ahmedtijaninet/call-center-metrics

The code you have provided appears to be calculating a variety of metrics related to customer service agents, such as attendance rate, schedule adherence, and skillset proficiency. These metrics can be useful for understanding the performance of individual agents or for comparing the performance of different agents or teams. Some of the metrics you are calculating, such as average handle time and average wait time, can also be useful for understanding the customer experience and for identifying areas for improvement.

data.csv content :

ID agent	abandonment rate	agent absenteeism rate	agent attendance rate	attendance by time	availability rate	certification rate	productivity by time	satisfaction score	schedule adherence	schedule adherence by time	schedule efficiency	schedule efficiency by time	schedule flexibility	schedule flexibility by time	skillset diversity	skillset effectiveness	skillset proficiency	skillset utilization	training completion rate	turnover rate	turnover rate by reason	turnover rate by time	utilization rate	average acw time by reason	average acw time by time	average wait time	average wait time by time	average handle time	average handle time by reason	average handle time by time	average hold time	average hold time by reason	average hold time by time	average queue time by reason	average queue time by time	average speed to answer	average speed to answer by time	average talk time	average talk time by reason	average talk time by time	average time in acw	average time in acw by reason	average time in acw by time	average time in queue	average time in queue before abandoning	average time in queue before abandoning by reason	average time in queue before abandoning by

Import necessary modules

import pandas as pd
import numpy as np

Read in data

python: data = pd.read_csv("data.csv")

Calculate abandonment rate

python: abandonment_rate = data[data["status"] == "abandoned"].count() / data["status"].count()

Calculate agent absenteeism rate

python: agent_absenteeism_rate = data[data["absent"] == 1].count() / data["absent"].count()

Calculate agent attendance rate

python: agent_attendance_rate = data[data["attended"] == 1].count() / data["attended"].count()

Calculate agent attendance rate by time of day

python: attendance_by_time = data.groupby("time_of_day")["attended"].mean()

Calculate agent availability rate

python: availability_rate = data[data["available"] == 1].count() / data["available"].count()

Calculate agent certification rate

python: certification_rate = data[data["certified"] == 1].count() / data["certified"].count()

Calculate agent productivity by time of day

python: productivity_by_time = data.groupby("time_of_day")["productivity"].mean()

Calculate agent satisfaction score

python: satisfaction_score = data["satisfaction"].mean()

Calculate agent schedule adherence

python: schedule_adherence = data[data["adhered_to_schedule"] == 1].count() / data["adhered_to_schedule"].count()

Calculate agent schedule adherence by time of day

python: schedule_adherence_by_time = data.groupby("time_of_day")["adhered_to_schedule"].mean()

Calculate agent schedule efficiency

python: schedule_efficiency = data["scheduled_time"].sum() / data["actual_time"].sum()

Calculate agent schedule efficiency by time of day

python: schedule_efficiency_by_time = data.groupby("time_of_day")["scheduled_time"].sum() / data.groupby("time_of_day")["actual_time"].sum()

Calculate agent schedule flexibility

python: schedule_flexibility = data[data["schedule_changes"] > 0].count() / data["schedule_changes"].count()

Calculate agent schedule flexibility by time of day

python: schedule_flexibility_by_time = data.groupby("time_of_day")["schedule_changes"].mean()

Calculate agent skillset diversity

python: skillset_diversity = data["skillset"].nunique() / data["skillset"].count()

Calculate agent skillset effectiveness

python: skillset_effectiveness = data[data["effective_use_of_skillset"] == 1].count() / data["effective_use_of_skillset"].count()

Calculate agent skillset proficiency

python: skillset_proficiency = data["skillset_proficiency"].mean()

Calculate agent skillset utilization

python: skillset_utilization = data[data["utilized_skillset"] == 1].count() / data["utilized_skillset"].count()

Calculate agent training completion rate

python: training_completion_rate = data[data["training_completed"] == 1].count() / data["training_completed"].count()

Calculate agent turnover rate

python: turnover_rate = data[data["left_company"] == 1].count() / data["left_company"].count()

Calculate agent turnover rate by reason

python: turnover_rate_by_reason = data.groupby("reason_for_leaving")["left_company"].mean()

Calculate agent turnover rate by time of day

python: turnover_rate_by_time = data.groupby("time_of_day")["left_company"].mean()

