Turn passenger feedback into the insight your network teams can act on
Every train journey generates feedback. Surveys from Qualtrics, complaints in the ticketing CRM, app reviews, social posts—arriving separately, faster than any team can manually categorise and theme. The result: the ORR reporting cycle arrives and weeks of analyst time goes into hand-coding comments that should have been structured months ago. Avanti West Coast freed 7,102 working days with a different approach.
Wordnerds turns what customers say into what organisations do. We integrate AI-powered insight from surveys, complaints, reviews and calls directly into Power BI where decisions happen — so everyone in the organisation can act on what customers are saying, not just the insight team.
Why is it so hard to make sense of what passengers are telling you?
Rail operators receive passenger feedback from surveys, CRM complaints, app reviews, social posts and contact-centre transcripts—all sitting in separate systems. A small insight team spends weeks before each ORR reporting deadline manually categorising what they can reach. The richest patterns stay buried in the channels no one had time to theme.
Your passengers take the time to tell you what went wrong, and what went right. The problem is volume, not intent. Surveys come from Qualtrics, complaints from the ticketing CRM, reviews from the app stores, commentary from social—and they sit in separate systems that never speak to each other. Before anyone can see a pattern, an analyst has to build a spreadsheet that was already out of date when they started.
The ORR reporting deadline arrives quarterly. The categories—delay attribution, complaint handling, accessibility—have to be consistent, traceable and defensible. Doing that by hand, differently each time, by whoever had capacity that week, is the problem that absorbs the most analytical time and produces the least defensible output.
What does passenger insight actually look like when it works?
Three things that are impossible when you're manually categorising feedback—and routine when you're not.
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GENERATE-IMAGES: pillar illustration 640×360px. Disparate passenger feedback channels (survey / complaint / social / app) converging into one unified stream; uniform house-style, warm brand palette
Every channel, in one view
Surveys, CRM complaints, app reviews, social posts and contact-centre transcripts in one analysis-ready stream—themed the same way, so you stop reconciling channels and start seeing the patterns across them. Avanti West Coast pulled together 1.3 million passenger voices across every source this way, freeing 7,102 working days for higher-value work.
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GENERATE-IMAGES: pillar illustration 640×360px. Automated classification engine routing passenger feedback into labelled ORR categories, with a visible trace/audit-trail cue; uniform house-style, brand palette
Automated, auditable classification
Passenger feedback is automatically classified into your ORR complaint categories—the same way each time, with every theme traced back to the verbatim behind it. No analyst variable. No black box. The kind of defensible, audit-ready output the ORR expects when it asks how you reached a category.
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GENERATE-IMAGES: pillar illustration 640×360px. Passenger insight landing in a route-level Power BI dashboard surface; evoke a dashboard in-use without faking Microsoft branding; uniform house-style
Route, station, depot—where the action is
Not just "reliability complaints are up 12% this quarter" but which services, which times of day, which stations drive the most complaints. Insight delivered into Microsoft Power BI, where your network and commercial teams already work—so the feedback reaches the people who can change something, not just the people who write the report.
When the ORR asks how you classify passenger complaints—you need a defensible answer
Wordnerds classifies passenger feedback against the ORR's quarterly complaint categories—automatically, consistently, with every theme traced back to the verbatim that produced it. The same classification logic runs in Q1 and Q4, so you can compare across periods and demonstrate trend for DfT reporting and Transport Focus submissions. Not just for the regulator—for the network planning teams who need the same insight to improve services.
When the ORR, DfT or a franchise review board asks "how did you categorise this?"—a general-purpose AI that runs differently every month cannot answer. The classification has to be transparent, reproducible and explainable: auditable AI where you can show your working, and where the categories hold stable over time so a trend line means something.
That is what makes the evidence useful beyond the compliance report. The same structured passenger insight that answers the ORR's quarterly categories also tells your operations team which routes are driving complaint volume and whether last quarter's intervention worked.
How does it work for a transport team?
Three steps—from scattered passenger feedback to structured insight in Power BI. The full method is one click away.
Unify every source
Send us your passenger feedback in whatever form it arrives—surveys, CRM complaints, app reviews, social exports, contact-centre transcripts. CSV to start, an API when you're ready.
Classify with your ORR framework
Sector-tuned AI classifies every comment against your ORR complaint categories—consistently, automatically, with every theme traceable to the verbatim behind it.
Deliver where decisions happen
Themed insight lands in Microsoft Power BI, filtered to route, station and depot level—so the network planning team sees the same data as the insight team, and can act without waiting for the next report.
What Avanti West Coast did with 1.3 million passenger voices
Avanti West Coast analysed 1.3 million passenger voices, freed 7,102 working days from manual categorisation, and released £1.35m in analyst capacity. The feedback that was previously read in samples was now read in full—classified, themed and delivered to the teams across the organisation who could act on it.
