Crescender Lab
Created Oct 31, 2023Tutorials｜BigQuery Connector
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Advantage
Effective analysis of cross-channel marketing performance
Achieving personalized cross-channel communication
Creating multi-dimensional, precise audience groups
Integrating a 360º consistent customer profile
Let's start with a simple understanding of what BigQuery is. BigQuery is a cloud-based data analysis service introduced by Google Cloud. It can handle data analysis operations at a scale of petabytes (PB). Some of the services you use in your daily life, like Google Search and Google Ads, rely on BigQuery as a core technology for data processing and analysis. BigQuery also comes with built-in machine learning capabilities, allowing users to perform more in-depth data analysis according to their needs.
4 main advantage of BigQuery
Fast: BigQuery is incredibly speedy. It can retrieve or analyze data at the scale of terabytes or petabytes in just seconds.
Versatile: It supports a variety of Business Intelligence (BI) tools and allows integration with third-party applications to enhance data analysis.
In addition to the above two major advantages, BigQuery uses SQL-like syntax to make it easier for users to query data. Data access is also encrypted to strengthen the security mechanism. As for the DR (Disaster Recovery) capability that everyone is most concerned about, BigQuery provides a wide range of data. Copy function to avoid the risk of data loss.
Please confirm that you have already obtained the following data from Crescendo Lab Team before proceeding with the setup on your existing platform:
GCP project id
Dataset id
Client email address
Secret.json file
How to create Segment Templates in Emarsys to retrieve data, please refer to：Creating relational segments from Emarsys
Considerations
Proficiency in SQL programming language is required.
Data synchronization is one-way from MAAC to BQ.
Consumption behavior (Transaction) tracking data is only available through domain Web GA data that has been connected to the MAAC platform.
There is a daily usage limit of 500 GB per individual customer.
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Explanations of the relevant data types provided by BigQuery
Currently, using BigQuery, you can access contact attributes, labels, open rates, click data, and consumption behavior data collected from Crescendo Lab's MAAC product database.
Update frequency
Attribute profile data: Updated daily at 00:00 AM
Event-type data: Real-time updates (with a potential 1-2 minute delay in case of system overload)
Attribute profile data - Contacts
Clients can filter out contacts by the date of contact added to MAAC, the date will align with the contacts created time on MAAC UI
Attribute profile data - Tags
Event-type data - Transactions
Consumption behavior (Transaction) tracking data is only available through domain Web GA data that has been connected to the MAAC platform.
Event-type data - Message send
Event-type data - Message open
Event-type data - Message click
Each message open and message click are independent BQ events.
If you need to retrieve data on message opens and click interactions when pushing messages using the Open API, please make sure to create an event ID.
In MAAC, different functionalities generate click interaction data. If you use the same UTM, it will result in two separate data entries (with different campaign names).
Event-type data - Prize send
Event-type data - Prize send
Event-type data - Prize redeem
Event-type data - Contact Profile Customer ID update
Event-type data - Contact tag add
Event-type data - Contact tag remove
The engagement history data is collected after the connector is established