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Analytics

分析

Answer any sales performance question using Apollo's analytics data. The user asks a question via "$ARGUMENTS".
使用Apollo的分析数据回答任何销售业绩相关问题。用户通过"$ARGUMENTS"提出问题。

Examples

示例

  • /apollo:analytics How many emails did I send last 30 days?
  • /apollo:analytics Show me team call connect rate this quarter by rep
  • /apollo:analytics What's our email reply rate week over week for this year?
  • /apollo:analytics Break down pipeline and won amount by opportunity stage all time
  • /apollo:analytics Which sequences have the highest reply rate in the last 6 months?
  • /apollo:analytics Show me activity summary — emails, calls, meetings, tasks — for each rep this quarter
  • /apollo:analytics How are calls trending by day of week over the last 3 months?
  • /apollo:analytics Show me emails sent vs replied broken down by contact stage and email type
  • /apollo:analytics 我过去30天发送了多少封邮件？
  • /apollo:analytics 展示本季度团队各销售代表的通话接通率
  • /apollo:analytics 今年我们的邮件回复率每周变化情况如何？
  • /apollo:analytics 按销售机会阶段拆分历史销售管线金额和成单金额
  • /apollo:analytics 过去6个月回复率最高的跟进序列是哪些？
  • /apollo:analytics 展示本季度每位销售代表的活动汇总——邮件、通话、会议、任务
  • /apollo:analytics 过去3个月按星期几划分的通话趋势如何？
  • /apollo:analytics 按联系人阶段和邮件类型拆分已发送邮件与已回复邮件的情况

