Dragon's Den: Exercise 3

The Opportunity

Implement an AI chatbot to handle routine customer inquiries across all channels (web, mobile app, SMS, social media).

The Business Case

Problem

• Customer service team handles 150,000 inquiries/year
• 60% are routine (order status, return policy, store hours)
• Average handling time: 8 minutes
• Cost per inquiry: $12
• Customer satisfaction declining due to wait times

Solution

• Deploy GPT-powered chatbot with RetailFlow knowledge base
• Handle 65% of routine inquiries automatically (conservative target)
• Escalate complex issues to human agents with context
• 24/7 availability, consistent responses

Investment Requirements

Total Cost: $450,000

• Platform license: $120,000/year
• Implementation & training: $180,000
• Integration with existing systems: $100,000
• Ongoing maintenance: $50,000/year

Timeline: 6 months to launch, 12 months to full adoption

Expected Returns

Cost Reduction (Year 1)

• Handle 58,500 inquiries automatically (150K × 60% routine × 65% automation)
• Labour savings: $702,000/year (58,500 × $12)
• Platform costs: -$170,000/year (license + maintenance)
• Net Year 1 benefit: $532,000
• One-time investment: $280,000 (implementation + integration)
• 3-Year cumulative benefit: $1.6M - $730K investment = $870K net
• ROI: 119% (3-year cumulative)
• Payback period: 10 months

Revenue Growth (Indirect)

• Faster response times → improved customer satisfaction
• Estimated 1-2% reduction in cart abandonment (conservative)
• Potential revenue impact: $10-15M × 1.5% = $150,000-225,000 (not included in ROI)

Risk Reduction

• Consistent answers reduce compliance risk
• 24/7 availability reduces SLA breaches

Strategic Positioning

• Match competitor capabilities (lagging currently)
• Foundation for future AI customer experience initiatives

AI Transformation Matrix Position

Quadrant: OPTIMIZE (Process + Incremental)

• Automates existing customer service process
• Doesn't fundamentally change the business model

Three Horizons Position

Horizon 1: Optimize current operations

• Quick payback
• Low risk
• Extends core business

Data Requirements

• Customer inquiry history (2 years)
• Product catalog and policies
• FAQs and knowledge base articles
• Data Readiness: HIGH (all data exists)

Risks & Mitigation

Risk 1: Customer frustration with bot limitations

• Mitigation: Easy escalation to humans, transparent about bot capabilities

Risk 2: Brand voice consistency

• Mitigation: Extensive training on brand guidelines, human oversight

Risk 3: Integration complexity

• Mitigation: Phased rollout, start with web channel only

Success Metrics

• 80% automation rate for routine inquiries
• Customer satisfaction score ≥ 4.0/5.0
• Reduction in average handling time by 40%
• 95% accuracy in responses

AI-Specific Evaluation Criteria

1. Data Readiness Score: 8/10

• ✅ 2 years of customer inquiry data (150K inquiries)
• ✅ Product catalog and policies well-documented
• ⚠️ Some inquiries lack proper categorisation
• ⚠️ Social media data quality needs improvement

2. Continuous Learning Plan: Batch Retraining (Quarterly)

• Retrain on new inquiries and resolutions
• A/B test responses for continuous improvement
• Ongoing cost: 30% of initial investment annually ($135K/year)

3. Accuracy & Risk Tolerance

• Risk level: Medium (customer-facing, not life-impacting)
• Target accuracy: 90-95% (matches requirement)
• Human oversight: All escalations reviewed by agents
• Failure cost: Customer frustration, potential sale lost

4. Explainability Requirements: Low-Medium

• Need to show customers "why" we're asking certain questions
• Agents need to understand chatbot logic for escalations
• Solution: Use retrieval-based + GPT hybrid (can show sources)

5. Ethical Risk Assessment: Low-Medium

• Bias risk: Could treat customer segments differently
• Mitigation: Test responses across customer demographics
• Monitoring: Track satisfaction scores by customer segment
• Budget add: +$25K for bias testing