Charging session analytics is the process of collecting, measuring, and interpreting data from individual EV charging sessions to improve utilization, revenue, uptime, and user experience. It turns raw session records (who charged, when, how much, and at what price) into actionable insights for CPOs (charge point operators), site hosts, fleet managers, and operations teams.
What Is Charging Session Analytics?
A charging session is a single charging event from start to stop (including authorization, energy delivery, and billing). Charging session analytics aggregates these events to answer questions such as:
– How many sessions happen per charger, per site, and per day?
– How much energy (kWh) is delivered and at what average price?
– What is the average session duration and dwell time (time connected vs time charging)?
– Which chargers or locations underperform, and why?
Session analytics is usually powered by data from the CPMS (Charge Point Management System), payment systems, and roaming platforms.
Why Charging Session Analytics Matters
Session-level data is the most reliable source for improving charging performance because it shows real behavior—not assumptions. It helps with:
– Increasing charger utilization rate and throughput
– Optimizing pricing and charging revenue models
– Reducing faults and improving uptime through diagnostics patterns
– Detecting fraud, failed payments, and abnormal session behavior
– Improving customer experience by reducing start failures and confusion
For multi-market operators, session analytics also supports consistent benchmarking across different tariff structures and regulations.
Core Data Captured in Charging Sessions
A well-instrumented session record includes:
– Charger ID, connector ID, site ID
– Session start/end timestamps
– Authorization method (RFID, app, Plug & Charge)
– Energy delivered (kWh)
– Power profile over time (optional, but valuable)
– Tariff applied (€/kWh, time fee, session fee, hybrid)
– Total cost charged, taxes, and payment status
– Roaming vs direct session flag (charging roaming)
– Stop reason (user stop, EV stop, fault, timeout, emergency stop)
Key KPIs Used in Charging Session Analytics
Session analytics often revolves around these performance indicators:
– Sessions per charger per day (transaction volume)
– kWh per session (average session size)
– kWh per charger per day / month (energy throughput)
– Average session duration (minutes connected)
– Active charging time vs idle time (bay blocking insight)
– Start success rate (attempts vs successful starts)
– Revenue per session and margin per session
– Peak hours and time-of-day demand curves
– Repeat user rate (customer retention indicator)
How Session Analytics Improves Revenue and ROI
Charging revenue is ultimately built session by session. Analytics helps operators:
– Identify which sites have high traffic but low conversion (pricing or UX friction)
– Adjust tariffs using demand patterns and demand-based pricing
– Add idle fees or parking integration where dwell time blocks capacity
– Increase direct-channel usage to reduce roaming fees and improve margin
– Predict revenue using stable KPIs like sessions/day and kWh/session
This directly supports charging ROI by improving both top-line revenue and cost control.
Operational Use Cases: Reliability and Uptime
Session anomalies often reveal technical or configuration issues early:
– Frequent “start–stop” micro-sessions can indicate connector issues or unstable power
– High authorization failures may signal backend token sync problems
– Repeated faults at specific times may indicate grid instability or thermal limits
– Large gaps in sessions can indicate downtime, vandalism, or poor discoverability
When combined with diagnostics, session analytics becomes a tool for proactive maintenance and faster service response.
Roaming vs Direct Sessions Analysis
Separating roaming and direct sessions is critical because they behave differently:
– Roaming sessions may have higher price to the driver but lower margin to the CPO
– Start failures can be higher if roaming token handling is inconsistent
– Customer ownership differs (billing and support handled by the eMSP)
Analytics typically tracks:
– Roaming session share (% of total)
– Margin impact of roaming fees
– Start success rate by channel (roaming vs direct)
Common Pitfalls in Charging Session Analytics
– Looking only at energy delivered and ignoring start failures and idle time
– Comparing sites without normalizing by connectors, hours open, or local pricing rules
– Mixing time-based and €/kWh pricing without a common benchmark metric
– Not tagging sessions by channel (direct vs roaming) or customer type (fleet vs public)
– Treating revenue as profit without subtracting electricity cost and platform fees
Related Glossary Terms
Charging Session Revenue
Charging Revenue Analytics
Charging Revenue Models
Charging ROI
Charger Utilization Rate
Uptime
Demand-Based Pricing
Charging Roaming
CPMS (Charge Point Management System)
OCPP