Step 1 — Interpret the Question

步骤1 — 解读问题

Parse "$ARGUMENTS" to determine the following parameters:
解析"$ARGUMENTS"以确定以下参数：

Metrics

指标

Select 1–15 metrics that match what the user is asking about. Always include the rate/percent version alongside raw counts when the user asks about performance.
Email 
num_emails_sent
, 
num_emails_delivered
, 
num_emails_opened
, 
num_emails_clicked
, 
num_emails_replied
, 
num_emails_bounced
, 
num_emails_unsubscribed
, 
percent_emails_replied
, 
num_contacts_emailed
, 
num_contacts_opened
, 
num_contacts_replied
Calls 
num_phone_calls
, 
num_phone_calls_completed
, 
num_phone_calls_connect
, 
num_phone_calls_connect_positive
, 
num_phone_calls_connect_negative
, 
num_phone_calls_connect_neutral
, 
percent_phone_calls_connect
, 
avg_phone_call_duration
, 
num_contacts_called
Key distinctions:
  • num_phone_calls_completed
    = all logged attempts
  • num_phone_calls_connect
    = recipient actually answered
  • num_phone_calls_connect_positive/negative/neutral
    = connected calls by outcome sentiment
Meetings 
num_all_meetings_scheduled
, 
num_meetings_held
, 
num_all_meetings_rescheduled
, 
num_calendar_events_scheduled
, 
num_calendar_events_cancelled
, 
num_all_meetings_scheduled_via_email
, 
num_all_meetings_scheduled_via_call
Key distinctions:
  • num_all_meetings_scheduled
    = includes cancelled
  • num_meetings_held
    = actually occurred
Tasks 
num_tasks
, 
num_tasks_completed
, 
num_tasks_scheduled
, 
num_tasks_completed_on_time
, 
percent_tasks_completed
, 
percent_tasks_completed_on_time
, 
overdue_tasks
, 
unfinished_overdue_tasks
, 
percent_unfinished_overdue_tasks
Key distinctions:
  • overdue_tasks
    = all overdue including completed late
  • unfinished_overdue_tasks
    = still pending and overdue
  • percent_unfinished_overdue_tasks
    = share of scheduled tasks that are overdue and unfinished (vs 
    num_tasks_scheduled
    )
Contacts & Accounts 
num_contacts
, 
num_accounts
, 
num_contacts_touched
, 
num_accounts_touched
, 
num_net_new_people
, 
num_net_new_companies
, 
num_contacts_with_job_change
Opportunities 
num_opportunities
, 
num_won
, 
num_closed
, 
deal_amount
, 
won_amount
, 
pipeline_amount
, 
revenue_amount
, 
avg_deal_amount
, 
avg_won_amount
, 
percent_win_rate
, 
avg_salescycle_days
Sequences 
num_contacts_added_to_sequence
, 
num_contacts_remove_from_sequence
Conversation Intelligence 
num_conversations_recorded
, 
num_conversations_listened
, 
avg_conversation_duration
, 
total_conversation_duration
, 
avg_talk_ratio
, 
avg_question_rate
, 
avg_longest_monologue
, 
speaker_switches
LinkedIn 
num_linkedin_tasks_scheduled
, 
num_linkedin_tasks_completed
, 
num_linkedin_tasks_skipped
, 
percent_linkedin_tasks_completed
选择1–15个与用户问题匹配的指标。当用户询问业绩相关问题时，务必同时包含原始计数和对应的比率/百分比版本。
Email 
num_emails_sent
, 
num_emails_delivered
, 
num_emails_opened
, 
num_emails_clicked
, 
num_emails_replied
, 
num_emails_bounced
, 
num_emails_unsubscribed
, 
percent_emails_replied
, 
num_contacts_emailed
, 
num_contacts_opened
, 
num_contacts_replied
Calls 
num_phone_calls
, 
num_phone_calls_completed
, 
num_phone_calls_connect
, 
num_phone_calls_connect_positive
, 
num_phone_calls_connect_negative
, 
num_phone_calls_connect_neutral
, 
percent_phone_calls_connect
, 
avg_phone_call_duration
, 
num_contacts_called
关键区别：
  • num_phone_calls_completed
    = 所有已记录的尝试通话
  • num_phone_calls_connect
    = 收件人实际接听的通话
  • num_phone_calls_connect_positive/negative/neutral
    = 按结果情感分类的已接通通话
Meetings 
num_all_meetings_scheduled
, 
num_meetings_held
, 
num_all_meetings_rescheduled
, 
num_calendar_events_scheduled
, 
num_calendar_events_cancelled
, 
num_all_meetings_scheduled_via_email
, 
num_all_meetings_scheduled_via_call
关键区别：
  • num_all_meetings_scheduled
    = 包含已取消的会议
  • num_meetings_held
    = 实际举办的会议
Tasks 
num_tasks
, 
num_tasks_completed
, 
num_tasks_scheduled
, 
num_tasks_completed_on_time
, 
percent_tasks_completed
, 
percent_tasks_completed_on_time
, 
overdue_tasks
, 
unfinished_overdue_tasks
, 
percent_unfinished_overdue_tasks
关键区别：
  • overdue_tasks
    = 所有逾期任务，包括逾期完成的任务
  • unfinished_overdue_tasks
    = 仍未完成且已逾期的任务
  • percent_unfinished_overdue_tasks
    = 已逾期且未完成的任务占已计划任务的比例（对比
    num_tasks_scheduled
    ）
Contacts & Accounts 
num_contacts
, 
num_accounts
, 
num_contacts_touched
, 
num_accounts_touched
, 
num_net_new_people
, 
num_net_new_companies
, 
num_contacts_with_job_change
Opportunities 
num_opportunities
, 
num_won
, 
num_closed
, 
deal_amount
, 
won_amount
, 
pipeline_amount
, 
revenue_amount
, 
avg_deal_amount
, 
avg_won_amount
, 
percent_win_rate
, 
avg_salescycle_days
Sequences 
num_contacts_added_to_sequence
, 
num_contacts_remove_from_sequence
Conversation Intelligence 
num_conversations_recorded
, 
num_conversations_listened
, 
avg_conversation_duration
, 
total_conversation_duration
, 
avg_talk_ratio
, 
avg_question_rate
, 
avg_longest_monologue
, 
speaker_switches
LinkedIn 
num_linkedin_tasks_scheduled
, 
num_linkedin_tasks_completed
, 
num_linkedin_tasks_skipped
, 
percent_linkedin_tasks_completed

Date Range

日期范围

Map the user's time reference to a preset modality (preferred) or a custom range:
Presets: 
today
, 
yesterday
, 
current_week
, 
current_month
, 
current_quarter
, 
current_year
, 
last_7_days
, 
last_2_weeks
, 
last_30_days
, 
last_3_months
, 
last_6_months
, 
last_12_months
, 
last_4_quarters
, 
last_2_years
, 
previous_week
, 
previous_month
, 
previous_quarter
, 
previous_year
, 
all_time
Custom: use 
range_start
+ 
range_end
(YYYY-MM-DD) for specific date windows. Do not combine with a modality.
Default to 
last_30_days
if no time reference is given.
将用户提及的时间范围映射为预设模式（优先选择）或自定义范围：
预设模式：
today
, 
yesterday
, 
current_week
, 
current_month
, 
current_quarter
, 
current_year
, 
last_7_days
, 
last_2_weeks
, 
last_30_days
, 
last_3_months
, 
last_6_months
, 
last_12_months
, 
last_4_quarters
, 
last_2_years
, 
previous_week
, 
previous_month
, 
previous_quarter
, 
previous_year
, 
all_time
自定义范围：使用
range_start
+ 
range_end
（YYYY-MM-DD）指定具体日期区间。请勿与预设模式混用。
如果未指定时间范围，默认使用
last_30_days
。

Breakdown (group_by)