Calculate agent utilization rate

python: utilization_rate = data[data["utilized"] == 1].count() / data["utilized"].count()

Calculate average after call work time by reason

python: average_acw_time_by_reason = data.groupby("call_reason")["after_call_work_time"].mean()

Calculate average after call work time by time of day

python: average_acw_time_by_time = data.groupby("time_of_day")["after_call_work_time"].mean()

Calculate average call wait time

python: average_wait_time = data["wait_time"].mean()

Calculate average call wait time by time of day

python: average_wait_time_by_time = data.groupby("time_of_day")["wait_time"].mean()

Calculate average handle time

python: average_handle_time = data["handle_time"].mean()

Calculate average handle time by reason

python: average_handle_time_by_reason = data.groupby("call_reason")["handle_time"].mean()

Calculate average handle time by time of day

python: average_handle_time_by_time = data.groupby("time_of_day")["handle_time"].mean()

Calculate average hold time

python: average_hold_time = data["hold_time"].mean()

Calculate average hold time by reason

python: average_hold_time_by_reason = data.groupby("call_reason")["hold_time"].mean()

Calculate average hold time by time of day

python: average_hold_time_by_time = data.groupby("time_of_day")["hold_time"].mean()

Calculate average queue time by reason

python: average_queue_time_by_reason = data.groupby("call_reason")["queue_time"].mean()

Calculate average speed to answer

python: average_speed_to_answer = data["speed_to_answer"].mean()

Calculate average speed to answer by time of day

python: average_speed_to_answer_by_time = data.groupby("time_of_day")["speed_to_answer"].mean()

Calculate average talk time

python: average_talk_time = data["talk_time"].mean()

Calculate average talk time by reason

python: average_talk_time_by_reason = data.groupby("call_reason")["talk_time"].mean()

Calculate average talk time by time of day

python: average_talk_time_by_time = data.groupby("time_of_day")["talk_time"].mean()

Calculate average time in after call work

python: average_time_in_acw = data["after_call_work_time"].mean()

Calculate average time in after call work by reason

python: average_time_in_acw_by_reason = data.groupby("call_reason")["after_call_work_time"].mean()

Calculate average time in after call work by time of day

python: average_time_in_acw_by_time = data.groupby("time_of_day")["after_call_work_time"].mean()

Calculate average time in queue

python: average_time_in_queue = data["queue_time"].mean()

Calculate average time in queue before abandoning

python: average_time_in_queue_before_abandoning = data[data["status"] == "abandoned"]["queue_time"].mean()

Calculate average time in queue before abandoning by reason

python: average_time_in_queue_before_abandoning_by_reason = data[data["status"] == "abandoned"].groupby("call_reason")["queue_time"].mean()

Calculate average time in queue before abandoning by time of day

python: average_time_in_queue_before_abandoning_by_time = data[data["status"] == "abandoned"].groupby("time_of_day")["queue_time"].mean()

Calculate average time in queue before disconnecting

python: average_time_in_queue_before_disconnecting = data[data["status"] == "disconnected"]["queue_time"].mean()

Calculate average time in queue before disconnecting by reason

python: average_time_in_queue_before_disconnecting_by_reason = data[data["status"] == "disconnected"].groupby("call_reason")["queue_time"].mean()

Calculate average time in queue before disconnecting by time of day

python: average_time_in_queue_before_disconnecting_by_time = data[data["status"] == "disconnected"].groupby("time_of_day")["queue_time"].mean()

Calculate average time in queue before transferring

python: average_time_in_queue_before_transferring = data[data["status"] == "transferred"]["queue_time"].mean()

Calculate average time in queue before transferring by reason

python: average_time_in_queue_before_transferring_by_reason = data[data["status"] == "transferred"].groupby("call_reason")["queue_time"].mean()

Calculate average time in queue before transferring by time of day

python: average_time_in_queue_before_transferring_by_time = data[data["status"] == "transferred"].groupby("time_of_day")["queue_time"].mean()

Calculate average wrap-up time

python: average_wrap_up_time = data["wrap_up_time"].mean()

Calculate average wrap-up time by reason

python: average_wrap_up_time_by_reason = data.groupby("call_reason")["wrap_up_time"].mean()

Calculate average wrap-up time by time of day

python: average_wrap_up_time_by_time = data.groupby("time_of_day")["wrap_up_time"].mean()

