Conversation Intelligence for Support & Sales

Conversation intelligence is most commonly associated with Sales (Gong for pipeline management) but generates equal or greater value for Support Ops and CS teams. Support-specific use cases: Agent coaching at scale: supervisors can review automatically flagged conversations — low-CSAT interactions (the platform links CSAT scores to conversation recordings), longest-handle-time calls (often involving agent uncertainty or knowledge gaps), and interactions flagged by the sentiment analysis engine for negative escalation moments. Instead of random QA sampling, supervisors focus their coaching time on the highest-impact conversations. Escalation pattern detection: searching all conversations for mentions of competitor names, churn language ("cancel," "switch," "evaluate alternatives"), and escalation triggers ("speak to your manager," "this is unacceptable") at scale — patterns that individual QA review would take weeks to identify are surfaced in minutes. Product feedback mining: searching all support conversations for mentions of specific features, bugs, or use case descriptions — enabling a Support Ops analyst to answer "how many conversations in the last 30 days mentioned problems with the reporting dashboard?" in seconds rather than days.

The traditional QA coaching model (supervisor randomly samples 3–5 calls per agent per week and provides feedback) has two structural weaknesses: sample size is too small to reliably identify patterns (an agent's worst behaviors may not appear in the sampled calls) and the feedback is delayed by days (the agent no longer remembers the specific interaction by the time they receive coaching). Conversation intelligence transforms coaching through: Comprehensive analysis: instead of sampling, every interaction is analyzed. The platform identifies patterns across 100% of conversations for each agent — revealing behaviors that only appear in certain situations (e.g., the agent handles simple questions well but uses passive language in escalation situations). Immediate flagging: interactions above the escalation or coaching threshold are immediately flagged in a supervisor dashboard — the coach can review and provide feedback within hours of the interaction, while both agent and coach have clear recollection. Self-review capability: agents can be given access to their own conversation library — high-performing agents use this for self-improvement without requiring supervisor time. Benchmark comparison: showing each agent how their specific metrics compare to the team average (average silence rate, average talk:listen ratio, average escalation rate) provides concrete, non-judgmental coaching context.

Conversation intelligence is an underutilized Product Ops resource — the verbatim voice of the customer at scale, searchable and analyzable. Product Ops workflows: Feature request detection: configure a "product request" keyword library in Gong or Chorus (including natural language variants like "wish you could," "it would be better if," "we need X to be able to") and receive a weekly digest of the conversations where these phrases appear, organized by topic cluster. This is a richer and faster VoC signal than surveying. Competitor intelligence: search all conversations for competitor names and analyze the context — "Competitor X has this feature" vs. "we looked at Competitor X but chose you because…" produces competitive intelligence from actual in-context customer statements. Persona validation: search for conversations where customers describe their role, team size, and workflow context — real-world persona data that supplements or challenges the personas the product team has documented. Documentation gap detection: high ticket volumes about specific topics are visible in the helpdesk, but conversation intelligence reveals the specific language customers use when describing these topics — enabling Knowledge Base authors to write articles that match the customers' vocabulary, improving searchability.