拆分维度（group_by）

Does the user want data broken down by something? Set 
group_by
to one of:
Time patterns (for trends and time series) 
smart_datetime_hour
, 
smart_datetime_day
, 
smart_datetime_week
, 
smart_datetime_month
, 
smart_datetime_year
smart_datetime_hour_of_day
, 
smart_datetime_day_of_week
, 
smart_datetime_month_of_year
People & Teams 
smart_user_id
(by rep), 
smart_subteam_id
(by team)
Email dimensions 
emailer_campaign_id
(by sequence), 
emailer_template_id
(by template), 
emailer_message_type
, 
emailer_step_id
, 
emailer_touch_id
, 
send_from_email
, 
send_from_domain
, 
email_account_id
Calls 
phone_call_outcome_id
, 
phone_call_purpose_id
, 
phone_call_sentiment
Contact attributes 
contact_stage_id
, 
contact_label_ids
, 
contact_owner_id
, 
persona
, 
person_title_unanalyzed
, 
person_seniority
, 
person_location_country
, 
person_location_state
, 
person_location_city
Account & company attributes 
account_id
, 
account_stage_id
, 
account_label_ids
, 
account_owner_id
, 
organization_industries
, 
organization_num_current_employees
, 
organization_hq_location_country
, 
organization_hq_location_state
, 
organization_hq_location_city
, 
organization_latest_funding_stage_cd
, 
organization_current_technologies
Opportunities 
opportunity_stage_id
, 
opportunity_owner_id
, 
opportunity_pipeline_id
, 
forecast_category
, 
lead_source
, 
opportunity_deal_source
Tasks 
task_type
, 
task_status
Conversations 
conversation_state
, 
conversation_type
, 
tracker_names_unanalyzed
, 
calendar_event_setting_type
Omit 
group_by
entirely for a flat summary (single row of totals).
用户是否希望按某个维度拆分数据？将
group_by
设置为以下选项之一：
时间模式（用于趋势和时间序列分析） 
smart_datetime_hour
, 
smart_datetime_day
, 
smart_datetime_week
, 
smart_datetime_month
, 
smart_datetime_year
smart_datetime_hour_of_day
, 
smart_datetime_day_of_week
, 
smart_datetime_month_of_year
人员与团队 
smart_user_id
（按销售代表）, 
smart_subteam_id
（按团队）
邮件维度 
emailer_campaign_id
（按跟进序列）, 
emailer_template_id
（按模板）, 
emailer_message_type
, 
emailer_step_id
, 
emailer_touch_id
, 
send_from_email
, 
send_from_domain
, 
email_account_id
通话 
phone_call_outcome_id
, 
phone_call_purpose_id
, 
phone_call_sentiment
联系人属性 
contact_stage_id
, 
contact_label_ids
, 
contact_owner_id
, 
persona
, 
person_title_unanalyzed
, 
person_seniority
, 
person_location_country
, 
person_location_state
, 
person_location_city
客户与公司属性 
account_id
, 
account_stage_id
, 
account_label_ids
, 
account_owner_id
, 
organization_industries
, 
organization_num_current_employees
, 
organization_hq_location_country
, 
organization_hq_location_state
, 
organization_hq_location_city
, 
organization_latest_funding_stage_cd
, 
organization_current_technologies
销售机会 
opportunity_stage_id
, 
opportunity_owner_id
, 
opportunity_pipeline_id
, 
forecast_category
, 
lead_source
, 
opportunity_deal_source
任务 
task_type
, 
task_status
对话 
conversation_state
, 
conversation_type
, 
tracker_names_unanalyzed
, 
calendar_event_setting_type
如果仅需扁平化汇总（单行总计），则完全省略
group_by
。

Pivot (pivot_group_by)

交叉维度（pivot_group_by）

If the user wants a cross-tab (e.g. "by rep AND by sequence", "broken down by stage vs email type"), set 
group_by
to the primary dimension and 
pivot_group_by
to the secondary. The tool returns one table per metric when a pivot is used. Prefer low-cardinality dimensions (e.g. 
emailer_message_type
, 
contact_stage_id
, 
phone_call_sentiment
) as the pivot.
如果用户需要交叉表（例如："按销售代表和跟进序列拆分"、"按阶段与邮件类型拆分"），将
group_by
设置为主维度，
pivot_group_by
设置为次维度。使用交叉维度时，工具会为每个指标返回一个单独的表格。优先选择低基数维度（例如
emailer_message_type
, 
contact_stage_id
, 
phone_call_sentiment
）作为交叉维度。

