Usage & Billing

Monitor your credit consumption and enrichment success metrics across all platform activities.

Accessing Usage

Navigate to Usage from the bottom section of the left navigation.

Interface

Date Range Picker: Top-right on both tabs — Select the time period to analyze

Tabs:

• Credits — Consumption and transaction history
• Enrichment — Success metrics by enrichment type

Credits Tab

Track all credit consumption across the platform.

Top Summary

Total Credits Usage: Sum of all credits consumed in selected date range

Example: “12,450 credits” for last 30 days

Category Buckets:

Usage breakdown with transaction counts:

Category	Description	Typical Use
Data Firewall	Hygiene and validation pipelines	Data cleaning, quality assessment
Enrichment	Contact and company enrichment	LinkedIn profiles, email discovery
Sequence	Outreach generation	Email sequence generation
Visitor ID	Anonymous visitor identification	Website visitor tracking
AI Assistant	RevOps Assistant usage	Chat conversations, configurations

Each bucket shows:

• Total credits consumed
• Number of transactions
• Percentage of total usage

Example:

Data Firewall: 5,200 credits (42%)
└─ 5,200 transactions

Enrichment: 4,800 credits (39%)
└─ 2,400 transactions

Sequence: 2,100 credits (17%)
└─ 300 transactions

Visitor ID: 250 credits (2%)
└─ 125 transactions

AI Assistant: 100 credits (1%)
└─ 50 transactions

Transactions Table

Detailed transaction history:

Columns:

Column	Description
Occurred At	Timestamp of transaction
Category	Feature group (Data Firewall, Enrichment, etc.)
Type	Debit (consumed) or Credit (refunded)
Amount	Credits consumed or credited
Actions	Details button for metadata

Table Features:

• Sortable columns
• Searchable (filter by category, type)
• Paginated results

Details Button:

Click to see per-entry metadata:

• Pipeline name
• Record ID (contact/company)
• Processing status
• Timestamp
• User who triggered (if manual)
• Additional context

Pagination:

• Bottom-right navigation
• Page size selector bottom-left
• Options: 25, 50, 100, 200 per page

Common Operations

Filter by Date:

Isolate campaign windows or specific time periods:

1. Click date range picker
2. Select start and end dates
3. Or choose preset: Last 7 days, Last 30 days, Last 90 days
4. View updated totals

Sort by Category:

Identify which features consume most credits:

1. Click “Category” column header
2. View grouped transactions
3. Compare category totals

Sort by Amount:

Find heavy usage patterns:

1. Click “Amount” column header
2. Largest transactions appear first
3. Investigate high-cost operations

Export Data:

For accounting or analysis:

1. Select date range
2. Click export button (if available)
3. Download CSV with all transactions

Enrichment Tab

Roll-up success metrics by enrichment type for selected date range.

Metric Cards

Each enrichment type shows three key metrics:

Attempts: Total enrichment runs attempted

Success Rate: Successful / Attempts percentage

Successful Enrichments: Absolute count of successes

Typical Cards

Company LinkedIn Enrichment:

Attempts: 1,250
Success Rate: 82%
Successful: 1,025

Contact LinkedIn Enrichment:

Attempts: 2,100
Success Rate: 68%
Successful: 1,428

Company Revenue Enrichment:

Attempts: 1,250
Success Rate: 75%
Successful: 937

Email Discovery:

Attempts: 500
Success Rate: 45%
Successful: 225

Interpretation

Healthy Metrics:

Enrichment Type	Good Success Rate
Company LinkedIn	70-90%
Contact LinkedIn	60-80%
Email Discovery	40-60%
Revenue Data	65-85%

High Attempts, Low Success:

Usually indicates:

• Source misconfiguration
• Under-specified inputs (missing required fields)
• Invalid/test data in source
• Wrong data associations

Example:

Contact LinkedIn Enrichment
Attempts: 5,000
Success Rate: 15%
Successful: 750

Problem: Too many failures

Solutions:

1. Check contacts have first name, last name
2. Verify company associations exist
3. Run quality assessment pipelines from Marketplace first
4. Review data source mappings

Low Success Rate:

