Know what your tenants are really saying—and prove it
Your tenants tell you what they think every day—in TSM surveys, the Big Listen, complaints and repairs notes. Keeping up by hand is the hard part, and when the Regulator asks how you know, a spreadsheet of themes is hard to defend. One housing association turned its whole Big Listen into a board-ready report with a single analyst, across 20,000+ homes.
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 keep up with what tenants are telling you?
Most housing teams have more tenant feedback than they can read. TSM surveys, the Big Listen, complaints and repairs notes all arrive separately, and a small insight team codes them by hand. The feedback piles up faster than anyone can theme it, and the richest detail goes unheard.
Tenants take the time to tell you what they think. The least they deserve is to be heard—but when each channel sits in its own system and one or two analysts theme thousands of comments by hand, doing them justice stops being realistic. The work is slow, it is repetitive, and the insight lands too late to act on.
So you fall back on the headline score and a handful of quotes. The pattern underneath—what is actually driving complaints on which estate, and whether last quarter's fix worked—stays buried in the text nobody had time to read.
How do you evidence to the Regulator that you're listening to tenants?
Evidence listening by showing your working: themes traced back to the tenant's own words, defined the same way in January as in December. Wordnerds classifies tenant feedback against the RSH Consumer Standards, the TSM measures and the 29 Awaab's Law hazards—so you can answer the Regulator, move toward C1, and fix what tenants are actually raising.
When the Regulator, the Housing Ombudsman or your own board asks "how do you know?", a black-box AI summary cannot answer—it counts differently every time it runs, and you cannot show how it reached a theme. Our classifications are transparent, traceable and auditable: every theme links back to the verbatim behind it, and the categories hold steady over time so you can compare this year to last and prove a trend.
That defensibility is what lets the evidence do real work—not just satisfy an inspection, but show which repairs, which estates and which moments in the tenant journey to fix first.
What changes when tenant feedback works for your whole organisation?
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GENERATE-IMAGES: pillar illustration 640×360px. Many tenant-feedback sources unifying into one themed stream; uniform house-style, brand palette
Every voice, every source
Bring TSM surveys, the Big Listen, complaints, repairs notes and calls into one place, themed the same way—so every tenant voice counts and you can finally see across channels instead of one survey at a time.
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GENERATE-IMAGES: pillar illustration 640×360px. Structured themed bars compared against peer benchmarks (Housemark); uniform house-style, brand palette
Structured, themed, benchmarked
Tenant feedback is classified against a ready-built housing framework and benchmarked with Housemark—so you see not just your own themes but how you compare to other associations, and where to focus first.
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GENERATE-IMAGES: pillar illustration 640×360px. Tenant insight live in a Power BI surface in a leader's hands; evoke a dashboard in-use without faking Microsoft branding; uniform house-style
Live in decision-makers' hands
Insight feeds straight into Microsoft Power BI, so leaders and service teams filter by theme, estate or time without waiting on the insight team. From insights to action—the feedback drives the operational change, not another report that goes nowhere.
Does Wordnerds already speak housing—TSM and Awaab's Law?
Yes. Wordnerds ships a housing theme bank built to the TSM categories and the 29 Awaab's Law (HHSRS) hazards, ready from day one. Sector-tuned AI models detect the themes automatically; your team then refines the definitions in housing's own language—so the framework fits your stock, your services and your tenants, not a generic template.
The classification runs on trained models, not hand-coding and not a general-purpose chatbot. They read every comment, detect the housing themes and count them reliably. Definition-led co-design sits on top as the quality layer: your analysts shape what each theme means—what counts as damp and mould, what counts as a communication failure—so the output is both automated and genuinely yours.
The result is a framework a regulator recognises and a team can defend.
How does it work for a housing team?
Three steps, and your tenant feedback is doing real work. Here's the short version—the full method is one click away.
Consolidate every source
Send us your tenant feedback in whatever form it arrives—TSM surveys, the Big Listen, complaints, repairs notes, calls. CSV to start, an API when you're ready.
Classify with the housing theme bank
Sector-tuned AI themes every comment against the TSM categories and the 29 Awaab's Law hazards, refined in your own language—reliably and the same way every time.
