Exporting Data to the BigQuery Tables

Set up data export to tables to regularly update information about the audience, contacts' activity, and campaign results in BigQuery. Available datasets for export:

• broadcasts;
• contactActivities;
• contacts;
• devices;
• events;
• messages;
• orderItems;
• orders;
• revenue.

This information will update the data in the BigQuery tables once a day after setting up.

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Note

You don’t need to pre-create BigQuery tables. They will be generated automatically at the first export, and since then all the incoming data will be updated. All the table names will correspond to the data sets.

Creating Data Source for Data Export

1. Go to Settings → Connectors and click Add data source.

2. Upload the key file and check the boxes for the data sets you want to upload. Click Save.

The connection will be displayed in Connectors → Export to BigQuery, here you can edit the settings.

List of Data Export Parameters

Broadcasts

ContactActivities

*The field will be removed as overdated; use the field indicated in brackets

Contacts

Devices

Events

Messages

OrderItems

Orders

Revenue

*The field will be removed as overdated; use the field indicated in brackets

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Note

Existing entries in the Revenue table (for orders placed after 03.08.2024) can be updated along with the upload of new orders. Updating may be necessary, for example, if the total cost of the order has changed.

Use Cases

Here are some key use cases for exporting data from Reteno to BigQuery:

1. Advanced Campaign Performance Analysis

• Dataset Used: broadcasts, contactActivities, messages
• Description: BigQuery allows teams to perform complex analyses on broadcast performance, message open rates, and click-through rates, helping refine campaign strategies. Exporting broadcasts data with metrics like media type, message status, and segmentation criteria enables a deep dive into which campaigns drive the best user engagement.
• Use Case Benefit: By identifying high-performing campaigns, marketers can optimize message content, timing, and target audiences, improving future campaign effectiveness and ROI.

2. Customer Segmentation and Behavioral Analytics

• Dataset Used: contacts, contactActivities, events
• Description: BigQuery can process large volumes of customer activity and segmentation data to identify patterns in user behavior, such as engagement frequency or preferred communication channels. This enables mobile apps to identify highly engaged users and customize interactions accordingly.
• Use Case Benefit: With detailed segmentation analysis, apps can create highly personalized, behavior-driven campaigns that lead to better user retention and increased lifetime value (LTV).

3. Personalization and Retargeting

• Dataset Used: devices, contacts, contactActivities, events
• Description: Exported data on user devices, activities, and engagement history allows for personalized retargeting efforts based on past behavior. For example, data on contacts’ last activity and device types enables personalized messaging for re-engagement.
• Use Case Benefit: Personalizing messages based on user activity enhances user experience and increases the likelihood of conversion, creating a more dynamic engagement strategy.

4. Predictive Modeling for Churn and Retention

• Dataset Used: contacts, orders, revenue, contactActivities
• Description: With datasets that contain historical revenue, engagement, and order data, BigQuery can be used to create predictive models for user churn and retention. These models help identify at-risk users based on engagement history, purchasing patterns, and campaign response rates.
• Use Case Benefit: By identifying users likely to churn, apps can trigger targeted campaigns or loyalty offers to retain them, ultimately reducing churn and increasing LTV.

5. Order and Revenue Analysis for Strategic Insights

• Dataset Used: orders, orderItems, revenue
• Description: Detailed order and revenue data exported to BigQuery allows teams to monitor average order values, purchasing frequency, and revenue trends. Analyzing these metrics helps identify opportunities for upselling, product bundling, and other revenue-boosting strategies.
• Use Case Benefit: By understanding revenue patterns, mobile apps can make data-driven decisions on pricing, promotions, and customer incentives to maximize revenue and profitability.

6. Monitoring and Alerting on Key Metrics

• Dataset Used: broadcasts, contactActivities, revenue
• Description: With data exported to BigQuery, teams can set up dashboards to monitor key performance metrics like message delivery rates, engagement levels, and revenue. When abnormal trends are detected (e.g., a drop in delivery rates), teams can receive alerts.
• Use Case Benefit: Monitoring allows for rapid response to performance issues, ensuring high campaign effectiveness and consistent user engagement.

7. Campaign ROI and Budget Allocation Analysis

• Dataset Used: broadcasts, orders, revenue, contactActivities
• Description: Exporting detailed broadcast and revenue data enables marketers to measure ROI by matching campaign efforts to revenue generated. This insight is valuable for calculating the cost-effectiveness of various campaigns and making informed budget allocation decisions.
• Use Case Benefit: By analyzing campaign ROI, marketers can focus on high-performing campaigns and optimize spending, ultimately maximizing marketing effectiveness.

8. Customer Journey Mapping Across Channels

• Dataset Used: contacts, contactActivities, events, orders
• Description: BigQuery’s data processing allows the consolidation of user journeys, tracking users as they interact with different channels and messages throughout their lifecycle. This provides a cohesive view of how users progress from onboarding to conversion.
• Use Case Benefit: Mapping the user journey offers insights into potential drop-off points and highlights areas for improvement, allowing apps to enhance the user experience and improve conversion rates.