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Problem Statement

Business Context

MobilityCorp operates a multi-modal last-mile transportation platform across multiple cities in the EU providing:

  • Electric scooters and eBikes (short-term, flexible rentals)
  • Electric cars and vans (advance bookings, fixed durations)

The company faces critical operational and customer experience challenges that threaten growth, profitability, and competitive positioning.

Core Business Problems

Problem 1: Demand-Supply Mismatch (Vehicle Imbalance)

  • Symptom: Customers cannot find vehicles in high-demand locations while excess inventory sits idle in low-demand areas.
  • Impact:
    • Lost revenue from unfulfilled bookings (~15-25% potential bookings)
    • Poor customer experience leading to churn
    • Increased manual relocation costs (€10-15 per move)
  • Root Cause: Static pricing and reactive (not predictive) fleet positioning.

Problem 2: Battery Management and Vehicle Availability

  • Symptom: Vehicles run out of charge mid-operation or sit unused due to dead batteries.
  • Impact:
    • Reduced fleet utilization (vehicles unavailable 20-30% of time)
    • Emergency manual interventions required
    • Customer frustration with unreliable availability
  • Root Cause: No predictive prioritization for battery swaps; staff visit locations randomly or by schedule, not by urgency.

Problem 3: Low Customer Engagement and Retention

  • Symptom: Most customers use the service ad-hoc; few rely on MobilityCorp for regular commutes.
  • Impact:
    • Low customer lifetime value (CLV)
    • High customer acquisition costs relative to retention
    • Unpredictable revenue patterns
  • Root Cause: No personalization, incentives, or proactive engagement to build habitual usage.

Problem 4: Operational Inefficiency

  • Symptom: Staff lack real-time intelligence on where to focus efforts (battery swaps, rebalancing, maintenance).
  • Impact:
    • Wasted staff time visiting low-priority locations
    • Delayed response to critical vehicle issues
    • Higher operational costs per vehicle
  • Root Cause: No AI-driven prioritization or predictive maintenance.

Technical Challenges

AI/ML Immaturity

  • No demand forecasting or predictive maintenance models in production.
  • Manual decision-making for pricing, rebalancing, and maintenance.
  • No infrastructure for model training, deployment, or monitoring.

Observability Gaps

  • Limited visibility into system health, AI model performance, or operational metrics.
  • No distributed tracing for debugging microservices.
  • Reactive incident response instead of proactive monitoring.