CC Hub | Contact Center Operations Platform

Agents Required

—

From last Erlang C run

Predicted SLA

—

% answered in threshold

Abandonment Rate

—

From last ACR calc

Occupancy

—

Target: 80–85%

Est. Cost / Hour

—

From cost estimator

Quick Erlang C

Live calculation — results update as you type

Live

Calls / 30 min

AHT (seconds)

Target SLA %

Answer in (sec)

Agents

—

minimum

SLA %

—

achieved

Occupancy

—

utilisation

Avg Wait

—

seconds

Platform Overview

Built by Jefri Karo Karo · breadandletters.com

📞 Erlang C — Basic, Advanced (AHT breakdown), Comparison Table

🎯 SLA Predictor — Live prediction + sensitivity matrix

📉 ACR / Abandonment — Actual vs theoretical + patience curve

🔻 Shrinkage — 7 components, gross headcount model

⚡ Occupancy — Utilisation gauge + burnout alerts

📈 Forecast — 4 methods + seasonal factors

👥 Headcount Planner — Interval-by-interval staffing plan

💰 Cost Estimator — Multi-currency, annual modeling

🗓 Schedule Efficiency — Required vs. scheduled gap analysis

🎛 What-If Simulator — Real-time slider scenario modeling

📂 Data Upload — CSV/Excel, daily/weekly/monthly/intraday

🤖 AI Advisor — Claude-powered CC expert

☁ Firebase Cloud — Save/load sessions across devices

Industry Benchmarks Reference

Voice SLA

80/20

80% in 20s

Chat SLA

80/30

80% in 30s

Occupancy

80–85%

optimal range

Abandonment

<5%

acceptable

Shrinkage

25–35%

typical range

AHT Voice

4–6 min

240–360 sec

FCR Target

>75%

first call resolution

CSAT Target

>85%

customer satisfaction

ℹErlang C models queuing systems where callers wait for the next available agent — the gold standard for CC staffing since A.K. Erlang, 1917.

Erlang C Calculator

Optimal agent staffing based on queuing theory

Erlang C

Basic

Advanced (AHT Breakdown)

Agent Comparison Table

Multi-Channel

Call Volume

Period Length

AHT (seconds)

Target SLA %

Answer Threshold (sec)

Max Caller Patience (sec)

Calls / Period

Period

Talk Time (sec)

Hold Time (sec)

Wrap-up (sec)

Target SLA %

Threshold (sec)

Shrinkage %

Channel

Max Concurrency (Chat)

Calls

Period

AHT (sec)

Threshold (sec)

Agents	SLA %	Occupancy	Avg Wait (sec)	P(Delay)	ASA (sec)	Status

⚠Multi-channel mode calculates agents needed per channel independently, then shows blended summary.

SLA Predictor

Predict % service level given current staffing, volume, and AHT

Prediction

Agents on Queue

Calls / Period

AHT (sec)

Threshold (sec)

Period

Historical ACR %

Sensitivity Matrix

Volume

AHT (sec)

Threshold (sec)

Agents	SLA %	Occupancy	Avg Wait	Traffic (E)	Status

ACR & Abandonment Rate

Actual ACR, theoretical model, and patience curve analysis

Patience Model

Calculator

Patience Curve

Staffing Impact

Total Calls Offered

Calls Abandoned

Avg Wait Time (sec)

Avg Patience (sec)

Calls / 30 min

AHT (sec)

Agents on Queue

Benchmark ACR %

Avg Patience (sec)

Model to (sec)

Wait (sec)	Abandon %	Callers Lost	Impact Level

ℹShows how ACR changes as you add/remove agents. Helps find the minimum staffing to achieve target ACR.

Calls / 30 min

AHT (sec)

Patience (sec)

Agents	Avg Wait	Est. ACR %	SLA %	Occupancy

Shrinkage Calculator

Gross headcount after all shrinkage components

Headcount

ℹShrinkage is time agents are paid but unavailable to handle contacts. Industry standard: 25–35%. Above 40% = management issue.

Net Agents (from Erlang C)

Operating Hours / Day

Shrinkage Components (%)

🍵 Breaks & Lunch

📚 Training

🤒 Sick Leave

📅 Annual Leave

🗣 Coaching & Meetings

💻 System / Logon

📋 Admin / Other

🔄 Custom Component

Occupancy Analyzer

Agent utilisation — find the sweet spot between efficiency and wellbeing

Utilisation

⚠Optimal: 80–85%. Above 85% → burnout risk, quality drops, attrition spikes. Below 70% → overstaffed, cost waste.

Calls / Period

AHT (sec)

Agents on Queue

Period (min)

Target Max Occupancy %

Min Acceptable Occupancy %

Volume Forecasting

4 models: Linear Regression, Moving Average, Exponential Smoothing, Seasonal Decomposition

Forecasting

Historical Data

Seasonal Factors

Forecast Output

Forecast Method

Window / Alpha

Periods to Forecast

Period Type

Historical Data (manual entry or upload CSV)

ℹSeasonal factors multiply the base forecast. 1.0 = baseline, 1.2 = 20% above, 0.8 = 20% below. Use historical patterns to set these.

