EU online shopping statistics 2024: 77% of internet users bought online (Eurostat)

Eurostat’s annual ICT survey shows that 77% of EU internet users (age 16–74) bought or ordered goods or services online in 2024. That’s up from 59% in 2014 — a +17 percentage point increase over a decade.

This Research Brief summarizes the headline numbers, highlights the strongest and weakest markets in the EU, and explains why these stats matter for pricing intelligence and competitor monitoring.

Key findings (with numbers)

• 77% of EU internet users bought online in 2024 (age 16–74).
• 59% → 77% (2014 to 2024): +17 pp growth over 10 years.
• Top e-shopping penetration in 2024: Ireland (96%), Netherlands (94%), Denmark (91%).
• Lowest in 2024: Bulgaria (57%), then Italy (60%) and Romania (60%).
• By age (2024): 25–34: 89%, 35–44: 86%, 16–24: 83%, 45–54: 78%, 55–64: 67%, 65–74: 53%.

What exactly does “bought online” mean here?

Eurostat defines e-commerce here as the purchase of goods or services over the internet for private use, via any device, from enterprises or private individuals. “Purchases” include orders where payment is required — but payment doesn’t have to be online.

This matters because it captures real demand for online buying behavior, not just “visiting online shops.”

EU leaders and laggards: why country penetration matters

If you sell (or track competitors) across borders, this stat is a quick proxy for:

• how “normalized” online shopping is in a market
• how large the online “audience” is for e-commerce campaigns
• how intense digital price competition tends to be

High-penetration markets like Ireland and the Netherlands often behave differently: more comparison shopping, stronger expectations around delivery/returns, and faster promotion cycles.

Age groups: who drives EU e-commerce demand?

Eurostat’s data shows the strongest online-buyer penetration sits in prime spending ages:

• 25–34 (89%) and 35–44 (86%) are the highest.
• Even 65–74 is substantial at 53%, meaning “older online buyers” are no longer a niche.

For pricing and competitive intelligence, age mix shapes category dynamics:

• fashion and consumer goods often over-index in younger cohorts
• household + health categories can rise with older cohorts

Why this matters for Trackabl users

If you’re building a price strategy or monitoring competitors, this macro signal helps you:

1. Prioritize markets for competitor tracking (start where adoption is highest).
2. Set realistic expectations for demand and price sensitivity by country.
3. Benchmark: if you’re underperforming in a high-penetration market, the issue is usually not “market readiness” — it’s offer, pricing, or competitor pressure.

Dataset links you can use (official)

• Eurostat Statistics Explained (full explainer + charts):
  https://ec.europa.eu/eurostat/statistics-explained/index.php?title=E-commerce_statistics_for_individuals

• Eurostat News (short summary, published 20 Feb 2025):
  https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20250220-3

• Eurostat Data Browser (table: online buyers, last 12 months):
  https://ec.europa.eu/eurostat/databrowser/view/isoc_ec_ib20/default/table?lang=en

• Eurostat Data Browser (historical series):
  https://ec.europa.eu/eurostat/databrowser/view/isoc_ec_ibuy/default/table?lang=en

FAQ

Is the 77% “of all people in the EU”?

No. It’s 77% of internet users (among individuals aged 16–74 who used the internet in the last 12 months).

Why do two different pages show the same number?

Eurostat publishes both:

• a Statistics Explained article (long-form explainer with tables/definitions), and
• a News article (short highlight summary).
  They reference the same underlying ICT survey results.

Can I use this data commercially in my own reports?

Eurostat data is generally intended for reuse, but you should follow Eurostat’s reuse/citation guidance and cite the dataset + publication page. When in doubt, link directly to the official sources above.

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If you want, the next Research Brief can be Destatis (Germany): e-commerce turnover, online retail share, and category splits — with direct dataset links and quoted definitions.