May indicate:

• Integration issues
• Provider API problems
• Data quality problems
• Incorrect field mappings

Success Rate Trends:

Compare across types to identify best performers:

• High success = good data quality
• Low success = investigate immediately
• Declining trends = deteriorating data or config issues

Usage Tips

1. Set Date Ranges Strategically

Week-over-Week:

This week vs last week

Track weekly usage patterns

Month-over-Month:

This month vs last month

Monitor monthly trends

Campaign Windows:

Campaign launch date to end date

Measure campaign-specific costs

2. Monitor Category Distribution

Ensure spending aligns with priorities:

Example Analysis:

Data Firewall: 60% (priority: high)
Enrichment: 35% (priority: medium)
AI Assistant: 5% (priority: low)

Spending matches priorities ✅

Misaligned Example:

Visitor ID: 70%
Data Firewall: 20%
Enrichment: 10%

Action: Reduce Visitor ID usage, increase core operations

3. Check Enrichment Success Rates

Weekly: Review Enrichment tab for issues

Red flags:

• Success rate < 30% (investigate immediately)
• Sudden drops in success (API issues?)
• High attempts with flat success (config problem)

Green flags:

• Success rate > 60%
• Consistent performance
• Gradual improvements

4. Review Transaction Details

For High-Cost Items:

1. Sort by Amount (descending)
2. Click Details on largest transactions
3. Verify legitimate usage
4. Identify optimization opportunities

For Unexpected Charges:

1. Filter by date range
2. Review transaction details
3. Check pipeline configurations
4. Contact support if needed

5. Track AI Assistant Usage

Monitor:

• Conversation count
• Credit consumption
• Feature usage patterns

Beta considerations:

• Usage patterns may change
• Pricing may be adjusted
• Provide feedback on costs

Cost Optimization

Identify Expensive Operations

High-Cost Operations:

• Agent pipelines for complex analysis
• Standard enrichment services
• Research-intensive operations

Optimization:

1. Run Classifier pipelines before expensive operations
2. Filter for high-quality records only
3. Save significantly on invalid/low-quality data

Set Processing Limits

In pipeline Settings:

• Daily limits prevent runaway costs
• Rate limits control speed
• Volume caps enforce budgets

Example:

Agent pipeline:
- Max 100 records/day
- Cost: 100 records × cost per record
- Monthly budget calculated from daily limit

Target High-Value Segments

Instead of enriching all records:

Before:

All contacts: 50,000
Cost: 50,000 × $2 = $100,000

After (targeted):

Active opportunities: 2,500
Cost: 2,500 × $2 = $5,000
Savings: $95,000

Use Low-Cost Pipelines First

Workflow:

1. Code pipelines (Free/low-cost)
2. Classifier pipelines (Low-moderate cost)
3. Filter for quality
4. Agent pipelines on valid records only
5. Enrichment services on cleaned data

Result: Only pay for quality data

Troubleshooting

Unexpected High Usage

Issue: Credits consumed faster than expected

Check:

1. Processing limits — Are they set too high?
2. Auto-enroll — Is pipeline automatically enrolling too many records?
3. List sizes — Are source lists larger than expected?
4. Review policy — Is auto-confirm enrolling invalid records?

Solutions:

• Reduce daily limits
• Use smaller, targeted lists
• Enable human review
• Filter with cheaper pipelines first

Zero Usage Showing

Issue: No usage appears in dashboard

Possible causes:

• No pipelines running
• Date range selected has no activity
• Pipelines in Draft status (not Active)

Solutions:

1. Check date range
2. Verify pipelines are Active
3. Check pipeline Overview tabs for processing
4. Ensure lists have records

Success Rates Declining

Issue: Enrichment success rates dropping over time

Investigate:

1. Data quality deteriorating?
2. Provider API changes?
3. Field mappings broken?
4. Integration issues?

Solutions:

• Review data quality
• Run hygiene pipelines
• Check field mappings
• Test with known-good records

Next Steps

• Configure Processing Limits — Control costs
• Data Deduplication — Optimize data quality
• Enrichment Best Practices — Reduce enrichment costs