A common way to review a charging network is to rank sites by revenue and look at the bottom of the list. That list mixes two very different problems.

Some sites earn little because not enough drivers come. These are demand-constrained. The fix is pricing, visibility, partnerships – or, eventually, exit.

Others earn little for their size because drivers come, find every charge point busy, and leave. These are capacity-constrained. They are turning revenue away, and the fix is more charge points, not less investment.

A revenue ranking can’t tell them apart. Utilisation together with session patterns – how often a site is fully occupied, and at which hours – can. Classify first, then decide.

One more trap: sites with nearby competitors often show higher utilisation, not lower. That’s selection, not cause – operators build where demand already is. Read competitor pressure alongside the site’s own demand, not as a verdict on it.

I built a portfolio Power BI model around this classification, on synthetic data. Happy to walk through it.

Question

Why are some of our charging sites underperforming – and what should we do about each one?

Terminology

Utilisation – the share of available time a charge point is actually in use.
Demand-constrained site – earns little because not enough drivers come.
Capacity-constrained site – drivers come, find every charge point busy, and leave.

Context

A common way to review a charging network is to rank sites by revenue and look at the bottom of the list. That list mixes two very different problems.

Experience

I’ve spent the last six years in EV charging infrastructure at SWARCO and E.ON, including building a utilisation model across ten markets. Site performance data is where most network decisions start – and where they most often go wrong.

Observations

Demand-constrained sites need pricing, visibility, partnerships – or, eventually, exit. Capacity-constrained sites are turning revenue away, and the fix is more charge points, not less investment. A revenue ranking can’t tell them apart.

One more trap: sites with nearby competitors often show higher utilisation, not lower. That’s selection, not cause – operators build where demand already is.

Research

I built a portfolio Power BI model around this classification, on synthetic data: every site classified by utilisation and how often it is fully occupied, and at which hours. Happy to walk through it.

Focus In On: Responsible for Data / CDO

New Areas of Value:

Trusted, data-driven decision making supported by evidence

Improvements around:

Low level of data literacy across the organisation

Difficulty combining multiple sources for one version of the truth

Relevant Business Perspectives

Relevant Industries