How CPOs Can Increase Revenue 35% with Dynamic Pricing and Session Analytics

Why Flat-Rate Pricing Limits CPO Revenue

The majority of public EV charging stations in India operate on flat per-unit rates, typically between ₹12 and ₹22 per kWh depending on the operator and city. This pricing model is simple to administer but ignores two important economic realities: EV drivers show strong, predictable demand concentration during evening hours (6-10 PM) and morning commute windows, and a significant portion of charging sessions occur during off-peak hours when chargers sit idle. A CPO running 10 DC fast chargers at ₹16/kWh with a 35% utilisation rate earns approximately ₹1.4 lakh per month. The same network with dynamic pricing adjusting rates to ₹20/kWh during peak hours (7-10 PM) and ₹11/kWh during off-peak hours (11 PM to 6 AM) can achieve 45-55% utilisation and monthly revenue of ₹1.8-2.1 lakh, a 30-50% improvement with no new capital spending.

Dynamic Pricing Models for Indian CPOs

• check_circleTime-of-use pricing: Different rates by time block, typically 3-4 blocks per day. Example: ₹11/kWh from 11 PM to 6 AM, ₹15/kWh from 6 AM to 6 PM, ₹20/kWh from 6 PM to 11 PM. This is the most common dynamic model and the easiest to configure on OCPP 2.0 chargers
• check_circleDemand-based pricing: Rate adjusts in real time based on current station occupancy. When fewer than 30% of chargers are occupied, the rate drops to a base level; when occupancy exceeds 70%, the rate steps up by ₹3-5/kWh. This requires OCPP 2.0 and a central management system
• check_circleUser-tier pricing: Registered members or fleet operators receive contracted rates while walk-in users pay a standard rate. Common among corporate campus CPOs and fleet depot operators. Requires a customer account system integrated with the charging management platform

Session Analytics: Utilisation, Duration, and Peak Hour Mapping

Session analytics is the prerequisite for any pricing strategy. Without granular data on when chargers are used, for how long, and at what power levels, pricing decisions are based on guesswork rather than actual demand patterns. Key metrics every CPO should track on a weekly basis:

• check_circleUtilisation rate: Total energy delivered divided by theoretical maximum energy at rated power. Target is above 40% for DC fast chargers; below 25% indicates pricing or location issues that need investigation
• check_circleAverage session duration: Median time a vehicle is connected per session. Duration above 90 minutes on a 60 kW charger indicates price-insensitive users comfortable with long dwell times, a segment that can absorb higher rates
• check_circlePeak hour demand distribution: Percentage of sessions starting in each hour block. This drives time-of-use pricing decisions and helps identify understaffed or undersupplied windows
• check_circleSession abandonment rate: Percentage of sessions where a user initiates but disconnects within 5 minutes. High abandonment during peak hours indicates pricing friction or connector type mismatch
• check_circleRevenue per charger per day: Target benchmark for a 60 kW DC fast charger in a metro location is ₹2,000-₹3,500 per day at 40-55% utilisation

Revenue per Charger Benchmarks for Indian CPOs in 2026

Revenue benchmarks vary significantly by charger type, location tier, and pricing model. DC fast chargers (60-120 kW) in metro locations with good utilisation generate ₹2,000-₹3,500 per charger per day; in Tier-2 cities, the range is ₹800-₹1,500. AC Type 2 chargers (7-22 kW) typically generate ₹300-₹600 per charger per day in most markets. Highway corridor chargers show strong weekend peaks and weekday troughs, making time-of-use pricing particularly effective. CPOs who have implemented session analytics report that their top 30% of chargers generate 60% of total revenue, while their bottom 20% often operate below the electricity cost threshold. Identifying and repricing or relocating underperforming assets is as important as optimising high-demand sites.

Setting Up Dynamic Pricing Within CERC and SERC Tariff Guidelines

Dynamic pricing does not give CPOs unlimited freedom to set rates. CERC and the relevant SERC in each state set tariff frameworks that cap the retail rate CPOs can charge consumers for electricity. Most states specify a maximum CPO retail tariff in the range of ₹22-₹28 per kWh for FY2026. Within this ceiling, CPOs are free to apply time-of-use or demand-based pricing. The practical steps for compliant dynamic pricing are:

• check_circleConfirm the maximum retail tariff ceiling with your DISCOM or SERC for your state and category of service connection
• check_circleSet your peak rates below the ceiling, typically leaving a ₹2-₹3 buffer to allow for mid-year tariff revisions
• check_circlePublish your pricing schedule on the charging station display and in the app, as required by the Ministry of Power Consumer Interface Guidelines
• check_circleLog every rate change event with a timestamp in the charging management system; DISCOM inspectors may request rate change records during compliance audits
• check_circleFor multi-state networks, maintain a separate rate configuration for each state to reflect different SERC tariff ceilings

ROI Case Study: 20-Charger Network Increasing Revenue by ₹1.8 Lakh Per Month

The case illustrates three principles that apply broadly to Indian CPO networks. First, attracting fleet operators with contracted off-peak rates fills dead hours without reducing peak-hour revenue from standard users. Second, corporate campus locations have two distinct peak windows, morning (8-10 AM) and evening (5-9 PM), which both support premium rates. Third, session data from the first month of dynamic pricing reveals which specific chargers are underperforming and allows targeted actions such as marketing to nearby businesses, app notification campaigns, or further rate adjustments.

How URGAA and KAILASH-AI Implement Automated Dynamic Pricing

Go4Garage's URGAA platform includes a pricing engine that connects to the operator's charging management system via OCPP 2.0 and updates tariff schedules automatically based on configurable rules. Operators define their pricing tiers, time blocks, and occupancy thresholds once; the system applies them across all connected chargers and logs every rate change event for compliance audit purposes. KAILASH-AI, Go4Garage's revenue analytics module, analyses session data to generate weekly pricing recommendations, identifying peak hours where the current rate may be below the demand-justified ceiling and off-peak windows where lower rates would attract incremental volume. The combined system has been deployed at 14 CPO networks across India, with operators reporting an average 28% revenue increase in the first quarter after deployment.