Revenue Intelligence That Connects Calls to Pipeline
Bridge the gap between conversations and conversions
Unify conversation intelligence with your revenue stack to forecast accurately, identify leak points, and optimize the entire sales engine with data.
Challenges You Face
Call Data Lives in Silos Outside CRM
Your sales calls are recorded but not integrated into your revenue stack. You can't correlate conversation quality, sentiment, or objection patterns with pipeline velocity, deal size, or win rates—leaving massive blind spots in your revenue analytics.
Forecasting Based on Incomplete Data
Your forecast relies on CRM stage updates and gut feelings from reps. You're missing conversational signals like prospect enthusiasm, objection severity, or competitive threats that could dramatically improve forecast accuracy and prevent surprises.
No Single Source of Truth for Revenue Metrics
Sales activity data is fragmented across call recordings, CRM notes, email threads, and Slack. You spend hours manually pulling reports instead of having one unified system that connects conversation intelligence with revenue outcomes.
Inability to Identify Revenue Leak Points
Deals slip through cracks, but you don't know where or why. Without systematic call analysis, you can't pinpoint whether revenue loss happens during discovery, objection handling, pricing discussions, or another specific stage in the buyer journey.
How Callbricks Solves This
SQL-Ready Call Data Exports
Export structured call data—transcripts, objections, sentiment scores, keywords—directly into your data warehouse. Join conversation intelligence with CRM, product usage, and support data to build comprehensive revenue dashboards.
Objection & Competitive Intelligence Dashboard
Track objection frequency, competitive mentions, and pricing pushback across all deals in your pipeline. Identify systemic issues affecting multiple deals and proactively address them before they kill forecast.
Pipeline Health Scoring with Call Sentiment
Enrich your pipeline reports with sentiment analysis from actual conversations. Flag deals where prospect sentiment is declining, talk ratios are unhealthy, or key decision-makers are disengaged—before the deal goes dark.
Historical Pattern Analysis for Forecasting
Analyze historical call patterns from closed-won and closed-lost deals. Use machine learning to identify conversational predictors of deal success, improving forecast accuracy by incorporating actual buyer engagement signals.
Real Use Cases
- ●Export call sentiment and objection data to your data warehouse to build revenue leak attribution models
- ●Create automated alerts when multiple deals in pipeline encounter the same objection cluster
- ●Correlate average calls per closed deal with deal size to optimize rep activity recommendations
- ●Build dashboards showing talk-time ratio by rep and its impact on conversion rates
- ●Track competitive win/loss rates by analyzing competitor mentions in call transcripts over time
Frequently Asked Questions
Start Mining Your Sales Calls Today
Join agencies and revenue operations who use Callbricks to extract actionable intelligence from every conversation.