Deliver into Power BI
The themed insight lands in Microsoft Power BI, where leaders and service teams explore it by theme, estate or time—so the feedback reaches the people who can act on it.
What does it look like when it works? The Big Listen.
A housing association managing more than 20,000 homes ran its Big Listen with Wordnerds. One analyst produced the full report—credible enough to drive action at the most senior level, with the Chief Executive citing it unprompted. Specific repairs and location issues were found and fixed, and a one-off exercise became regular monthly analysis.
The point was not a tidier report. It was that the insight changed what the organisation did—and kept doing. Once the feedback was structured and themed, the team stopped spending its time coding comments and started spending it on the issues the comments revealed.
That is the shift this page is about: when the Regulator or the board asks how you know, you have the answer, and the evidence behind it.
When the Regulator or your board asks how you know, you have the answer—with the evidence behind it.
Housing teams already doing this
From associations of every size, on what changed when the feedback finally came together.
"In housing we're often accused of not listening to our customers. We felt we were only scraping the surface of what they were telling us. Wordnerds allows us to show residents we're not just taking their feedback, we're actually hearing it as well."
— Rebecca Pavey-Kenny, Customer Experience Manager, BPHA
"Wordnerds is allowing us to spend less time on analysing the data and more time demonstrating to our customers that we're driving improvements through their feedback."
— Joanne Silner, Head of Customer Experience, Raven Housing Trust
Built for housing—and every sector that has to listen
Housing is where we go deepest, but Wordnerds is a Voice of Customer platform, not a housing tool. We do the same work for transport operators, retailers, utilities and travel brands—anywhere customer feedback needs to drive decisions.
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.
Do you have a housing theme bank, and can you analyse against TSM categories and the 29 Awaab's Law hazards?
Yes. We ship a housing theme bank built to the TSM measures and the 29 HHSRS (Awaab's Law) hazards from day one. Sector-tuned AI detects the themes automatically; your team refines the definitions in housing's own language, so the framework matches your stock and services.
We're a smaller housing association—is Wordnerds for us?
Yes—leaner teams often gain the most. The same regulatory bar applies whatever your size, but a one- or two-person insight team feels the manual workload hardest. Automating the theming recovers the most capacity per person exactly where capacity is tightest.
Can't we just use ChatGPT or Copilot to analyse tenant feedback?
You can try, but free LLMs run differently every time, can't count reliably, and leave no audit trail—so you can't show the Regulator how a theme was reached or compare one year to the next. We apply trained classification you can defend, with every theme traceable to the tenant's words.
We already use Qualtrics or Medallia—why do we need Wordnerds?
Wordnerds works alongside them. Those platforms are excellent at collecting survey responses; we're the specialist layer that analyses the free-text they gather—plus complaints, repairs and calls—and delivers the themed insight into Power BI. We unlock the feedback you already have rather than replacing your survey tool.
Can we export the data, and what if we don't have Power BI?
Your data stays yours—our open approach means no platform lock-in. Power BI is where we deliver natively because it's where Microsoft-based housing teams already work, but the structured output is exportable, so you keep full control of your tenant insight.
Is our tenant data secure and held in the UK?
Yes. We're built for UK regulated sectors, with UK data residency and Cyber Essentials Plus. Our processing is transparent rather than a black box, and we don't train external AI models on your tenant data.
How does Wordnerds help us evidence to the Regulator that we're listening?
Every theme traces back to the tenant verbatim behind it, and the categories stay stable over time—so the same issue is counted the same way in January and December. That gives you auditable evidence for the TSM measures, Awaab's Law and your board: not a score, but the working behind it.
What if the AI gets it wrong?
No AI—and no team of analysts—gets text classification 100% right. What matters is that you can see where it's wrong and fix it: every theme is auditable, and automated accuracy tools flag underperforming categories so your team can refine the definitions. Most housing teams find 80–90% accuracy is the right balance between precision and the time it would take to hand-code the same data.
What skills does our team need to use Wordnerds?
Familiarity with Power BI is useful but not required—we provide full onboarding and a dedicated Customer Success Manager who supports your team through implementation and ongoing use. The insight team uses the Wordnerds platform to refine themes and run analysis; everyone else accesses the output through the dashboards they already know.
