TLDR
Data Enrichment automatically transforms your feedback data with AI-powered sentiment tracking, metric calculations, and data standardization—eliminating spreadsheets and manual cleanup so you can move from raw feedback to deeper insights in minutes.
What is Data Enrichment?
Data Enrichment transforms your feedback data automatically, enabling you to extract deeper insights without spreadsheets, custom scripts, or engineering resources. With AI-powered functions, automated calculations, and automated normalization, you can turn raw feedback into decision-ready signals in minutes.
What are types of data enrichment are supported?
Data Enrichment includes 3 main capability areas:
AI-Powered Enrichment - Prompt-based functions like sentiment tracking across conversation lifecycles
Data Calculations - Automated metric calculations and text-to-numeric conversions
Data Normalization - Automated standardization and cleanup of inconsistent data formats
Getting Started
Accessing Data Enrichment
Navigate to Profile → Manage Data in your Enterpret platform
Select the Enrichment tab
Click the Create New Enrichment button
How Enrichment Works
Enrichment automates data transformations by connecting source events to built-in functions:
Objects: Data units (e.g., feedback records, accounts).
Triggers: Events such as creation or update that initiate enrichment.
Functions: Pre-built processors (e.g., Sentiment Shift, Country Normalizer).
Actions: Where to store the enriched output fields.
Each function follows an input → process → output pattern.
Setting Up Your First Enrichment
Objects:
Select Feedback Record and click NEXT
Trigger:
Select Created or Updated as the trigger type.
Select data sources to filter on
Function:
Select a function from the drop down menu
Select the Input Column
Action:
Define column names for the output fields
Click NEXT
Details:
Enter enrichment name and description
Click the check box to run backfill for this enrichment
Select date range to backfill enrichment data
Click CREATE
Global Enrichments
These enrichments are available for all Enterpret customers in the platform.
AI-Powered Enrichments
AI-powered enrichments use functions powered by large language models with customizable prompts to automatically analyze and extract insights from your feedback content that would be impossible to detect manually at scale.
Sentiment Shift
Purpose: Detect tone changes throughout ticket lifecycles to identify which agents, channels, or topics improve or worsen customer sentiment.
Use Cases:
Surface which support agents consistently improve customer sentiment
Identify topics that tend to escalate or de-escalate situations
Track sentiment recovery patterns across different channels
Configuration:
Available globally for all customers
Automatically analyzes multi-turn conversations
Results appear as a new field in your data explorer
Data Calculations
Data calculation functions automatically derive key metrics and convert data formats within Enterpret, eliminating the need for external spreadsheets or custom scripts.
Total Response Time
Purpose: Automatically calculate exact turnaround time on closed tickets to identify workflow bottlenecks.
Benefits:
No more manual time calculations in spreadsheets
Instant filtering by resolution time thresholds
Correlation with sentiment data for comprehensive analysis
Setup:
Select tickets/conversations data source
Define start and end events (e.g., Updated At, Created At for Zendesk ticket )
Data Normalization
Data normalization functions automatically clean and standardize inconsistent data formats across your sources, ensuring reliable analysis and reporting.
Country Code Standardization
Purpose: Clean up inconsistent country representations across data sources.
Examples:
"US," "USA," "United States" → "US"
"UK," "United Kingdom," "Great Britain" → "GB"
Setup:
Select a source.
Choose the Country to ISO2 function.
Select the field containing country data and map it as the function input.
Specify a name for the new output field.
Timestamp Conversion
Purpose: Convert epoch timestamps and inconsistent date formats into human-readable formats.
Examples:
1625097600000 → "2025-05-30 11:27"
Various date formats → Standardized ISO format
Custom Enrichments
Custom enrichments are tailor-made enrichment functions that your Customer Success Manager can create specifically for your unique data and use cases. These address specialized enrichment needs that go beyond our standard library, such as:
Industry-Specific Classifications: Custom categorization for your domain or business model
Advanced Pattern Recognition: Complex regex-based extractions for unique data formats
Specialized Calculations: Custom formulas for business-specific metrics
Workflow-Specific Enrichments: Functions designed around your team's unique processes
Common Custom Enrichment Examples
Qualitative Rating Conversion Convert your organization's specific text ratings into numeric scores for trending and analysis.
Example: "Excellent" → 5, "Good" → 3, "Needs Improvement" → 1
Why Custom: Each organization uses different rating scales and terminology
NPS Archetype Classification Automatically categorize NPS scores based on your company's specific thresholds.
Example: 9-10 → "Promoter", 7-8 → "Passive", 0-6 → "Detractor"
Why Custom: Organizations may define NPS ranges differently based on industry or methodology
URL Parameter Extraction Extract specific campaign data, user IDs, or tracking parameters from your website URLs.
Example: From "shop.com/products?source=facebook&user=12345" extract Source: facebook, User ID: 12345
Why Custom: URL structures and important parameters vary significantly by organization
Competitive Intelligence Tracking Identify mentions of your specific competitors in customer conversations.
Example: Detect mentions of "Competitor X" or "CompetitorY.com" with count and context
Why Custom: Each company has different competitors with various name variations
How to Request Custom Enrichments
Identify Your Need: Define the specific data transformation or insight you want to achieve
Contact Your CSM: Discuss your use case and provide sample data