Manja.ai

Analyzes uploaded call recordings and transcripts to extract performance metrics, objection patterns, and deal progression signals specific to each rep's actual conversations. Uses speech-to-text transcription combined with NLP-based intent detection to identify talking points, objection handling, and close attempts, then correlates these patterns with deal outcomes to surface personalized coaching areas rather than generic sales advice.

Automatically identifies and categorizes objections from call transcripts using NLP classification, then clusters similar objections across multiple calls to reveal which objection types appear most frequently and which ones correlate with deal loss. Builds a rep-specific objection taxonomy that evolves as more calls are analyzed, enabling targeted practice on high-impact objection types.

Segments call analysis by deal stage (discovery, qualification, proposal, negotiation, close) and generates stage-specific coaching insights tied to rep behavior patterns at each stage. Uses temporal analysis of call transcripts to identify which stage each call belongs to, then compares rep's approach (questions asked, value propositions mentioned, objection handling) against successful patterns from their own win history.

Extracts speaker diarization from call recordings to measure rep talk time vs prospect talk time, then calculates conversation balance metrics (prospect-to-rep talk time ratio, rep interruption frequency, prospect question count). Compares these metrics against rep's own win/loss history and industry benchmarks to surface whether rep is over-talking, under-listening, or interrupting too frequently.

Synthesizes insights from conversation analysis, objection patterns, and deal-stage behavior into prioritized coaching action plans that recommend specific skills to practice (e.g., 'improve discovery questioning in first calls' or 'handle price objections with value-based reframing'). Generates rep-specific practice scenarios and suggested talking points based on actual objections and deal patterns from their call history.

Accepts call recordings in multiple audio formats (MP3, WAV, M4A) via web upload or API, automatically transcribes them using speech-to-text (likely cloud-based ASR like AWS Transcribe or Google Cloud Speech-to-Text), and stores transcripts with metadata (call date, duration, rep, prospect) for downstream analysis. Handles variable audio quality and call lengths (typically 15-60 minutes for sales calls).

Offers free tier with limited monthly call analysis (typically 5-10 calls/month) to enable individual reps to test value before team/enterprise commitment. Upsells to paid tiers based on call volume, team size, or advanced features (CRM integration, custom coaching frameworks, team dashboards). Freemium model reduces adoption friction by allowing reps to experiment without manager approval or budget allocation.

Integrates with Salesforce, HubSpot, or other CRMs to automatically link analyzed calls to deals, pull deal stage and outcome data (won/lost), and correlate rep conversation patterns with deal results. Enables analysis like 'your discovery questions correlate with 15% higher close rates' by matching call metadata (rep, prospect, date) with CRM deal records.

Aggregates anonymized conversation metrics and coaching insights across multiple reps on a team to enable peer benchmarking (e.g., 'top performers ask 40% more discovery questions than average reps'). Surfaces best practices from high-performing reps (talk time ratio, objection handling patterns, discovery question types) that lower-performing reps can learn from, without exposing individual rep names or sensitive deal data.