NolijWork

𝗧𝗿𝘆𝗶𝗻𝗴 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 #𝗚𝗲𝗻𝗔𝗜 𝗶𝘀 𝗯𝗲𝗻𝗲𝗳𝗶𝗰𝗶𝗮𝗹 𝘁𝗼 𝗬𝗢𝗨𝗥 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻, 𝗼𝗿 𝗻𝗼𝘁? ⭐️ This is a handy explainer from Gartner on the broad areas where GenAI is beneficial, and perhaps more importantly, where it is 𝗡𝗢𝗧. ⭐️ If service improvement (i.e. optimization) is 𝗬𝗢𝗨𝗥 goal, ithen data science and machine learning (another form of AI) are more relevant than GenAI. ⭐️ In service-intensive sectors such as #SocialHousing, #SocialCare, using these alternative methods, with data you already own, will drive significantly greater impact. See link in comments for more information.

𝐂𝐚𝐥𝐥𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐟𝐫𝐨𝐧𝐭𝐥𝐢𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐇𝐨𝐮𝐬𝐢𝐧𝐠 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐜𝐲 (“𝐓𝐚𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐞𝐦𝐩𝐞𝐫𝐚𝐭𝐮𝐫𝐞”) I was struck by this content on "All In For Change" from Homeless Network Scotland, as reported by Scottish Housing News. It could easily apply across the UK, even though the report relates to Scotland. Part of the resonance centred on item 3 - increasing housing stock, and what NolijWork are doing around Voids and Responsive Repairs. 👉🏻 With roughly 4.5 million social homes across the UK, of which approximately 5% will become void annually – amounting to circa 225,000 homes per annum. 👉🏻 In the midst of a housing crisis – properties that are out of service for longer than strictly necessary represents a significant missed opportunity. Void turnaround targets and performance stats will naturally differ for Minor versus Major works 💡 𝐁𝐔𝐓 Here’s the rub, these statistics are reported via averages, 𝐀𝐍𝐃 that hides a critical reality, 💡 Many properties turn around quickly, in a matter of just a few days., whilst other properties can take an inordinate amount of time to reinstate and relet. 💡 By reporting statistics as averages – it masks the key realities towards the extremities – often resulting in headline figures such as 20-30 days on “average”. 💡 Moreover, it can lead to a belief that merely hitting targets is sufficient, when opportunities for significant improvement still exist. If providers look more deeply at their Voids performance, beyond headline averages, they may well identify opportunities to accelerate voids, create extra capacity and provide much needed housing faster. Not only is this a win for tenants, it’s a potential win / double-win for providers, increasing available revenue, but also potentially reducing temporary accommodation costs, which can be orders of magnitude greater.    See comments for links to the original publications. #SocialHousing

🚀 See how the DIATOMIC UK Accelerator is driving real results for innovators in Birmingham.   Commercial opportunities and follow-on funding have been secured by participants on a new accelerator programme in the West Midlands, focused on housing and waste management.    Learn more at the link in the comments 👇    Paul O'Neill NolijWork Graham Hygate Osmium Group Limited  #DIATOMIC #WMIA #WestMidlandsInnovation #WMPlanforGrowth #Innovation #Technology West Midlands Growth Company Greater Birmingham Chambers of Commerce

💡 𝗧𝗵𝗲𝘆 𝘀𝗮𝘆 𝗮 𝗽𝗶𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝘄𝗼𝗿𝘁𝗵 𝗮 𝘁𝗵𝗼𝘂𝘀𝗮𝗻𝗱 𝘄𝗼𝗿𝗱𝘀... 𝗪𝗲𝗹𝗹 Virpi Oinonen 𝗼𝗳 Business Illustrator Ltd 𝗵𝗮𝘀 𝗮 𝘂𝗻𝗶𝗾𝘂𝗲 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝗲𝗻𝗰𝗮𝗽𝘀𝘂𝗹𝗮𝘁𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘁𝗵𝗮𝘁 𝗱𝗲𝗳𝘆 𝗽𝘂𝘁𝘁𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝘄𝗼𝗿𝗱𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗽𝗹𝗮𝗰𝗲. This image perfectly encapsulates a problem faced by #SocialHousing, in tackling Responsive repairs, when contrasting realities can simultaneously be true. ·       👉🏼 Most responsive repairs are literally as simple as (A) something is broken, so (B) it needs to be fixed ·       👉🏼 Equally however, other repairs involve repeated visits, multiple attempts at resolution, and can be further confounded by human failures. How do I know this? Well, here’s evidence to demonstrate it, from analysing operational repairs data to show how they REALLY work in practice. In this case, a generated view of 100,000+ responsive repairs (and this is the simple version). ·       💡 The majority of repairs are highly transactional, as shown by the tasks in the dashed green boxes ·       💡 Whereas when things go wrong, as illustrated by tasks in the dashed red zone, it all becomes much, much more complicated... ❌ It’s the latter repairs that lead to tenant dissatisfaction, repeated contact, then complaints, and if unchecked, ultimately end up as the subject of investigations by the Housing Ombudsman. ⚠️ Housing providers are caught on the horns of a dilemma. They need to deal with high-volume transactional repairs, but equally with the lower volume of “complex cases”. A bifurcated approach is a significant challenge to an established one-size-fits-all model.   ✔️ The typical solution of applying more resources to repairs and complaints, is unlikely to yield significant improvement, unless accompanied by new ways of understanding and thinking about this challenge. This is where insights from your data comes into its own!