Filters

筛选条件

  • "my data" / "for me" / "my performance" → 
    filters: { user_ids: ["current"] }
  • Specific user by Apollo user ID → 
    filters: { user_ids: ["<user_id>"] }
    (can combine: 
    ["current", "user_id_1"]
    )
  • "team" / no user mention → omit filters entirely (returns team-wide data)
  • Filter by team/subteam → 
    filters: { team_ids: ["<subteam_id>"] }
  • Filter by sequence name → first call 
    mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_search
    to resolve the name to an ID, then pass 
    filters: { emailer_campaign_ids: ["<id>"] }
  • "我的数据" / "针对我" / "我的业绩" → 
    filters: { user_ids: ["current"] }
  • 通过Apollo用户ID指定特定用户 → 
    filters: { user_ids: ["<user_id>"] }
    （可组合：
    ["current", "user_id_1"]
    ）
  • "团队" / 未提及用户 → 完全省略筛选条件（返回团队整体数据）
  • 按团队/子团队筛选 → 
    filters: { team_ids: ["<subteam_id>"] }
  • 按跟进序列名称筛选 → 先调用
    mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_search
    将名称解析为ID，再传入
    filters: { emailer_campaign_ids: ["<id>"] }

Sort

排序

If the user asks "who has the most...", "ranked by...", or "top reps by...", set:
sort: { metric: "<metric_name>", asc: false }
Use 
asc: true
for "lowest first" or "worst performing" queries.
Two constraints:
  • Sort only applies when 
    group_by
    is set — it has no effect on flat queries
  • The sort metric must be included in the 
    metrics
    array
如果用户询问"谁的...最多"、"按...排名"或"顶级销售代表按..."，设置：
sort: { metric: "<metric_name>", asc: false }
对于"最少优先"或"业绩最差"的查询，使用
asc: true
。
两个限制条件：
  • 排序仅在设置
    group_by
    时生效——对扁平化查询无影响
  • 排序指标必须包含在
    metrics
    数组中

Step 2 — Call the Analytics Tool

步骤2 — 调用分析工具

Use 
mcp__claude_ai_Apollo_MCP__apollo_analytics_sync_report
with the parameters determined above.
If the question spans multiple independent dimensions (e.g. "show me email metrics by rep AND separately by sequence"), make two sequential calls.
If the question is ambiguous, make a reasonable default call first, then offer to refine.
使用上述确定的参数调用
mcp__claude_ai_Apollo_MCP__apollo_analytics_sync_report
。
如果问题涉及多个独立维度（例如："展示按销售代表拆分的邮件指标以及按跟进序列拆分的邮件指标"），请进行两次连续调用。
如果问题存在歧义，请先进行合理的默认调用，再提供优化建议。

Step 3 — Present the Results

步骤3 — 展示结果

Flat response (no group_by): Present as a clean two-column summary table — metric name and value.
Grouped response (group_by only): Present as a table with the dimension as the first column and metrics as subsequent columns. Highlight notable outliers (top performer, lowest rate, biggest gap).
Pivot response (group_by + pivot_group_by): Present each metric as a separate labeled table. Add a brief summary sentence per table.
Always:
  • Convert decimals to readable percentages (e.g. 
    0.14
    → 
    14%
    )
  • Format large numbers with commas
  • If the response says "Showing first N of M rows", mention the total count and offer to refine
  • Add 1–2 sentences of insight after the data (e.g. "Tuesday has the highest call volume at 355 calls", "Sarah Flores leads reply rate at 14%")
扁平化响应（无group_by）：以简洁的两列汇总表格展示——指标名称和数值。
分组响应（仅设置group_by）：以表格形式展示，第一列为维度，后续列为指标。突出显示显著异常值（顶级执行者、最低比率、最大差距）。
交叉维度响应（设置group_by + pivot_group_by）：为每个指标展示单独的带标签表格。每个表格添加一句简短的总结性语句。
始终遵循以下规则：
  • 将小数转换为易读的百分比（例如
    0.14
    → 
    14%
    ）
  • 大数字添加逗号格式化
  • 如果响应显示"显示前N条，共M条"，请提及总条数并提供优化建议
  • 在数据后添加1–2句洞察性语句（例如"周二的通话量最高，达355通"、"Sarah Flores的回复率最高，为14%"）

Step 4 — Offer Follow-up Actions

步骤4 — 提供后续操作建议

After presenting results, suggest 2–3 relevant next steps:
  1. Drill deeper — break down by another dimension (e.g. "want to see this by rep?")
  2. Change date range — compare with a different time period
  3. Add more metrics — "want to add meetings or tasks to this view?"
  4. Pivot view — "want to cross-tab this — e.g. by rep × sequence?"
  5. Export — format as CSV-style table for copy-paste
展示结果后，建议2–3个相关后续步骤：
  1. 深入分析 — 按另一个维度拆分数据（例如："想要按销售代表查看吗？"）
  2. 更改日期范围 — 与其他时间段进行对比
  3. 添加更多指标 — "想要在视图中添加会议或任务数据吗？"
  4. 切换交叉视图 — "想要生成交叉表吗？例如按销售代表×跟进序列"
  5. 导出数据 — 格式化为CSV风格表格以便复制粘贴 ",