Calculate call abandonment rate by reason

python: call_abandonment_rate_by_reason = data[data["status"] == "abandoned"].groupby("call_reason").count() / data.groupby("call_reason").count()

Calculate call abandonment rate by time of day

python: call_abandonment_rate_by_time = data[data["status"] == "abandoned"].groupby("time_of_day").count() / data.groupby("time_of_day").count()

Calculate call back rate

python: call_back_rate = data[data["call_back_requested"] == 1].count() / data["call_back_requested"].count()

Calculate call back rate by time of day

python: call_back_rate_by_time = data[data["call_back_requested"] == 1].groupby("time_of_day").count() / data.groupby("time_of_day").count()

Calculate customer effort score

python: customer_effort_score = data["customer_effort"].mean()

Calculate customer satisfaction by reason

python: customer_satisfaction_by_reason = data.groupby("call_reason")["customer_satisfaction"].mean()

Calculate customer satisfaction score

python: customer_satisfaction_score = data["customer_satisfaction"].mean()

Calculate first call resolution rate

python: first_call_resolution_rate = data[data["first_call_resolution"] == 1].count() / data["first_call_resolution"].count()

Calculate first call resolution rate by reason

python: first_call_resolution_rate_by_reason = data[data["first_call_resolution"] == 1].groupby("call_reason").count() / data.groupby("call_reason").count()

Calculate net promoter score

python: net_promoter_score = data["net_promoter_score"].mean()

Calculate net promoter score by reason

python: net_promoter_score_by_reason = data.groupby("call_reason")["net_promoter_score"].mean()

Calculate net promoter score by time of day

python: net_promoter_score_by_time = data.groupby("time_of_day")["net_promoter_score"].mean()

Calculate quality assurance score

python: quality_assurance_score = data["quality_assurance_score"].mean()

Calculate service level

python: service_level = data[data["call_answer_time"] <= data["service_level_goal"]].count() / data["call_answer_time"].count()

Calculate service level by reason

python: service_level_by_reason = data[data["call_answer_time"] <= data["service_level_goal"]].groupby("call_reason").count() / data.groupby("call_reason").count()

Calculate service level by time of day

python: service_level_by_time = data[data["call_answer_time"] <= data["service_level_goal"]].groupby("time_of_day").count() / data.groupby("time_of_day").count()

Calculate call center training budget

python: training_budget = data["training_cost"].sum()

Calculate call center training churn rate

python: training_churn_rate = data[data["left_company_after_training"] == 1].count() / data["left_company_after_training"].count()

Calculate call center training cost per agent

python: training_cost_per_agent = data["training_cost"].sum() / data["agent_id"].nunique()

Calculate call center training cost per day

python: training_cost_per_day = data["training_cost"].sum() / data["training_days"].sum()

Calculate call center training cost per hour

python: training_cost_per_hour = data["training_cost"].sum() / (data["training_days"].sum() * 8)

Calculate call center training cost per month

python: training_cost_per_month = data["training_cost"].sum() / (data["training_days"].sum() / 30)

Calculate call center training cost per week

python: training_cost_per_week = data["training_cost"].sum() / (data["training_days"].sum() / 7)

Calculate call center training cost per year

python: training_cost_per_year = data["training_cost"].sum() / (data["training_days"].sum() / 365)

Calculate call center training effectiveness

python: training_effectiveness = data[data["training_effective"] == 1].count() / data["training_effective"].count()

Calculate call center training efficiency

python: training_efficiency = data["training_days"].sum() / data["agent_id"].nunique()

Calculate the number of agents who completed training

python: training_completed = data[data["training_completed"] == 1].count()

Calculate the number of agents who left the company after training

python: training_churn = data[(data["training_completed"] == 1) & (data["left_company"] == 1)].count()

Calculate the training retention rate

python: training_retention_rate = 1 - (training_churn / training_completed)

Calculate call resolution rate

python: call_resolution_rate = data[data["status"] == "resolved"].count() / data["status"].count()

Calculate call resolution rate by time of day

python: call_resolution_rate_by_time = data[data["status"] == "resolved"].groupby("time_of_day").count() / data.groupby("time_of_day").count()

Calculate call routing efficiency

python: call_routing_efficiency = data[data["call_routed_correctly"] == 1].count() / data["call_routed_correctly"].count()

Calculate call routing efficiency by time of day

python: call_routing_efficiency_by_time = data[data["call_routed_correctly"] == 1].groupby("time_of_day").count() / data.groupby("time_of_day").count()

