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: [e-mail]
- 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: [e-mail]
- 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

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Need to ensure data quality? Learn about data quality and spam prevention to maintain effective campaigns.