Overview

Joint Analytics in SurveyAnalytica enables you to combine data from multiple surveys, datasets, contact lists, and campaigns into a single unified analytics view. Instead of analyzing each data source in isolation, Joint Analytics merges and cross-references data to provide holistic insights across your entire customer intelligence ecosystem.

What is Joint Analytics?

Joint Analytics is activated when an analytics entity has multiple data sources configured. This allows the platform to:

• Merge response data from multiple surveys into a single dashboard
• Cross-reference survey responses with imported datasets (CRM data, transaction records, etc.)
• Combine campaign engagement metrics with survey feedback
• Create unified customer profiles spanning multiple touchpoints

Setting Up Joint Analytics

Step 1: Create an Analytics Entity

Navigate to the Analytics section from the sidebar and create a new analytics entity. Give it a descriptive name that reflects the cross-entity analysis you want to perform.

Step 2: Add Data Sources

In the analytics configuration, add multiple data sources. Each data source can be:

• Survey: Any published survey in your workspace. The system pulls all response data including question answers, metadata, and timestamps.
• Dataset: Any uploaded dataset (CSV, Excel, etc.) that has been published for analytics.
• Contact List: Contact data with associated attributes like demographics, purchase history, or engagement scores.
• Campaign: Campaign message data including delivery status, open rates, click rates, and engagement timestamps.

Step 3: Define Relationships

When combining data from multiple sources, define how records should be linked across entities. Common join keys include:

• Email address or phone number (linking survey respondents to contacts)
• Contact ID (linking campaign messages to contact records)
• Custom identifiers (employee ID, order number, etc.)

Using Joint Analytics

Once configured, Joint Analytics provides the same full analytics experience as single-entity analytics, but with data merged across sources:

• Dashboard: Create charts and visualizations using fields from any of the connected data sources.
• Trends: Track metrics over time across all data sources.
• Text Analytics: Analyze free-text responses from multiple surveys simultaneously.
• Pivot Tables: Cross-tabulate fields from different data sources.
• Segmentation: Build segments using criteria from multiple entities.
• AI Insights: The AI assistant has access to the merged dataset and can provide cross-entity insights.

How It Works

When the platform detects that an analytics entity has more than one data source, it automatically switches to joint analytics mode. The analytics engine handles the data merging by:

1. Querying each data source from its respective storage
2. Applying relationship joins based on configured key fields
3. Merging the resulting datasets into a unified view
4. Making all fields from all sources available for charting, filtering, and segmentation

Best Practices

• Consistent identifiers: Ensure your surveys capture a unique identifier (email, phone, customer ID) that matches a field in your datasets or contact lists.
• Data quality: Clean and standardize join key fields across all sources before creating joint analytics. Mismatched formats (e.g., +1-555-1234 vs 5551234) will result in unlinked records.
• Performance: Joint analytics on very large datasets may take longer to load. Use date range filters to limit the time window and improve performance.
• Naming convention: When fields from different sources have the same name, the system prefixes them with the source entity name to avoid ambiguity.

Example Use Cases

• Customer satisfaction + purchase data: Combine NPS survey results with transaction data to correlate satisfaction scores with purchase behavior.
• Employee engagement + HR data: Merge employee survey responses with HR records to analyze engagement by department, tenure, or role.
• Campaign performance + survey feedback: Link campaign engagement (opens, clicks) with post-campaign survey responses to measure campaign effectiveness.
• Multi-wave studies: Combine responses from multiple survey waves to track changes in sentiment over time for the same respondent population.