Before Wordnerds, Avanti's insight team manually categorised hundreds of passenger comments per reporting period. That work was accurate enough to answer an internal audience, but not at a scale or speed that could change what the organisation did operationally. When the categorisation became automated and consistent, the time that had gone into theming went into acting on the themes instead.
The shift was not just efficiency. It was that passenger insight—from surveys, complaints and app reviews—reached the frontline teams who shape the service, not just the report writers. That is the difference between a quarterly compliance report and passenger intelligence that changes Tuesday's timetable.
When the franchise review board asks what your passengers are actually saying, you have the answer—at route level, with the data behind every claim.
What other transport operators say
From the insight and CX teams running it day to day.
"By utilising the work that Wordnerds do, we have been able to add real depth to the validity of all our numbers. We are able to pinpoint, highlight and really hone in on the issues that matter to our customers and present the power of those issues to all stakeholders. As a result we have seen several major changes already to customer journeys based on stories that Wordnerds have helped us tell."
— Graeme Baldock, Resource Planning and Analysis Manager, Transport for Wales
"We have been able to use the specific insight we now have at our fingertips to support the case for focused investment and action, such as installing tables onto our trains and how we deploy our onboard cleaning teams. As a result, the sentiment score for the overall onboard environment has increased, providing evidence that our efforts have positively impacted customer perceptions."
— Neil Atkin, Head of CX, Greater Anglia
"Wordnerds gives us the detail we need from complaints data for truly actionable insights. We can now understand exactly what's happening in first class, take specific actions to stakeholder meetings, and provide key actions to service areas that actually move the needle on customer satisfaction."
— Louis Helm, Customer Relations Lead, TransPennine Express
Built for transport—and the sectors you'd expect from a serious VoC platform
Transport is where we go deepest in regulated-sector insight, but Wordnerds works across housing, retail and travel & hospitality too. A general VoC platform—transport is the depth, not the limit.
Frequently asked questions
What is Wordnerds?
Wordnerds is a UK Voice of Customer platform. It analyses customer feedback from surveys, complaints, reviews and calls using transparent, explainable AI, then delivers the insight into Microsoft Power BI where teams already work—so the whole organisation can act on what customers say, not just the insight team.
Can Wordnerds produce ORR/DfT-compliant passenger complaint reports?
Yes. We automatically classify passenger complaints into the ORR's quarterly complaint categories—consistently, with every theme traceable back to the verbatim behind it. The same classification logic runs every period, so you can show trend over time and demonstrate to the ORR or DfT that the categorisation is reproducible, not analyst-dependent.
Can you handle feedback from multiple channels—surveys, complaints, social and app reviews?
Yes. We pull together surveys (including Qualtrics and SurveyMonkey), CRM complaints, iOS and Android app reviews, social exports (Twitter/X) and contact-centre transcripts into one analysis-ready stream. Everything is themed the same way, so you get a single comparable view of passenger sentiment across every channel you already use.
We already use Qualtrics—why do we need Wordnerds?
Wordnerds works alongside it. Qualtrics is excellent at collecting survey responses; we're the specialist layer that classifies the free-text it gathers—plus your CRM complaints, app reviews and social—and delivers the themed insight into Power BI. We unlock the feedback you already collect rather than replacing your survey tool.
Can't we just use ChatGPT or Copilot to classify passenger complaints?
Free LLMs classify differently every time they run, can't maintain ORR-category consistency across periods, and leave no audit trail you can show the regulator. They also can't drilldown to route or station level reliably. We apply trained classification you can defend—consistent, traceable and built for the categories your reporting framework requires.
Is our passenger data stored securely in the UK?
Yes. We're built for UK regulated sectors, with UK data residency, Cyber Essentials Plus certification, and ISO 27001 accredited data centres. Our processing is transparent rather than a black box, and we don't train external AI models on your passenger data. GDPR-compliant as standard.
Can we start with one data source and expand later?
Absolutely. Most operators start with their survey or complaints data, prove the value, then add contact centre transcripts, social and app reviews over time. The platform is built to expand—adding a new source doesn't require rebuilding what's already working.
Can't we just build this ourselves?
You could—but it involves unifying data from multiple sources in different formats, developing AI classification that's transparent and defensible, building dashboards for board, operations, and depot teams, and maintaining everything as requirements change. We've spent years building this specifically for transport operators, working with Avanti West Coast, Greater Anglia, Transport for Wales and others. Most operators find it faster and cheaper to use what we've already built.
What does GBR restructuring mean for passenger feedback reporting?
GBR will likely consolidate rather than eliminate reporting requirements—the ORR complaint-category framework is expected to persist in some form under the new structure. Operators who have consistent, auditable classification in place will adapt more easily than those still relying on manual period-by-period coding. We're tracking the guidance as it develops.
