: Real-time delivery of data to business intelligence systems.
- Cloud storage uploads: Automatic exports of raw data as CSV files to cloud storage services like Amazon S3 or Google Cloud Storage.
Combining both methods ensures data redundancy, protects against data loss, and maintains uninterrupted access to critical insights.
Datascape: Advanced raw data visualization
Datascape is Adjust’s advanced analytics solution that consolidates raw data from various sources into a single interface. Unlike traditional analytics platforms, Datascape provides real-time performance insights, streamlining data analysis. With granular filtering options, time period comparisons, and direct report exports, Datascape helps marketers quickly identify trends and optimize performance.
Want to make raw data actionable? Schedule a demo from Adjust today!
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