analytics

name	analytics
description	Instant sales analytics. Ask any performance question — emails, calls, meetings, tasks, opportunities, sequences, conversation intelligence — and get formatted tables with real Apollo data.
user-invocable	true
argument-hint	["your analytics question"]

Analytics

Answer any sales performance question using Apollo's analytics data. The user asks a question via "$ARGUMENTS".

Examples

• /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

Step 1 — Interpret the Question

Parse "$ARGUMENTS" to determine the following parameters:

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Metrics

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

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Date Range

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.

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Breakdown (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).

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Pivot (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.

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Filters

• "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>"] }

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Sort

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

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Step 2 — Call the Analytics Tool

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.

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Step 3 — Present the Results

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%")

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Step 4 — Offer Follow-up Actions

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