Calculate call transfer rate

python: call_transfer_rate = data[data["status"] == "transferred"].count() / data["status"].count()

Calculate call transfer rate by time of day

python: call_transfer_rate_by_time = data[data["status"] == "transferred"].groupby("time_of_day").count() / data.groupby("time_of_day").count()

Calculate call center revenue retention rate

python: revenue_retention_rate = data[data["revenue_retained"] == 1].count() / data["revenue_retained"].count()

Calculate call center revenue retention rate by reason

python: revenue_retention_rate_by_reason = data[data["revenue_retained"] == 1].groupby("call_reason").count() / data.groupby("call_reason").count()

Calculate call center revenue retention rate by time of day

python: revenue_retention_rate_by_time = data[data["revenue_retained"] == 1].groupby("time_of_day").count() / data.groupby("time_of_day").count()

Calculate call center capacity utilization rate

python: capacity_utilization_rate = data["call_count"].sum() / (data["agent_id"].nunique() * data["hours_of_operation"].max())

Calculate call center cost per agent

python: cost_per_agent = data["agent_cost"].sum() / data["agent_id"].nunique()

Calculate call center cost per call

python: cost_per_call = data["call_cost"].sum() / data["call_count"].sum()

Calculate call center cost per agent by reason

python: cost_per_agent_by_reason = data.groupby("call_reason")["agent_cost"].sum() / data.groupby("call_reason")["agent_id"].nunique()

Calculate call center cost per agent by time of day

python: cost_per_agent_by_time = data.groupby("time_of_day")["agent_cost"].sum() / data.groupby("time_of_day")["agent_id"].nunique()

Calculate call center cost per call

python: cost_per_call = data["call_cost"].sum() / data["call_count"].sum()

Calculate call center cost per call by reason

python: cost_per_call_by_reason = data.groupby("call_reason")["call_cost"].sum() / data.groupby("call_reason")["call_count"].sum()

Calculate call center cost per call by time of day

python: cost_per_call_by_time = data.groupby("time_of_day")["call_cost"].sum() / data.groupby("time_of_day")["call_count"].sum()

Calculate call center cost per contact

python: cost_per_contact = data["contact_cost"].sum() / data["contact_count"].sum()

Calculate call center cost per contact by reason

python: cost_per_contact_by_reason = data.groupby("call_reason")["contact_cost"].sum() / data.groupby("call_reason")["contact_count"].sum()

Calculate call center cost per contact by time of day

python: cost_per_contact_by_time = data.groupby("time_of_day")["contact_cost"].sum() / data.groupby("time_of_day")["contact_count"].sum()

Calculate call center cost per customer

python: cost_per_customer = data["customer_cost"].sum() / data["customer_count"].sum()

Calculate call center revenue per week

python: revenue_per_week = data["revenue"].sum() / (data["week"].nunique())

Calculate call center revenue per week by reason

python: revenue_per_week_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["week"].nunique()

Calculate call center revenue per week by time of day

python: revenue_per_week_by_time = data.groupby("time_of_day")["revenue"].sum() / data.groupby("time_of_day")["week"].nunique()

Calculate call center revenue per year

python: revenue_per_year = data["revenue"].sum() / (data["year"].nunique())

Calculate call center revenue per year by reason

python: revenue_per_year_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["year"].nunique()

Calculate call center revenue per year by time of day

python: revenue_per_year_by_time = data.groupby("time_of_day")["revenue"].sum() / data.groupby("time_of_day")["year"].nunique()

Calculate call center revenue per minute

python: revenue_per_minute = data["revenue"].sum() / (data["call_duration"].sum() / 60)

Calculate call center revenue per minute by reason

python: revenue_per_minute_by_reason = data.groupby("call_reason")["revenue"].sum() / (data.groupby("call_reason")["call_duration"].sum() / 60)

Calculate call center revenue per minute by time of day

python: revenue_per_minute_by_time = data.groupby("time_of_day")["revenue"].sum() / (data.groupby("time_of_day")["call_duration"].sum() / 60)

Calculate call center revenue per month

python: revenue_per_month = data["revenue"].sum() / (data["month"].nunique())

Calculate call center revenue per month by reason

python: revenue_per_month_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["month"].nunique()

