Author: Tom Nutley, Chief Revenue Officer, Urban Sharing
Shared micromobility has grown rapidly in recent years as cities embrace sustainable transportation options. Key performance indicators (KPIs) and Service Level Agreements (SLAs) serve as the foundation for measuring success in these systems, guiding decisions by municipalities, operators and users. While KPIs can provide invaluable insights, they can also lead to unintended consequences if poorly designed or inflexible. This piece examines the good, the bad, and the ugly aspects of shared micromobility KPIs.
The Good: Flexible, Dynamic Metrics
Cities like Washington, D.C., and Budapest provide strong examples of how adaptable, performance-based KPIs can drive better outcomes for both users and operators.
Washington, D.C.’s approach to regulating micromobility has been praised for its flexibility. The city uses dynamic fleet caps, allowing the number of vehicles to adjust according to real-time demand and performance. Instead of rigid fleet limits, operators can grow their fleet size based on metrics like average daily trips per vehicle and overall service quality. This model ensures that demand is met without overwhelming the streets or leaving areas underserved, encouraging operators to continuously improve service delivery.
Budapest’s reported tender is also set to use micro-incentives to boost operator performance. By rewarding companies for hitting or exceeding specific KPIs, such as improved fleet availability, fleet deployment locations or reduced vehicle downtime, the city encourages innovation and competition. This results in higher service quality for users, while operators benefit from the incentives tied to exceeding these benchmarks.
One of the best practices in KPI design is incorporating rolling averages to account for seasonality, particularly in cities with significant weather fluctuations. Without adjusting for seasonal demand changes, rigid KPIs can mislead city officials into thinking there’s underperformance during off-peak months. Using rolling averages that account for seasonality provides a more accurate long-term view of system health and helps both cities and operators make smarter decisions.
The Bad: The Misuse of Availability & Rigid Requirements
While some KPIs are designed to foster better outcomes, others can lead to operational inefficiencies. A clear example of this is the common reliance on availability as a core metric for shared micromobility.
At first glance, availability – measuring the percentage of time vehicles or stations are in operation – seems like a logical way to track service reliability. However, it can be a flawed metric when deployed without considering the broader context. Availability causes operators to serve the few rather than the many. For example, companies have to distribute vehicles to areas of low utilisation rather than distributing to areas with high-demand which further feeds the supply to demand struggle. Utilising usability instead, to take into account an area of 250-500m that averages the availability, ensures that all system stakeholders get the best outcome.
Another problematic KPI involves ridership requirements. Cities often impose strict quotas for the number of vehicles in operation or the number of trips made, even when these metrics don’t align with real-world conditions. Operators may be forced to keep a high number of vehicles available, even if demand doesn’t justify it or many of the vehicles are in disrepair. The focus shifts from providing quality service to meeting arbitrary benchmarks, often at the expense of user experience.
The Ugly: Reporting Challenges and Trust Issues
While many shared micromobility KPIs are well-intentioned, the misalignment between metrics and real-world conditions can lead to ugly operational consequences, as seen in the Beam example. Underreporting fleet numbers is an unusual tactic in an industry where operators typically face pressure to overreport to meet city mandates, particularly when they are dealing with vandalism, theft, or other maintenance issues. This unusual case sheds light on the broader challenge of transparency in KPI reporting and the potential conflicts it can create between cities and operators.
In most cases, operators are incentivized to report higher fleet numbers or trip counts to meet regulatory demands. When cities set overly ambitious or rigid KPI targets – such as a minimum number of operational vehicles or trips per day – operators may feel compelled to fudge their reports. This creates an atmosphere of distrust between cities and operators, undermining the collaborative effort needed to run a successful shared micromobility system.
The Beam situation also underscores the issue of transparency in the face of operational difficulties. Rather than incentivizing operators to be honest about their challenges – such as vehicle vandalism, theft, or service gaps – strict KPI requirements can push companies to misrepresent their data to avoid penalties or fleet reductions. This lack of alignment between cities’ goals and operator realities can lead to poor service outcomes for users, who may find themselves without access to enough vehicles, or worse, vehicles that are not fit for use.
A more effective approach, seen in cities like Washington, D.C., is to implement dynamic metrics that allow fleet sizes to expand or contract based on actual performance. This model encourages operators to be transparent about their fleet management without the fear of penalties for failing to meet arbitrary numbers. Instead, operators can focus on improving service quality and user satisfaction, knowing that their fleet size and service scope will adjust based on real demand and performance metrics.
Conclusion: Flexibility is the Key to Success
Many of the issues with shared micromobility KPIs come down to a lack of flexibility. Fixed metrics might provide initial structure, but they often lead to unintended consequences, including poor service quality and lack of transparency. As we’ve seen, the most effective KPIs are dynamic and adaptable, allowing cities and operators to respond to real-time demand and operational challenges like seasonality.
Ultimately, success in micromobility depends on aligning city objectives with user outcomes. When KPIs create conflicting priorities, they can hinder rather than help operators. Instead of focusing on arbitrary fleet sizes or availability metrics, cities should prioritise ensuring that vehicles are in the right locations, fully operational, and accessible to users when and where they need them. A simple, flexible, and performance-based approach is often the most effective way to ensure that shared micromobility services can thrive.
By fostering transparency, building trust between cities and operators, and focusing on user experience, the micromobility sector can move toward a future where KPIs genuinely serve everyone involved. Keeping it simple and adaptable is the key to making that future a reality.
