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

Once configured, BigQuery tables automatically update daily, with no need for pre-creating tables. Tables are generated on the first export, with all future data seamlessly synced, and each table name corresponding to the specific dataset.

Creating a 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

Use Cases

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

1. Advanced Campaign Performance Analysis

• Dataset Used: broadcasts, contactActivities, messages
• Description: BigQuery enables e-commerce teams to analyze campaign performance in-depth, tracking key metrics such as open and click-through rates. Exporting data on broadcasts, including segmentation criteria, delivery status, and message type, allows marketers to assess which campaigns achieve the best engagement.
• Use Case Benefit: By identifying the most effective campaigns, marketers can optimize message content, timing, and audience targeting, enhancing the impact and ROI of future campaigns.

2. Customer Segmentation and Behavioral Analytics

• Dataset Used: contacts, contactActivities, events
• Description: BigQuery can process large volumes of customer activity data, enabling e-commerce brands to spot behavioral patterns such as purchase frequency or preferred interaction channels. This insight allows for deeper segmentation, making it possible to target highly engaged customers more accurately.
• Use Case Benefit: Tailored, behavior-driven campaigns lead to increased customer retention and higher lifetime value (LTV) by delivering experiences that resonate with each audience segment.

3. Personalization and Retargeting

• Dataset Used: devices, contacts, contactActivities, events
• Description: By exporting data on user devices, engagement history, and past interactions, e-commerce teams can implement personalized retargeting. For instance, messaging can be tailored to recent activity or device type to enhance relevance and engagement.
• Use Case Benefit: Personalizing messages based on user actions and preferences improves user experience, boosts conversions, and builds a more dynamic engagement strategy.

4. Predictive Modeling

• Dataset Used: contacts, orders, revenue, contactActivities
• Description: Historical data on revenue, purchases, and engagement in BigQuery allows for the development of predictive models to forecast customer churn. This enables brands to identify at-risk customers and target them with retention strategies.
• Use Case Benefit: Targeted retention campaigns reduce churn and increase LTV, helping brands retain valuable customers and improve long-term revenue.

5. Order and Revenue Analysis for Strategic Insights

• Dataset Used: orders, orderItems, revenue
• Description: By analyzing order and revenue data in BigQuery, e-commerce businesses can track metrics such as average order value and revenue trends. This analysis helps identify upselling and bundling opportunities.
• Use Case Benefit: Insights into revenue patterns allow for data-driven decisions on pricing, promotions, and product offerings, maximizing revenue potential.

6. Monitoring and Alerting on Key Metrics

• Dataset Used: broadcasts, contactActivities, revenue
• Description: BigQuery dashboards can monitor key performance indicators (KPIs) such as engagement levels and revenue trends, with the ability to set up alerts for significant changes (e.g., a sudden drop in delivery rates).
• Use Case Benefit: Monitoring helps maintain campaign effectiveness by allowing teams to quickly address performance issues, ensuring consistent engagement.

7. Campaign ROI and Budget Allocation Analysis

• Dataset Used: broadcasts, orders, revenue, contactActivities
• Description: Exporting campaign and revenue data into BigQuery allows e-commerce marketers to calculate ROI and assess campaign cost-effectiveness, providing guidance on optimal budget allocation.
• Use Case Benefit: By focusing on high-performing campaigns, marketers can optimize spending to maximize marketing effectiveness and returns.

8. Customer Journey Mapping Across Channels

• Dataset Used: contacts, contactActivities, events, orders
• Description: BigQuery’s data processing capabilities enable brands to consolidate user journeys across channels, providing insights into the customer lifecycle from discovery to conversion.
• Use Case Benefit: Understanding the customer journey reveals drop-off points and highlights opportunities for improvement, supporting a smoother experience and higher conversion rates.

Exporting data to BigQuery empowers e-commerce brands with deep insights into campaign performance, customer behavior, and revenue trends, enhancing the ability to make informed, data-driven decisions across marketing strategies.