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ROI for real-time agent knowledge management for a contact centre.

AI Generated
Multi-Page
PDF Report

Estimate the financial impact of implementing a real-time agent knowledge management platform for omnichannel contact centres, including annual efficiency, quality, training, supervisor/admin, and revenue uplift savings.

Model Capabilities
  • AI Generated
  • Sub 60 Seconds to create
  • Refactored Inputs into Groups
  • Changes key Outputs to Cards
  • Total Time 3 Minutes
Prompt Used for AI Generation:

Knowledge Management Platform ROI Calculator

Real-Time Agent Knowledge for Omnichannel Contact Centre

This is a HEURISTIC financial model for implementing a Knowledge Management (KM) platform that provides real-time contextual information to agents across:

• Voice
• Email
• Chat
• WhatsApp

The KM platform delivers:

• Faster answer retrieval
• Reduced Average Handle Time (AHT)
• Improved First Contact Resolution (FCR)
• Reduced repeat contacts
• Reduced onboarding/training time
• Improved CSAT leading to improved NPS
• Reduced supervisor dependency
• Lower knowledge maintenance effort

All financial values must be in £ (GBP).

No external benchmarks.
All uplifts must be explicit user-editable assumptions.

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CALCULATOR OBJECTIVE

Estimate:

• Annual efficiency savings
• Annual quality/compliance savings
• Revenue uplift from CSAT/NPS improvement
• Training savings
• Supervisor/admin savings
• Total annual 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 improvements 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

• Total Agents
• Voice Contacts per Month
• Email Contacts per Month
• Chat Contacts per Month
• WhatsApp Contacts per Month

• Average Handle Time – Voice (mins)
• Average Handle Time – Email (mins)
• Average Handle Time – Chat (mins)
• Average Handle Time – WhatsApp (mins)

• Agent Fully Loaded Cost per Year (£)
• Supervisor Fully Loaded Cost per Year (£)

• Current First Contact Resolution (%)
• Current CSAT (%)
• Current NPS
• Annual Revenue (£)

• Training Weeks per Agent per Year
• Working Hours per Week

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SECTION 2 — AHT REDUCTION

Assumptions:

• Assumption: AHT Reduction (%) from Real-Time Knowledge
• Assumption: Realisation Factor (%)

Derived:

Total Monthly Handling Minutes (All Channels)

Annual Handling Minutes =
Monthly_Total * 12

Hours Saved (Annual) =
Baseline_Hours - Post_KM_Hours

Realised Hours Saved =
Hours_Saved * (Realisation_Factor / 100)

Annual Labour Savings =
Realised_Hours_Saved *
(Agent_Fully_Loaded_Cost / Productive_Hours_Per_Year)

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SECTION 3 — FCR IMPROVEMENT

Assumptions:

• Assumption: FCR Improvement (percentage points)
• Assumption: Repeat Contact Rate for Unresolved (%)

Derived:

Avoided Repeat Contacts (Annualised)

Avoided Handling Hours

Annual Labour Savings from FCR

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SECTION 4 — TRAINING TIME REDUCTION

Assumptions:

• Assumption: Training Reduction (%)

Baseline Training Hours =
Agents * Weeks * Hours_Per_Week

Post-KM Training Hours =
Baseline * (1 - Training_Reduction / 100)

Training Hours Saved

Annual Training Cost Savings

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SECTION 5 — SUPERVISOR & KNOWLEDGE ADMIN EFFICIENCY

Assumptions:

• Assumption: Supervisor Time Reduction (hrs/month)
• Assumption: Knowledge Maintenance Efficiency Gain (%)

Derived:

Annual Supervisor Hours Saved =
Reduction * 12

Annual Supervisor Cost Savings

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SECTION 6 — CSAT → NPS → REVENUE IMPACT

Assumptions:

• Assumption: CSAT Uplift (points)
• Assumption: NPS Change per CSAT Point
• Assumption: Revenue Uplift per NPS Point (%)

Derived:

New NPS

Revenue Uplift %

Annual Incremental Revenue =
Annual_Revenue * Revenue_Uplift%

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SECTION 7 — KM PLATFORM COSTS

Inputs:

• Implementation Cost (£)
• Change Management Cost (£)
• Licence Cost per Agent per Month (£)
• Knowledge Admin Licence per Month (£)
• Support Cost per Month (£)

Derived:

Annual Recurring Cost =
(Licence_Cost_per_Agent * Total_Agents * 12)

One-Time Cost =
Implementation + Change_Management

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FINANCIAL STRUCTURE

Total Annual Savings =
Sum of:

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

Annualisation must remain explicit.

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DEFAULT ASSUMPTIONS (Editable)

• AHT Reduction: 7%
• FCR Improvement: +4 percentage points
• Training Reduction: 25%
• Supervisor Time Reduction: 8 hrs/month
• CSAT Uplift: +3 points
• Revenue Uplift per NPS Point: 0.25%
• Realisation Factor: 70%