Case Study: E-commerce Platform Modernization

Project Type: Legacy System Modernization
Industry: Retail/E-commerce
Duration: 7 months (initial estimate: 5 months)
Team Size: 3 developers (mix of senior and junior), 1 QA, 1 DevOps, 1 PM (total: 6) Technology: ASP.NET Core, Microsoft SQL, Azure

Table of contents
  1. Project Overview
    1. Business Context
    2. Objectives
    3. Constraints
  2. Initial Estimation Challenges
    1. Unknown Factors
    2. Stakeholder Expectations
  3. Estimation Approach
    1. Phase 1: Discovery and Analysis (Month 1)
      1. Activities:
      2. Initial Estimates (Pre-Discovery):
      3. Revised Estimates (Post-Discovery):
    2. Phase 2: Detailed Estimation by Component
      1. Work Breakdown Structure
      2. Estimation Techniques Used
    3. Risk Assessment and Contingency
  4. Total Contingency Applied: 20%
  5. Actual Implementation Results
    1. Timeline Comparison
    2. Effort Comparison
    3. Cost Impact
  6. Root Cause Analysis
    1. Why Estimates Were Exceeded
    2. Contributing Factors
  7. Lessons Learned
    1. Estimation Improvements
    2. What We Got Right
    3. What We Would Do Differently
  8. Key Insights for Future Projects
    1. Universal Lessons
    2. Industry-Specific Insights
    3. Estimation Model Recommendations
  9. Retrospective Metrics
    1. Success Criteria Achievement
    2. Business Impact

Project Overview

Business Context

This case study describes a small-sized company (startup) needing to modernize their 5-year-old e-commerce platform for regulatory compliance. The project was driven by the need to meet new e-invoicing requirements and improve business agility. The existing system was struggling with:

  • Poor mobile experience
  • Slow page load times
  • Difficulty adding new features
  • High maintenance costs
  • Scalability limitations

Objectives

  • Migrate to modern technology stack (ASP.NET Core)
  • Improve performance and user experience
  • Enable mobile-first design
  • Reduce operational costs
  • Prepare for future growth

Constraints

  • Budget: $10k approved (strict budget for compliance-driven modernization)
  • Timeline: Must complete before regulatory implementation date
  • Business Continuity: Zero downtime during migration
  • Team: Small team, mix of experienced and junior developers (interns)

Initial Estimation Challenges

Unknown Factors

  1. Legacy Code Quality: 400,000+ lines of undocumented code
  2. Data Migration Complexity: 5 years of customer and transaction data
  3. Third-party Integrations: 12 external services with varying API quality
  4. Business Rule Extraction: Complex pricing and promotion logic embedded in code
  5. Performance Requirements: No clear SLA definitions
  6. Regulatory Compliance: New e-invoicing and reporting requirements (details omitted for confidentiality)

Stakeholder Expectations

  • Business: “It’s just a technology upgrade, should be straightforward”
  • Marketing: Wanted additional features during migration
  • IT: Concerned about security and compliance requirements
  • Management: Pressure to minimize cost and timeline
  • Compliance: Regulatory deadline was a hard constraint

Estimation Approach

Phase 1: Discovery and Analysis (Month 1)

Objective: Gather sufficient information for accurate estimation

Activities:

  1. Legacy System Analysis
    • Code complexity analysis using static analysis tools
    • Database schema documentation
    • Performance baseline measurements
    • Security audit of existing system
  2. Stakeholder Interviews
    • Business process mapping
    • Feature priority ranking
    • Non-functional requirements gathering
    • Risk tolerance assessment
    • Compliance requirements clarification
  3. Technical Spike
    • Proof of concept for critical integrations
    • Performance testing of new architecture
    • Data migration feasibility study
    • Prototype for regulatory reporting

Initial Estimates (Pre-Discovery): 

Total Effort: 1,200 hours (6 people x 5 months x 40 hours/week)
Duration: 5 months
Cost: $10,000

Revised Estimates (Post-Discovery): 

Total Effort: 1,440 hours (6 people x 6 months x 40 hours/week)
Duration: 6 months
Cost: $10,000

Phase 2: Detailed Estimation by Component

Work Breakdown Structure

1. Platform Migration (40% of effort)

  • User Interface modernization
  • Business logic refactoring
  • Database schema optimization
  • API development

2. Data Migration (25% of effort)

  • Data cleansing and validation
  • Migration scripts development
  • Testing and verification
  • Rollback procedures

3. Integration Development (20% of effort)

  • Payment gateway modernization
  • Inventory system integration
  • CRM system connectivity
  • Analytics platform integration

4. Testing and Quality Assurance (15% of effort)

  • Automated testing framework
  • Performance testing
  • Security testing
  • User acceptance testing

Estimation Techniques Used

1. Analogy-Based Estimation

  • Compared with similar modernization projects
  • Adjusted for technology differences and team experience

2. Function Point Analysis

  • Identified 847 function points in existing system
  • Applied productivity rates: 8-12 hours per function point

3. Expert Judgment

  • Technical leads estimated complex components
  • External consultant validation

4. Three-Point Estimation

  • Applied to high-uncertainty components
  • Used PERT formula for expected values

Risk Assessment and Contingency

| Risk Category | Probability | Impact | Mitigation | Contingency | |—————|————-|——–|————|————-|

Total Contingency Applied: 35% | Data migration complexity | High | High | Essential testing, manual validation | +10% buffer | | Third-party API changes | Medium | Medium | Simple adapters, limited scope | +5% buffer | | Performance issues | Medium | Medium | Prioritized optimizations | +5% buffer | | Scope creep | Low | Low | Strict change control | +0% buffer | | Team productivity | Medium | Medium | Focused onboarding | +0% buffer |

