Call Coaching

作者 Zoominfod07402feb2b9无许可证收录于 2026年10月8日更新于 2026年10月8日

Coach a rep on how they handled a specific call — discovery quality, objection handling, talk dynamics, and next-call focus. Name the call if you know it, or let the skill pull recent calls and identify the most likely candidate to confirm. Uses conversation_intelligence to read how the conversation actually went. Use when someone asks "how did I do on the Acme call", "coach me on my last discovery call", "where did I lose them", or wants actionable feedback before the next conversation. Analyzes one call at a time.

AI 生成的概览

利用对话智能针对某一次销售通话为销售代表提供辅导,涵盖需求挖掘、异议处理、谈话动态和下次通话重点。

功能
该技能针对单次销售通话生成结构化的辅导反馈。它先通过解析客户或联系人并浏览近期会议来定位通话,然后分析对话中的需求挖掘质量、异议处理、客户情绪和谈话动态。输出内容包括做得好的地方、可改进之处及具体替代做法、谈话平衡的解读,以及一两个下次通话的重点。如果没有转录文本,它会如实说明,而不是凭空编造评估。
适用场景
适用于销售代表询问自己在某次通话中的表现、希望就最近一次需求挖掘通话获得辅导、想知道在哪里失去了客户,或希望在下次对话前获得可执行反馈的场景。它一次只分析一次通话。
运行要求
需要有效的日历或会议集成以浏览互动记录,至少一个已连接的会议来源以进行对话智能分析,以及用于分析的 AI 额度。该技能不包含脚本,仅为指令。

Call Coaching

Give a rep specific, actionable feedback on a single call: what worked, what to improve, and what to focus on next time.

Prerequisites

browse_engagements (to find the call) is free but requires an active calendar/meeting integration; conversation_intelligence (to analyze it) requires at least one connected meeting source and consumes AI credits. If the call has no transcript to analyze, say so rather than coaching from assumption.

Input

Provided via $ARGUMENTS:

  • Which call (optional) — an account and/or date. If omitted, the skill identifies the most likely recent call and confirms before analyzing.
  • Focus (optional) — e.g. "discovery", "the pricing objection", "did I talk too much". Targets the coaching.

Workflow

  1. Identify the call. browse_engagements filters by date and by company/contact ID, not by call name, so resolve any named account or contact first via search_companies / search_contacts, then call browse_engagements (engagementType: MEETINGS, sort: -chronological) scoped to that ID and date window and pick the matching meeting from the results. If nothing was named, call browse_engagements for the user's recent meetings, propose the most likely candidate, and confirm it before spending credits. If more than one meeting plausibly matches, ask. Keep the chosen engagement ID.
  2. Analyze how it went. Run conversation_intelligence scoped to that engagement ID. Ask how the rep handled discovery (did they uncover pain, budget, timeline, decision process), how objections were handled, what the customer's reactions and sentiment were, and where the conversation stalled or advanced. Keep the query scoped to this one call.
  3. Coach against good practice. Assess the call against solid discovery and objection-handling fundamentals — open questions over pitching, listening over talking, surfacing next steps, addressing concerns directly. Ground every point in a specific moment from the call; cite what was said. Be candid and useful, not generic. If the transcript is too thin to judge something, say so rather than inventing a critique.

Output Format

Call coaching — [Call title, date]

What went well (2-3) — specific strengths, each tied to a moment in the call.

What to improve (2-3) — the highest-leverage changes, each with the moment that shows it and a concrete alternative ("when they raised price, you discounted; instead anchor on the value point from earlier").

Talk dynamics — a quick read on balance (who drove, listening vs pitching, questions asked) if the data supports it.

Focus for the next call — one or two things to do differently next time, tied to where this deal now stands.

When there is no data

If the call cannot be found or has no transcript, tell the user and offer to coach from notes they provide, rather than fabricating an assessment.

来源与署名

来源:Zoominfo/zoominfo-mcp-plugin位于skills/call-coaching提交d07402f

许可证: 无许可证

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