What a peer benchmark shows that your own ETF data cannot
Every retail platform has detailed data on its own clients. What it lacks is a reference point: is the pattern it sees normal for the market, early, or unusual? This note explains what a standardised peer benchmark adds, and where it needs care.
The problem with looking only inward
Suppose a platform sees more clients holding a particular ETF each month, or a rising share of clients setting up savings plans. Those are useful facts, but they raise questions the platform's own data cannot answer. Is the whole market moving the same way? Is the platform ahead of its peers or behind them? Is a short holding period a feature of this product everywhere, or of this platform's clients?
Four lenses a benchmark can offer
- Product adoption. How widely a product is held across platforms, and whether adoption on yours is above or below the group.
- Savings plans. How much recurring investment goes into a product, and how your platform's savings-plan activity compares.
- Holding behaviour. How long clients keep a product and how often they switch, set against what peers see.
- Retention and flows. Whether money comes in and stays, relative to the wider group.
The aim is context for the platform's own judgement, not a score or a league table.
What needs care
- Like for like. Platforms differ in size, client type and product shelf. A benchmark is only useful if every contributor's data is aligned to one schema with agreed definitions.
- Anonymity. Data should be aggregated and anonymised, with no personal data or client-level records, and no platform able to see another's figures.
- Interpretation. A gap against the peer group is a prompt to ask why, not a conclusion in itself.
How platforms can use it
- Product shelf and range reviews, informed by how products are used across the market.
- Conversations with ETF issuers, backed by independent evidence.
- Records for product governance and value-for-money reviews. No rule currently requires usage analytics, but they can help a firm evidence its process. See our white paper on the EU Retail Investment Strategy.
- Planning what to feature next, based on where demand is building.
Contribute to the dataset
ETF Explorers aggregates anonymised, ISIN-level ETF usage data from European retail platforms and standardises it into one benchmark. The benchmark sharpens with each platform that joins. Get in touch to find out more.