Designed by npsBench
In 2025, customer experience measurement reached a new level of scale. Across the year, npsBench processed more than 215 million data calls originating from multiple public platforms, including Google, Yelp, TripAdvisor, and other large review ecosystems. These calls were not collected for volume alone. They formed the foundation of a structured, continuous analysis designed to understand how companies truly perform in the eyes of their customers.
This level of data coverage makes it possible to move beyond isolated scores and snapshots. It enables market-level insight.
From raw signals to structured intelligence
Each call represents a real interaction with a public data source. Reviews, ratings, timestamps, locations, categories, and contextual signals are captured through automated pipelines and synchronized in near real time.
At this scale, raw data is meaningless without structure.
npsBench applies normalization layers that align heterogeneous review formats into a consistent analytical model.
Language, platform bias, scoring systems, and temporal effects are adjusted so that comparisons remain valid across cities, countries, and industries.
The result is not a collection of opinions. It is a comparable, decision-ready dataset.
358,000 companies analyzed across global markets
From these 215 million calls, more than 358,000 companies were analyzed in 2025.
Each company is mapped within its competitive context. Location matters. Industry dynamics matter. Customer expectations vary dramatically between sectors, regions, and even neighborhoods. A raw NPS number without context rarely tells the full story.
By structuring data at company level, npsBench makes it possible to answer questions that traditional surveys cannot:
- How does a brand perform against local competitors, not just national averages?
- Is a score driven by operational excellence or by market conditions?
- Is performance improving relative to the industry, or only in isolation?
Scale allows these questions to be answered with confidence.
Coverage across 20 distinct industries
The analysis spans 20 different industries, each with its own customer logic.
Hospitality does not behave like retail. Professional services do not follow the same patterns as healthcare.
Education, tourism, restaurants, automotive services, and entertainment each carry unique expectations, review behavior, and sensitivity to friction.
This diversity is critical.
Because npsBench operates at scale, benchmarks are not generalized. They are industry-specific. A strong NPS in one sector may represent average performance in another. Understanding that difference is where data becomes strategy.
Why scale changes the quality of insights
When analysis is based on thousands of companies, insights tend to remain descriptive. When it is based on hundreds of thousands, patterns emerge.
At this scale, it becomes possible to:
- detect early shifts in customer sentiment at industry level,
- identify structural advantages and weaknesses by geography,
- isolate experience drivers that consistently move NPS up or down,
- distinguish temporary noise from durable performance trends.
More data does not automatically create better insight. But sufficient coverage makes weak assumptions visible. That is what allows benchmarks to be trusted.
From benchmarking to actionable industry reports
The outcome of this work is not a static scorecard.
The volume and diversity of data processed in 2025 enable deep industry reports that highlight:
- where leaders truly differentiate,
- which experience factors matter most by sector,
- how customer expectations evolve over time,
- and where underperformance is structural rather than accidental.
These reports are designed for operators, executives, and analysts who need to understand their market position, not just monitor a number.
A foundation built for long-term intelligence
Processing 215 million data calls in a single year is not an endpoint. It is infrastructure.
This scale ensures that insights remain stable, comparable, and resistant to short-term distortion. It also allows npsBench to expand coverage without sacrificing precision, whether by adding new industries, new regions, or new analytical layers.
In a competitive environment where customer loyalty is fragile, understanding relative performance is no longer optional. It is the baseline.
And scale is what makes that understanding possible.
