Why winning Charge Point Operators are putting utilisation data at the core of their strategy
Who’d be a Charge Point Operator (CPO)? As UK transport accelerates toward a low-emission future, CPOs must scale networks, anticipate demand, balance regional discrepancies and leverage grid capacity, all while providing a seamless customer experience and working towards profitability. While past years focused on scaling at speed, today’s landscape demands something different. The central challenge is no longer building infrastructure, but achieving strategic precision when deploying it.
With CPOs working towards profitability, and consolidation likely on the horizon, today’s decisions will shape long-term competitiveness. Against this backdrop, utilisation data provides a dynamic picture of charge point usage across locations and customer segments. For CPOs, this data reveals more than just frequency or duration; it illuminates customer behaviour patterns.
In a recent conversation with Fastned, the European ultra-rapid charging network, we heard about two sites with identical energy throughput but very different stories. A London site with an improbable sounding 60% utilisation reflected steady, round-the-clock demand from urban taxi fleets. In contrast, a Dutch site reporting a more moderate 25% utilisation operated with twice as many chargers and served users at a consistent but lower intensity through daytime hours. A site with eight chargers reporting 25% utilisation might signal that two of those chargers are in almost constant use, or that all are full during peak hours, leading to queues and customer drop-off, requiring radically different operational responses. Without such insights, operators risk misallocating resources, either by overbuilding in low-demand areas or missing critical opportunities in high-demand zones.
With millions of charges taking place each month, the industry must ensure its data is accurate, complete, and consistent. Robust data collection hinges on comprehensive aggregation across multiple CPOs and data providers, ensuring a holistic and unbiased view. Transparency and collaboration within the industry help everyone progress; sharing metrics while respecting commercial sensitivities can seem threatening in fledgling markets but is the norm elsewhere. Data granularity is equally essential; going beyond simple percentages to analyse session-level details such as simultaneous usage, state of charge on arrival and departure, and charging speed distribution.
At Zapmap Insights we’re advancing live data aggregation by collating, cleansing, standardising and segmenting these data feeds from diverse operators on our platform. Constant feedback loops and input from end-users ensure continuous quality improvement cycles, resulting in current and historical performance insights, delivered in standardised formats with flexible delivery, creating the foundation for more robust, real-time intelligence.
The right chargers in the right locations
Location types such as urban hubs, motorway service areas, retail locations, and residential streets serve distinct use cases with varied utilisation profiles. Slow, on-street charge points may be in use for longer but see a low number of sessions, while motorway sites see more transient, time-limited use. Understanding these patterns ensures network growth is not only technically sound but commercially viable and user-led.
Combining utilisation data with additional sources such as demographics, housing stock, traffic flows, and grid capacity maps yields even richer planning insights. These datasets help identify true demand centres, avoiding overbuild in underused areas while recognising the latent potential of emerging corridors. This approach also supports collaboration with Local Authorities, crucial to effective infrastructure planning and equitable access by balancing ultra-rapid hubs with on-street and destination chargers.
From performance metric to strategic asset
When combined with predictive analytics, utilisation data becomes a powerful, forward-looking tool. Grid capacity and constraints data enable CPOs to forecast site expansion feasibility, while traffic flow data helps predict demand. Customer behaviour analytics inform design improvements and targeted service offerings. Utilisation combined with pricing data enables intelligent demand shaping and dynamic pricing strategies. Integrating real-time data from sources like charger telemetry and customer feedback improves the accuracy of demand forecasts. This data-driven approach supports dynamic strategies such as load balancing and pricing incentives, which reduce grid stress, guide investment, and protect profitability.
The EV charging industry already generates vast amounts of data. Those who leverage it fully to optimise their networks in a smart and capital-efficient way will shape the future of EV infrastructure. In a market where capital is finite and expectations are rising, investing in data is a critical investment in resilience.
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