> For the complete documentation index, see [llms.txt](https://help.blings.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.blings.io/app/personalization-data-management/variables-data-enrichment.md).

# Variables & Data Enrichment

Combine multiple data sources to create richer, more personalized video experiences. Use variables and data enrichment to build comprehensive viewer profiles and deliver highly targeted content.

## What Is Data Enrichment?

Data enrichment enhances your existing contact data by adding additional information from multiple sources. This creates more complete viewer profiles for better personalization.

## Data Sources for Enrichment

### Internal Data

Information from your own systems:

**CRM Data:**

* Contact information (name, email, company)
* Purchase history and transaction data
* Interaction history and engagement metrics
* Account status and subscription details

**Website Analytics:**

* Pages visited and time spent
* Search queries and interests
* Device and browser information
* Geographic location and time zone

**Email Marketing:**

* Open rates and click-through rates
* Email preferences and subscription status
* Campaign engagement history
* A/B test performance data

### External Data

Information from third-party sources:

**Company Intelligence:**

* Company size and revenue
* Industry and sector classification
* Technology stack and tools used
* Recent news and company events

**Demographic Data:**

* Age, gender, and location
* Education and professional background
* Income level and purchasing power
* Lifestyle and interest categories

**Behavioral Data:**

* Online browsing patterns
* Social media activity
* Professional networks and connections
* Content consumption preferences

## Combining Data Sources

### Multi-Source Variables

Create variables that combine data from multiple sources:

**Example:**

```
user_profile = {
  basic_info: {first_name, last_name, email, company},
  crm_data: {purchase_history, account_status, interaction_count},
  website_data: {pages_visited, time_on_site, interests},
  enriched_data: {company_size, industry, technology_stack}
}
```

### Calculated Variables

Derive new insights from combined data:

**Examples:**

* `engagement_score` = (email\_opens \* 0.3) + (website\_visits \* 0.4) + (purchase\_frequency \* 0.3)
* `lifetime_value` = total\_purchases + (predicted\_future\_value \* 0.7)
* `churn_risk` = calculate\_risk\_score(account\_age, usage\_pattern, support\_tickets)
* `upsell_potential` = analyze\_purchase\_patterns\_and\_gaps

## Real-World Enrichment Examples

### B2B Customer Profile

```
Base Data (CRM):
- Name: John Smith
- Company: TechStart Inc
- Email: john@techstart.com
- Role: CTO

Enriched Data:
- Company Size: 50-100 employees
- Industry: SaaS/Technology
- Revenue: $5M-$10M
- Technology Stack: AWS, React, Node.js
- Recent Funding: Series A ($2M)
- Location: San Francisco, CA

Combined Insights:
- High-growth startup in competitive market
- Technical decision maker with budget authority
- Likely interested in scaling solutions
- Geographic proximity to sales team
```

### E-commerce Customer Profile

```
Base Data (Website):
- Email: sarah@email.com
- Recent Purchase: $150
- Pages Visited: Product pages, reviews

Enriched Data:
- Age: 28-35
- Location: New York, NY
- Income Level: $75K-$100K
- Interests: Fashion, Technology, Travel
- Social Media: Active on Instagram, Pinterest

Combined Insights:
- Fashion-conscious millennial with disposable income
- Values social proof and reviews
- Likely to respond to visual content
- Potential for premium product recommendations
```

## Advanced Enrichment Techniques

### Predictive Scoring

Use enriched data to predict future behavior:

**Lead Scoring:**

```
score = 0
IF company_size = "Enterprise" THEN score += 20
IF industry = "Technology" THEN score += 15
IF job_title = "Decision Maker" THEN score += 25
IF website_engagement = "High" THEN score += 20
IF email_engagement = "High" THEN score += 20

IF score >= 80 THEN "Hot Lead"
IF score >= 60 THEN "Warm Lead"
ELSE "Cold Lead"
```

### Segmentation

Create dynamic segments based on enriched data:

**Examples:**

* High-Value Enterprise Customers
* At-Risk Subscribers
* Upsell Opportunities
* New Market Prospects
* Product Evangelists

### Personalization Rules

Use enriched data to customize content:

**Examples:**

```
IF company_size = "Enterprise" AND industry = "Healthcare" 
THEN show HIPAA-compliant features

IF engagement_score > 80 AND purchase_history = "High"
THEN show VIP exclusive content

IF churn_risk > 70 
THEN show retention-focused messaging
```

## Implementation Strategies

### Data Integration

Connect multiple data sources:

**API Integrations:**

* CRM systems (Salesforce, HubSpot, Pipedrive)
* Marketing automation (Marketo, Pardot, ActiveCampaign)
* Analytics platforms (Google Analytics, Mixpanel)
* Enrichment services (Clearbit, ZoomInfo, FullContact)

### Data Processing

Clean and standardize enriched data:

**Steps:**

1. **Data validation** — Check for accuracy and completeness
2. **Format standardization** — Ensure consistent data formats
3. **Deduplication** — Remove duplicate or conflicting information
4. **Quality scoring** — Rate data reliability and freshness

### Privacy and Compliance

Handle enriched data responsibly:

**Considerations:**

* **Data consent** — Ensure proper permissions for data enrichment
* **GDPR compliance** — Respect data subject rights
* **Data retention** — Set appropriate storage and deletion policies
* **Security measures** — Protect sensitive enriched information

## Best Practices

### Data Quality

* **Validate sources** — Use reliable, up-to-date enrichment services
* **Regular updates** — Keep enriched data current and relevant
* **Accuracy checks** — Verify enriched data against known information
* **Fallback strategies** — Have defaults for missing or unreliable data

### Performance

* **Caching** — Store enriched data to reduce API calls
* **Batch processing** — Enrich data in bulk when possible
* **Incremental updates** — Only refresh changed or new data
* **Rate limiting** — Respect API limits and quotas

### User Experience

* **Relevant personalization** — Use enrichment to add value, not creepiness
* **Transparency** — Be clear about how data is used
* **Opt-out options** — Allow users to control data enrichment
* **Testing** — A/B test enriched personalization for effectiveness

## Common Use Cases

* **Account-based marketing**
* **Lead scoring and qualification**
* **Customer lifecycle management**
* **Churn prediction and prevention**
* **Upsell and cross-sell opportunities**
* **Market expansion and targeting**
* **Product development insights**

***

**Need to ensure data quality?** Learn about [data quality and spam prevention](/app/personalization-data-management/data-quality-spam-prevention.md) to maintain effective campaigns.