Apparel NPS Benchmarks

Last update: July 1, 2026

Next update: August 1, 2026

How to read the Apparel benchmark

This page compares Net Promoter Score performance across the Apparel industry. The latest benchmark is 27.1 for Jun 2026, giving operators a sector-specific baseline that is often more useful than a general market average.

Industry pages are ideal when customer expectations vary strongly by category. They show which countries and cities currently overperform in this sector and help teams set more realistic goals based on peer performance rather than generic NPS targets.

Spain currently leads the visible country-level benchmark set for Apparel at 28.8, while Des Moines, USA is one of the strongest city-level references at 37.0.

Key insights

  • Spain is currently one of the strongest countries for Apparel with a benchmark NPS of 28.8.
  • Des Moines (USA) is currently one of the top-performing cities in this industry with an NPS of 37.0.
  • Use the country comparison and top/bottom city tables below to identify where this industry is outperforming and where customer loyalty remains more fragile.

How to interpret this benchmark

  • Industry pages are the best way to set realistic expectations when the category behaves very differently from the overall market. Compare the sector baseline first, then validate where countries and cities sit around it.
  • If a country benchmark looks average but one or two cities are far ahead, the opportunity may be local execution rather than a structural industry issue.

Apparel

Apparel NPS (Overall)

Last Month27.2+0.10 pts
6 Months Ago27.10.00 pts
12 Months Ago27-0.10 pts

Apparel NPS Evolution (Overall)

Top markets this month for Apparel

  • Spain is currently benchmarked at 28.8 NPS.
  • USA is currently benchmarked at 28.1 NPS.
  • Italy is currently benchmarked at 27.5 NPS.

Biggest country movers this month for Apparel

  • France posts the strongest month-over-month gain at +0.2 pts.
  • Spain posts the weakest month-over-month change at -0.3 pts.

Country Comparison — Apparel

# Country NPS Δ vs 1M Δ vs 6M Δ vs 12M
1 Canada 24.3 -0.10 pts -0.50 pts +0.70 pts
2 France 25.7 +0.20 pts +0.20 pts n/a
3 Germany 26.5 +0.10 pts +0.10 pts n/a
4 Italy 27.5 +0.20 pts +0.70 pts n/a
5 Spain 28.8 -0.30 pts -0.50 pts n/a
6 USA 28.1 -0.10 pts -0.20 pts -0.60 pts

Top 5 Cities — Apparel

#CityNPS
1 Des Moines (USA) 37
2 Columbia (USA) 37
3 Des Moines (USA) 37
4 Columbia (USA) 37
5 Austin (USA) 35

Bottom 5 Cities — Apparel

#CityNPS
1 Dover (USA) 18
2 Dover (USA) 18
3 Leipzig (Germany) 19
4 Leipzig (Germany) 19
5 Dijon (France) 20

Continue exploring related benchmarks

Compare nearby geographies and sector pages to understand whether benchmark shifts come from the local market, the national context or the industry itself.

Spain benchmark
Top country benchmark
USA benchmark
Top country benchmark
Italy benchmark
Top country benchmark
Germany benchmark
Top country benchmark
Des Moines benchmark
Top city benchmark
Columbia benchmark
Top city benchmark

Frequently asked questions about Apparel

A good NPS in Apparel should be judged against current sector benchmarks, not against a generic cross-industry number. This page gives you that sector-specific reference point.

Customer expectations can vary widely from one industry to another. An industry benchmark tells you whether your score is strong relative to comparable businesses, even when overall country averages look different.

Use the industry benchmark as the sector baseline, then review the linked country and city pages to understand where local markets are outperforming or underperforming that baseline.

What does this report deliver?

The NPS Index Report gives you a clear view of customer loyalty across the markets that matter. Each month we compile Net Promoter Score benchmarks for key industries and break them down by country, region, and city. You see your space at a glance. Then you see your context. That combination turns NPS from a single number into a decision tool. Trends become visible. Seasonality is obvious. Outliers no longer trick you.

This report is built for operators and analysts who need to act. You get clean monthly snapshots, directional movement, and variance indicators that highlight where to dig. Want to know if your 3-point dip is noise or a real shift? You will know. Need to brief the team with one chart per market plus a short narrative? Done. Use the benchmarks to set realistic targets, size the gap to leaders, and test if new initiatives are moving the needle.

Data History

  • From January 2024: Data is available for Canada and the United States.
  • From September 2025: Data collection expands to include France, Spain, Italy and Germany.

Methodology

We aggregate public review data from platforms such as Google, TripAdvisor, and Yelp via a multi-source API, ingesting continuously and snapping everything to monthly windows. Listings are matched to real businesses using name, address, and category signals; duplicates and closed venues are removed to protect panel integrity.

Every review passes spam and anomaly checks that include burst detection, reviewer history scoring, language verification, and platform cross-validation. We run NLP to identify intent and experience dimensions, map native 0–10 recommendation answers directly when present, and otherwise infer promoter, passive, and detractor classes from text and rating context with calibrated thresholds. For each industry and geography, NPS is computed as promoters minus detractors, with Bayesian smoothing to stabilize low-volume samples and confidence ranges published alongside the point estimate.

We normalize categories to a unified industry taxonomy, align time zones, standardize language, and apply weighting so large markets do not overpower smaller ones within a region. Monthly values are reported as exact snapshots, and a rolling three-month view clarifies trend direction while preserving month-to-month comparability. We publish a monthly NPS only when minimum sample size and coverage criteria are met, with methods, thresholds, and known limitations disclosed in the report's technical notes.

Finally, models and pipelines are audited continuously with backtests and drift checks, and any detected bias or source change triggers a documented review and re-calibration.

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