What does it take to turn insight into change? The Insight Report, Episode 1
Network Rail Scotland's Lorna Brown on getting eight rail organisations to share customer feedback in one insight hub.
Wordnerds webinars
Wordnerds webinars are free on-demand recordings, each with a full searchable transcript, on customer feedback, Voice of Customer, social housing regulation and AI analysis methods. Watch any session and read the transcript — grouped by topic so you can find the one that fits your question.
Our webinar series
The Insight Report
Conversations with CX leaders in the age of AI, hosted by Wordnerds' own Nat and Helen. Each episode is one leader's story and the same questions for every guest, then yours. Episode 1, with Lorna Brown of Network Rail Scotland, is out now.
What does it take to turn insight into change? The Insight Report, Episode 1
Network Rail Scotland's Lorna Brown on getting eight rail organisations to share customer feedback in one insight hub.
What does the regulator actually look for in a consumer standards inspection?
The regulator does not grade on your statistics alone — it grades on whether you found your own problems and can evidence what you are doing about them.
Can you predict your Consumer C rating? Four tenant-feedback signals
Four tenant-feedback signals predict your Consumer C rating before the Regulator visits.
How do you segment customers beyond age and demographics? Attitudes Not Age Groups
Effective customer segmentation stacks four layers: who customers are, what they do, how they think, and what motivates them right now.
How do you prepare for the Welsh HHSRS hazard rollout in April 2026?
From 1 April 2026, every Welsh housing association must monitor all 29 Housing Health and Safety Rating System (HHSRS) hazards from day one.
How do you move from owning Voice of Customer to facilitating it? The Customer Always in the Room
Facilitating Voice of Customer means the insights team becomes the architect who lets colleagues find their own answers, not the gatekeeper who produces every report.
How do you improve your TSM score? Six signals from 135,000 tenant comments
The UK's first qualitative TSM benchmark, built from 135,000 free-text tenant comments at 18 housing associations.
How do you adopt AI intentionally in a Voice of Customer programme?
Intentional AI adoption means matching the right type of AI to the right job, not putting everything through generative AI.
How do you turn 390,000 hotel reviews into board-ready evidence?
Wordnerds analysed nearly 400,000 hotel guest reviews across budget, mid-range and luxury chains to show how customer-experience teams turn review data into board-ready evidence of revenue impact.
How do you prepare for Awaab's Law? Get ahead of all 29 HHSRS hazards
Under Awaab's Law, a UK housing association's legal responsibility for a hazard begins the moment a tenant mentions it — on any channel, not just a formal complaint.
How do you turn customer feedback into an evidence-based action plan? The Customer Feedback Framework
Wordnerds' Customer Feedback Framework turns scattered tenant and customer feedback into evidence-based action plans through three stages: collect, categorise and prioritise.
How do you get stakeholders to act on customer insight? From Insight to Influence
Insight only changes behaviour when it lands in a framework stakeholders already trust.
How do you turn customer insight into action in social housing?
Gentoo turned a reactive, siloed complaints team into a C1-rated customer voice operation in twelve months.
How do you analyse customer feedback in Power BI?
Wordnerds reports qualitative tenant feedback in Microsoft Power BI through a three-step framework: classification, semantic model and visualization.
How can you use AI to analyse online reviews?
Wordnerds and Trustpilot show how AI turns online reviews into a competitor benchmark you can act on.
Can I use Copilot or ChatGPT to analyse my customer feedback?
Microsoft Copilot and ChatGPT can summarise customer feedback, but they can't reliably quantify it — the numbers change every run.
