Estimate the financial impact and ROI of transitioning from manual outbound dialling to an AI-powered proactive outbound solution in a healthcare contact centre coordinating patient transport.
This is a HEURISTIC financial model for a healthcare outbound contact centre that:
• Arranges taxis to transport patients to hospital
• Currently uses manual outbound dialling
• Employs 100 outbound agents
• Experiences high answerphone / no-answer rates
• Is transitioning to an AI-powered proactive outbound solution
The AI solution will:
• Automatically dial patients
• Retry intelligently
• Detect answerphone
• Handle confirmations via IVR / voice AI
• Escalate only live / exception cases to agents
• Reduce manual dial time
• Increase contact rates
• Reduce wasted agent time
• Reduce overtime
All financial values must be in £ (GBP).
No external benchmarks.
All improvements must be explicit user-editable assumptions.
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CALCULATOR OBJECTIVE
Estimate:
• Labour time saved from automation
• Reduction in answerphone / no-answer wasted time
• Reduced overtime
• Improved confirmation rates
• Reduced missed appointments
• Annual labour savings
• Total annual costs
• Net benefit (Year 1)
• ROI (%)
• Payback period (Months)
• 3-Year Net Benefit
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MANDATORY MODELING RULES
• Heuristic-only mode
• All performance uplifts MUST be explicit "Assumption:" inputs
• No hidden constants
• All assumptions must feed a measurable financial output
• All inputs must affect at least one Terminal KPI
• Annualisation MUST be explicit (× 12 shown in formulaString)
• Payback MUST use annualised benefit divided to monthly
• No algebraic collapsing
• No unused inputs
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SECTION 1 — BASELINE STRUCTURE INPUTS
• Number of Outbound Agents (default 100)
• Outbound Calls per Month
• Average Call Duration (minutes)
• Average Dial Attempts per Booking
• No-Answer Rate (%)
• Answerphone Rate (%)
• Successful Contact Rate (%)
• Agent Fully Loaded Cost per Year (£)
• Overtime Hours per Month
• Overtime Cost Multiplier (%)
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SECTION 2 — MANUAL DIAL INEFFICIENCY
Derived Baseline:
Total Monthly Dial Attempts =
Calls_per_Month * Avg_Dial_Attempts
Wasted Calls (No Answer + Answerphone)
Wasted Minutes per Month =
Wasted_Calls * Avg_Call_Duration
Annual Wasted Hours =
(Wasted_Minutes / 60) * 12
Annual Wasted Labour Cost =
Annual_Wasted_Hours *
(Agent_Fully_Loaded_Cost / Productive_Hours_Per_Year)
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SECTION 3 — AI OUTBOUND IMPROVEMENTS
Assumptions:
• Assumption: Answerphone Detection Efficiency (%)
• Assumption: No-Answer Retry Automation Efficiency (%)
• Assumption: Agent Escalation Rate After AI (%)
• Assumption: Overtime Reduction (%)
• Assumption: Missed Appointment Reduction (%)
Derived:
Reduced Wasted Minutes
Post-AI Escalated Calls to Agents
Agent Handling Hours Post-AI
Annual Labour Savings =
(Baseline_Hours - Post_AI_Hours) *
(Agent_Fully_Loaded_Cost / Productive_Hours_Per_Year)
Overtime Savings =
Overtime_Hours_per_Month *
12 *
Overtime_Cost
Missed Appointment Avoidance Value =
Assumption: Cost per Missed Appointment (£)
*
Reduction_in_Missed_Appointments
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SECTION 4 — PRODUCTIVE HOURS CALCULATION
Productive Hours per FTE per Year =
2080 * (1 - Shrinkage%)
If shrinkage not provided, it MUST be an explicit input.
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SECTION 5 — AI SOLUTION COSTS
Inputs:
• Implementation Cost (£)
• AI Platform Licence per Month (£)
• AI Usage Cost per Call (£)
• Support Cost per Month (£)
Derived:
Annual Recurring Cost =
(AI_Platform_Licence * 12)
One-Time Cost =
Implementation Cost
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FINANCIAL STRUCTURE
Total Annual Savings =
Labour_Savings
Net Annual Benefit =
Total_Annual_Savings - Annual_Recurring_Cost
Year 1 Net Benefit =
Net_Annual_Benefit - One_Time_Cost
ROI (%) =
IF((Annual_Recurring_Cost + One_Time_Cost) > 0,
Year_1_Net_Benefit /
(Annual_Recurring_Cost + One_Time_Cost) * 100,
0)
Payback (Months) =
IF(Net_Annual_Benefit / 12 > 0,
One_Time_Cost /
(Net_Annual_Benefit / 12),
0)
3-Year Net Benefit =
(Net_Annual_Benefit * 3) - One_Time_Cost