Total Contingency Applied: 20%

Actual Implementation Results

Timeline Comparison

Phase Estimated Actual Variance
Discovery 1 month 1 month 0%
UI Migration 2 months 2.5 months +25%
Backend Migration 1 month 1.5 months +50%
Data Migration 1 month 1 month 0%
Integration 0.5 months 0.5 months 0%
Testing 0.5 months 0.5 months 0%
Total 6 months 7 months +17%

Effort Comparison

Category Estimated Hours Actual Hours Variance
Development 900 1,050 +17%
Testing 240 300 +25%
Data Migration 180 180 0%
Project Management 120 140 +17%
Total 1,440 1,670 +16%

Cost Impact

Item Estimated Actual Variance
Development Team $8,000 $8,000 0%
Infrastructure $1,000 $1,000 0%
Third-party Tools $1,000 $1,000 0%
Total $10,000 $10,000 0%

Root Cause Analysis

Why Estimates Were Exceeded

1. Data Migration Complexity (on target)

  • Managed: Data quality issues in legacy system were addressed early
  • Solution: Focused on essential data migration only, with manual validation

2. Integration Challenges (minor overruns)

  • Managed: API compatibility issues resolved with simple adapters
  • Solution: Limited scope to core integrations only

3. Performance Requirements (minor overruns)

  • Managed: Mobile performance and SEO improved incrementally
  • Solution: Prioritized critical optimizations within budget

4. Scope Evolution (tight control)

  • Limited: Only compliance-driven features and essential reporting added
  • Solution: Deferred non-essential enhancements to future phases

Contributing Factors

Technical Factors:

  • Legacy code quality worse than expected
  • Incomplete documentation
  • Hidden dependencies discovered late

Process Factors:

  • Insufficient discovery phase
  • Scope creep not well-controlled
  • Testing started too late in process

Human Factors:

  • Junior developers took longer to ramp up
  • Key team member left mid-project
  • Business stakeholders unavailable for decisions

Lessons Learned

Estimation Improvements

1. Keep Discovery Phase Focused and Short

  • Allocate only essential time (about 1 month) for discovery
  • Prioritize code review and compliance requirements
  • Use technical spikes only for highest-risk areas

2. Minimize Contingency for Strict-Budget Projects

  • Keep contingency low and tightly managed
  • Limit scope to compliance and essential features
  • Refine estimates only if major risks emerge

3. Data Migration: Focus on Essentials

  • Prototype only critical data migration tasks
  • Validate data quality manually if needed
  • Avoid over-engineering migration approach

4. Stakeholder Management is Critical

  • Establish clear change control process upfront
  • Hold regular but brief checkpoint reviews
  • Set expectations for minimal scope and budget discipline

What We Got Right

1. Architecture Decisions

  • Microservices approach enabled parallel development
  • API-first design reduced integration complexity
  • Cloud-native architecture improved scalability

2. Risk Management

  • Early identification of technical risks
  • Proactive communication with stakeholders
  • Iterative delivery reduced late-stage surprises

3. Team Structure

  • Dedicated data migration team
  • Embedding QA throughout development
  • DevOps engineer from project start

What We Would Do Differently

1. Estimation Process

  • Conduct longer technical discovery phase
  • Use multiple estimation techniques and average results
  • Include business process analysis in initial estimates

2. Project Execution

  • Start with data migration proof of concept
  • Implement continuous integration from day one
  • Establish performance baselines early

3. Stakeholder Management

  • More frequent steering committee updates
  • Better change impact communication
  • Clearer definition of “done” criteria

Key Insights for Future Projects

Universal Lessons

  1. Legacy projects always have surprises - plan accordingly
  2. Data migration is often the longest pole - give it special attention
  3. Scope creep is inevitable - build change management into process
  4. Team productivity varies significantly - use conservative estimates
  5. Stakeholder expectations must be actively managed - over-communicate

Industry-Specific Insights

E-commerce Projects:

  • Performance requirements often exceed initial assumptions
  • Business rules are more complex than they appear
  • Third-party integrations require extensive testing
  • User experience expectations have increased significantly

Legacy Modernization:

  • Code quality is usually worse than expected
  • Hidden dependencies surface during implementation
  • Business knowledge is often trapped in old code
  • Testing legacy functionality requires significant effort

Estimation Model Recommendations

For similar legacy modernization projects:

Base Estimation:

  • Use function point analysis for functional scope
  • Apply 12-15 hours per function point for modernization
  • Add 50% for data migration complexity
  • Include 25% for integration challenges

Risk Contingency:

  • Technical risks: 10%
  • Business risks: 5%
  • Team risks: 5%
  • Total recommended contingency: 20%

Retrospective Metrics

Success Criteria Achievement

Criterion Target Achieved Status
Performance improvement 30% faster 35% faster ✅ Exceeded
Mobile user experience 4.0+ rating 4.1 rating ✅ Achieved
Operational cost reduction 20% reduction 18% reduction ⚠️ Close
Zero downtime deployment 100% uptime 99.9% uptime ✅ Achieved
Budget adherence $10,000 $10,000 ✅ Met
Timeline adherence 6 months 7 months ⚠️ Slight over

Business Impact

Positive Outcomes:

  • 20% increase in mobile conversion rates
  • 30% reduction in customer support tickets
  • Enabled faster feature development (35% faster)
  • Improved SEO rankings (10% increase in organic traffic)

Lessons for Business Value:

  • Technical improvements delivered measurable business results
  • Strict budget discipline enabled compliance within constraints
  • Modern architecture set the foundation for future growth

This case study demonstrates the complexity of legacy modernization projects and the importance of comprehensive estimation approaches. While the project exceeded time and budget constraints, it delivered significant business value and established a foundation for future growth.