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:

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

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.

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).

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.

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>"] }

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

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.

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%")

Step 4 — Offer Follow-up Actions

After presenting results, suggest 2–3 relevant next steps:

Drill deeper — break down by another dimension (e.g. "want to see this by rep?")
Change date range — compare with a different time period
Add more metrics — "want to add meetings or tasks to this view?"
Pivot view — "want to cross-tab this — e.g. by rep × sequence?"
Export — format as CSV-style table for copy-paste

analytics

NPX Install

Tags

SKILL.md Content

Analytics

Examples

Step 1 — Interpret the Question

Metrics

Date Range

Breakdown (group_by)

Pivot (pivot_group_by)

Filters

Sort

Step 2 — Call the Analytics Tool

Step 3 — Present the Results

Step 4 — Offer Follow-up Actions