Calculate call center revenue per month by time of day

python: revenue_by_time = data.groupby("time_of_day")["revenue"].sum() / data.groupby("time_of_day")["duration"].count()

Calculate call center revenue per hour

python: revenue_per_hour = data["revenue"].sum() / (data["hours_of_operation"].max())

Calculate call center revenue per hour by reason

python: revenue_per_hour_by_reason = data.groupby("call_reason")["revenue"].sum() / (data.groupby("call_reason")["hours_of_operation"].max())

Calculate call center revenue per hour by time of day

python: revenue_per_hour_by_time = data.groupby("time_of_day")["revenue"].sum() / (data.groupby("time_of_day")["hours_of_operation"].max())

Calculate call center revenue per interaction

python: revenue_per_interaction = data["revenue"].sum() / data["interaction_count"].sum()

Calculate call center revenue per interaction by reason

python: revenue_per_interaction_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["interaction_count"].sum()

Calculate call center revenue per interaction by time of day

python: revenue_per_interaction_by_time = data.groupby("time_of_day")["revenue"].sum() / data.groupby("time_of_day")["interaction_count"].sum()

Calculate call center revenue per customer

python: revenue_per_customer = data["revenue"].sum() / data["customer_count"].sum()

Calculate call center revenue per customer by reason

python: revenue_per_customer_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["customer_count"].sum()

Calculate call center revenue per customer by time of day

python: revenue_per_customer_by_time = data.groupby("time_of_day")["revenue"].sum() / data.groupby("time_of_day")["customer_count"].sum()

Calculate call center revenue per day

python: revenue_per_day = data["revenue"].sum() / data["day"].nunique()

Calculate call center revenue per day by reason

python: revenue_per_day_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["day"].nunique()

Calculate call center revenue per call

python: revenue_per_call = data["revenue"].sum() / data["call_count"].sum()

Calculate call center revenue per call by reason

python: revenue_per_call_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["call_count"].sum()

Calculate call center revenue per call by time of day

python: revenue_per_call_by_time = data.groupby("time_of_day")["revenue"].sum() / data.groupby("time_of_day")["call_count"].sum()

Calculate call center revenue per contact

python: revenue_per_contact = data["revenue"].sum() / data["contact_count"].sum()

Calculate call center revenue per contact by reason

python: revenue_per_contact_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["contact_count"].sum()

Calculate call center revenue per contact by time of day

python: revenue_per_contact_by_time = data.groupby("time_of_day")["revenue"].sum() / data.groupby("time_of_day")["contact_count"].sum()

Calculate call center cost per customer by reason

python: cost_per_customer_by_reason = data.groupby("call_reason")["cost"].sum() / data.groupby("call_reason")["customer_count"].sum()

Calculate call center cost per customer by time of day

python: cost_per_customer_by_time = data.groupby("time_of_day")["cost"].sum() / data.groupby("time_of_day")["customer_count"].sum()

Calculate call center cost per day

python: cost_per_day = data["cost"].sum() / data["day"].nunique()

Calculate call center cost per day by reason

python: cost_per_day_by_reason = data.groupby("call_reason")["cost"].sum() / data.groupby("call_reason")["day"].nunique()

Calculate call center cost per day by time of day

python: cost_per_day_by_time = data.groupby("time_of_day")["cost"].sum() / data.groupby("time_of_day")["day"].nunique()

Calculate call center cost per minute

python: cost_per_minute = data["cost"].sum() / (data["call_duration"].sum() / 60)

Calculate call center cost per minute by reason

python: cost_per_minute_by_reason = data.groupby("call_reason")["cost"].sum() / (data.groupby("call_reason")["call_duration"].sum() / 60)

Calculate call center cost per minute by time of day

python: cost_per_minute_by_time = data.groupby("time_of_day")["cost"].sum() / (data.groupby("time_of_day")["call_duration"].sum() / 60)

Calculate call center cost per month

python: cost_per_month = data["cost"].sum() / data["month"].nunique()

Calculate call center cost per month by reason

python: cost_per_month_by_reason = data.groupby("call_reason")["cost"].sum() / data.groupby("call_reason")["month"].nunique()

Calculate call center cost per month by time of day

python: cost_per_month_by_time = data.groupby("time_of_day")["cost"].sum() / data.groupby("time_of_day")["month"].nunique()

Calculate call center cost savings

python: cost_savings = data["initial_cost"].sum() - data["cost"].sum()

