Ensure every customer’s voice is heard with Wordnerds

Tenant Satisfaction Measures add a whole new dimension to the landlord’s responsibilities to their residents. The stated aim of the program is to “rebalance the relationship between residents and landlords” in a way that emphasises the voice of the customer.


Wordnerds, now in partnership with HACT, provides a cloud-based text analysis software that helps housing associations listen more closely and effectively to residents. Whether it’s surveys, live chat, complaints, emails, or call centre transcripts — if there’s words, we’re your nerds.

Housing associations use Wordnerds to:

  • Improve tenant satisfaction
  • Find new and emerging trends among residents
  • Evidence poor contractor behaviour
  • Achieve consistency across courts/neighbourhoods
  • Prevent escalations to the ombudsman

A (not-so) fond farewell to manual analysis

Wordnerds has set out to create a platform that allows organisations to hear every voice they are listening for. In the case of housing associations, this is particularly vital. With mounting regulatory pressure and the highest of stakes if something is missed, it is crucial that housing associations hear everything their residents tell them.


Working with multiple large housing associations has allowed Wordnerds to develop a bank of language models, trained on UK housing data, that enable insights professionals to skip the reading-through-spreadsheets part of their day and get straight to making data-backed recommendations to their organisations.

AI doesn't give you enough

Avoid surprises

Auto-generated topics allow a holistic view of what is happening in your data set; no need to worry about missing a sudden spike in new language like “furlough” that you weren’t already looking for. Plus, fragment-level sentiment analysis will always clue you in on how individuals feel about any given topic.

Know the context

AI only gets you so far. Where it really falls down is understanding the contextual nuance of language — whether a tenant is talking about “outstanding service” or “outstanding debt”, or whether they really meant it when they say a contractor did a “bang up job”. Our secret sauce is corpus linguistics, meaning our platform can discern things like sarcasm and industry-specific language without having to give a team of data scientists 6 weeks of lead time and 6 months of budget to understand a dataset.

Train categories

While Wordnerds has already worked with several large UK housing associations to train over 70 categories into which to automatically sort your tenant feedback on issues including the TSMs, damp and mould, and Ombudsman escalations, it is easy to train your own categories given 15 minutes and the right mindset.

Watch & listen

NHC x Wordnerds – Adapting customer data to tenant satisfaction measures

Wordnerds explore how best to report on tenant data to conform to the Ombudsman and Regulator’s requirements. Guest starring Peter Stephenson, Research Analyst at Karbon Homes, who took the time to lay out how Karbon have approached TSMs and the issue of damp and mould.

Fancy a chat?

To ease the burden of analysing resident feedback, and to bridge the gap between what AI can achieve and insights professionals actually need, book a chat today

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