Headcount Planner

Interval-by-interval staffing plan across full operating hours

Staffing Plan

Start Time

End Time

Interval

AHT (sec)

Target SLA %

Threshold (sec)

Shrinkage %

Daily Volume

Volume Pattern

Days of Week

Cost Estimator

Full staffing cost model — salary, overhead, training, technology

Financial

Currency

Avg Hourly Rate (Agent)

Net Agents Required

Shrinkage %

Operating Hours / Day

Days / Week

Benefits & Overhead %

Team Leader Ratio (1:N)

Training Cost / Agent

Tech / Seat / Agent / Month

Attrition Rate %

Recruiting Cost / Hire

Schedule Efficiency Analyzer

Compare Erlang-required vs. actual scheduled agents — find every gap

Gap Analysis

ℹEnter your Erlang C required agents and actual scheduled headcount per interval. The tool calculates schedule efficiency, understaffed and overstaffed intervals.

Start

End

Interval

What-If Simulator

Real-time scenario modelling — drag sliders to see instant impact on all KPIs

Live Sim

📞 Call Volume (per 30 min)

200

⏱ AHT (seconds)

240s

👥 Agents on Queue

15

🎯 SLA Threshold (sec)

20s

🔻 Shrinkage %

30%

💰 Hourly Rate

8

😤 Max Patience (sec)

120s

SLA Achievement

—

Occupancy

—

Avg Wait Time

—

Est. Abandonment

—

Gross Agents Needed

—

Est. Cost / Hour

—

Data Upload Center

Import CSV or Excel files — daily, weekly, monthly, or intraday (any interval)

Data

ℹSupported formats: CSV, Excel (.xlsx/.xls). Supported data types: Intraday call volumes, Daily summaries, Monthly reports, Agent rosters. After upload, map your columns and use the data across all calculators.

Intraday Volume

Daily Summary

Monthly Report

Agent Roster

Custom Format

📊

Drop Intraday Volume File

CSV or Excel • Columns: Time, Calls, AHT, Handled, Abandoned

Expected columns (customizable):

Time (HH:MM or interval label)
Calls_Offered (integer)
Calls_Handled (integer)
Calls_Abandoned (integer)
AHT_Seconds (integer)
Agents_Scheduled (integer) [optional]
Agents_Available (integer) [optional]

📅

Drop Daily Summary File

CSV or Excel • Date, Volume, AHT, SLA, ACR, Agents

Expected columns:

Date (YYYY-MM-DD)
Total_Calls (integer)
Calls_Handled (integer)
Calls_Abandoned (integer)
AHT_Seconds (integer)
SLA_Pct (0.0–1.0 or 0–100)
Agents_Rostered (integer) [optional]

📆

Drop Monthly Report File

CSV or Excel • Month, Volume, KPIs — auto-feeds Forecast

Expected columns:

Month (YYYY-MM or label)
Total_Calls (integer)
Avg_AHT_Seconds (integer)
Avg_SLA_Pct (float)
Avg_ACR_Pct (float)
Avg_Occupancy_Pct (float)
Headcount (integer) [optional]

👥

Drop Agent Roster File

CSV or Excel • Agent list with shift schedules

Expected columns:

Agent_ID (string)
Agent_Name (string)
Team (string)
Shift_Start (HH:MM)
Shift_End (HH:MM)
Days_On (e.g. Mon-Fri)
Status (Active/Leave/Training)

⚠Custom format lets you upload any CSV/Excel and manually map columns to CC Hub fields.

📋

Drop Any CSV / Excel File

You'll map columns manually after upload

Loaded Datasets

No datasets loaded yet. Upload a file above.

Firebase Firestore Setup

CC Hub uses Google Firebase Firestore (free Spark plan) to save configurations and data across sessions and devices.
Free tier: 50,000 reads/day · 20,000 writes/day · 1GB storage · No credit card needed.

Setup (3 minutes): console.firebase.google.com → New Project → Firestore Database → Add Web App → copy config below.

API Key

Project ID

Auth Domain

App ID

Save Session

Store current state to Firebase cloud

Session Name

Notes

Tags (comma-separated)

Saved Sessions

Load or delete previous sessions

Connect Firebase and click Refresh.

Center Profile

Contact Center Name

Your Name / Manager

Industry

Default SLA Target %

Default SLA Threshold (sec)

Default AHT (sec)

Default Shrinkage %

Default Currency

Default Interval

About CC Hub

CC Hub is a free, open, browser-based Contact Center Intelligence Platform built by Jefri Karo Karo (breadandletters.com).

All calculations run locally in your browser. Data only leaves your device when: using AI Advisor (→ Anthropic API), or saving sessions (→ your own Firebase project).

Mathematical Models:

Erlang C queuing theory (A.K. Erlang, 1917) · Exponential patience distribution · Linear regression · Exponential smoothing (Holt) · Seasonal decomposition (additive)

References:

ICMI Contact Center Standards · COPC CX Standard v7 · NICE WFM Best Practices · Genesys Cloud WFM Guide · HDI Service Desk Standards

Dashboard

Firebase Firestore Setup