Calculate call center cost savings by reason

python: cost_savings_by_reason = data.groupby("call_reason")["initial_cost"].sum() - data.groupby("call_reason")["cost"].sum()

Calculate call center cost savings by time of day

python: cost_savings_by_time = data.groupby("time_of_day")["initial_cost"].sum() - data.groupby("time_of_day")["cost"].sum()

Calculate call center efficiency rate

python: efficiency_rate = (data["call_count"].sum() / data["hours_of_operation"].max()) / data["agent_count"].max()

Calculate call center efficiency rate by reason

python: efficiency_rate_by_reason = (data.groupby("call_reason")["call_count"].sum() / data.groupby("call_reason")["hours_of_operation"].max()) / data.groupby("call_reason")["agent_count"].max()

Calculate call center efficiency rate by time of day

python: efficiency_rate_by_time = (data.groupby("time_of_day")["call_count"].sum() / data.groupby("time_of_day")["hours_of_operation"].max()) / data.groupby("time_of_day")["agent_count"].max()

Calculate call center occupancy rate

python: occupancy_rate = (data["hours_of_operation"].max() - data["idle_time"].sum()) / data["hours_of_operation"].max()

Calculate call center performance index

python: performance_index = (data["call_count"].sum() / data["hours_of_operation"].max()) * data["satisfaction_score"].mean()

Calculate call center performance index by reason

python: performance_index_by_reason = (data.groupby("call_reason")["call_count"].sum() / data.groupby("call_reason")["hours_of_operation"].max()) * data.groupby("call_reason")["satisfaction_score"].mean()

Calculate call center performance index by time of day

python: performance_index_by_time = (data.groupby("time_of_day")["call_count"].sum() / data.groupby("time_of_day")["hours_of_operation"].max()) * data.groupby("time_of_day")["satisfaction_score"].mean()

Calculate call center productivity rate

python: productivity_rate = data["call_count"].sum() / (data["hours_of_operation"].max() * data["agent_count"].max())

Calculate call center productivity rate by reason

python: productivity_rate_by_reason = data.groupby("call_reason")["call_count"].sum() / (data.groupby("call_reason")["hours_of_operation"].max() * data.groupby("call_reason")["agent_count"].max())

Calculate call center productivity rate by time of day

python: productivity_rate_by_time = data.groupby("time_of_day")["call_count"].sum() / (data.groupby("time_of_day")["hours_of_operation"].max() * data.groupby("time_of_day")["agent_count"].max())

Calculate call center revenue churn rate

python: revenue_churn_rate = (data["initial_revenue"].sum() - data["revenue"].sum()) / data["initial_revenue"].sum()

Calculate call center revenue churn rate by reason

python: revenue_churn_rate_by_reason = (data.groupby("call_reason")["initial_revenue"].sum() - data.groupby("call_reason")["revenue"].sum()) / data.groupby("call_reason")["initial_revenue"].sum()

Calculate call center revenue churn rate by time of day

python: revenue_churn_rate_by_time = (data.groupby("time_of_day")["initial_revenue"].sum() - data.groupby("time_of_day")["revenue"].sum()) / data.groupby("time_of_day")["initial_revenue"].sum()

Calculate call center revenue growth

python: revenue_growth = (data["revenue"].sum() - data["initial_revenue"].sum()) / data["initial_revenue"].sum()

Calculate call center revenue growth by reason

python: revenue_growth_by_reason = (data.groupby("call_reason")["revenue"].sum() - data.groupby("call_reason")["initial_revenue"].sum()) / data.groupby("call_reason")["initial_revenue"].sum()

Calculate call center revenue growth by time of day

python: revenue_growth_by_time = (data.groupby("time_of_day")["revenue"].sum() - data.groupby("time_of_day")["initial_revenue"].sum()) / data.groupby("time_of_day")["initial_revenue"].sum()

Calculate call center revenue per agent

python: revenue_per_agent = data["revenue"].sum() / data["agent_count"].max()

Calculate call center revenue per agent by reason

python: revenue_per_agent_by_reason = data.groupby("call_reason")["revenue"].sum() / data.groupby("call_reason")["agent_count"].max()

Calculate call center revenue per agent by time of day

python: revenue_per_agent_by_time = data.groupby("time_of_day")["revenue"].sum() / data.groupby("time_of_day")["agent_count"].max()