🔍 𝗔𝗻 𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝗹𝗼𝗼𝗸 𝗮𝘁 𝘄𝗵𝗮𝘁 #𝗦𝗼𝗰𝗶𝗮𝗹𝗛𝗼𝘂𝘀𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗮𝗶𝗻𝘁𝘀 𝗱𝗮𝘁𝗮 𝗳𝗿𝗼𝗺 𝗧𝗲𝗻𝗮𝗻𝘁 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀 (𝗧𝗦𝗠𝘀) 𝗺𝗶𝗴𝗵𝘁 𝗿𝗲𝘃𝗲𝗮𝗹... 😠 As shown on the first swarm plot for TP09 - 50% satisfaction with complaint handling represents a TOP-END score, but relatively few even approach that level. ⚠️ Some providers are experiencing up to 15% Level 1 complaint rates, relative to the number of properties they manage (i.e. 150/1000), a considerable workload in its own right, as evidenced in the second swarm plot. 💡 N.B. TP09 only pertains to tenants who made a complaint, i.e. a sub-set of the overall cohort. That said, satisfaction with complaints does show some correlation with overall satisfaction, once it gets beyond a certain level, as shown by the trend line in TP01 vs TP09 chart ❗ Finally, data on tenant complaint satisfaction versus provider complaint handling performance tell different stories, i.e. the tenant’s view versus the provider’s view, and they do not necessarily align. Simply hitting quantitative timeline KPIs are no guarantee of tenant satisfaction, as the latter does not “capture” the whole tenant experience. [See TP09 vs CH02(1) chart, especially the distribution of points from trend line] 𝗡𝗢𝗧𝗘𝗦 :- 👉🏼 Data based on a subset of TSM data (Housing Association LCRA data only, representing over 2.25 million homes) 👉🏼 Providers are grouped by number of units, which are arbitrarily grouped as follows (<10k, 10-30k, 30-50k, >50k)  👉🏼 Node sizing on graphs represents the number of units under management. i.e. bigger node = larger provider. 𝗧𝗦𝗠 𝗱𝗮𝘁𝗮, 𝘄𝗵𝗲𝗻 𝗳𝘂𝗹𝗹𝘆 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱, 𝗶𝘀 𝗹𝗶𝗸𝗲𝗹𝘆 𝘁𝗼 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁 𝗮𝗻 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝗳𝗼𝗿 𝗳𝘂𝗿𝘁𝗵𝗲𝗿 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲-𝗯𝗮𝘀𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝘀𝗲𝗰𝘁𝗼𝗿.  

𝐖𝐡𝐚𝐭 #𝐒𝐨𝐜𝐢𝐚𝐥𝐇𝐨𝐮𝐬𝐢𝐧𝐠 𝐓𝐞𝐧𝐚𝐧𝐭 𝐒𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐬 (𝐓𝐒𝐌𝐬) 𝐝𝐨𝐧’𝐭 𝐬𝐡𝐨𝐰… ⚠️ Whilst the Tenant Satisfaction Measures (TSM) scheme reveals the significance of Repairs for the tenant experience, the reported data masks at least one hidden problem. Certain key performance indicators only provide a broad-brush view of compliance with target timelines. • 🎯 RP02 - "Repairs completed within target timescale, Non-emergency" • 🎯 RP02 - "Repairs completed within target timescale, Emergency" 🔍 By comparing these “Repair timeliness” metrics to TP02 (Satisfaction with Repairs) in the attached plots – you get a sense from the trend lines of the relatively limited influence on overall Repairs Satisfaction. ⚠️ What these metrics fail to differentiate is the extent of over-runs. i.e. does it matter to tenants if something is 1 day late, versus 365 days late…? I expect so. 💡 But one survey provider in particular, by gathering additional data as part of their brief, may have helped identify a contributory factor. This appears in supporting information often published alongside core TSM performance data. (See attached examples) ⭐️ The specific issue is referred to as “Outstanding/forgotten repairs” – and it appears high on the list of tenant feedback. The question is how are organisations “forgetting” about repairs, or failing to keep tenants in the loop? This highlights that Responsive Repairs is not just a transactional operation, but rather an end-to-end service. 💡 This feedback is also in keeping with NolijWork’s own data analysis of raw repairs datasets, when analysing service performance, where we can look in more detail at how services operate in practice, at scale. So for providers with considerable property portfolios, the questions become 👉🏼 how can they easily spot these “outlier scenarios”, which may seem like looking for a needle in a haystack?  👉🏼 moreover, how can they detect what drives such exceptions to happen, so they can be prevented in the first place? ✅️ Well, NolijWork believes it all comes down to using the data that organisations already possess, but remains largely untapped. This is exactly how we help clients to understand how their services REALLY work, what isn’t working well, and what is driving such outcomes.

What might #SocialHousing Complaints data from Tenant Satisfaction Measures (TSMs) reveal, as promised from an earlier post? 💡 TP09 - 50% satisfaction with complaint handling represents a TOP END score, but relatively few even approach that level.   [See x-axis range in coloured TP01 vs TP09 chart] 💡 Some providers are experiencing up to 15% complaint rates, relative to the number of properties they manage, a considerable workload in its own right. [See x-axis range from TP09 vs CH01(1) chart (rate / 1000 homes)] 💡 TP09 only pertains to tenants who made a complaint, i.e. a smaller cohort. That said, satisfaction with complaints does show some correlation with overall satisfaction, once it gets beyond a certain level. [See TP01 vs TP09 chart with trend line] 💡 Data on tenant complaint satisfaction versus provider complaint handling performance tell different stories, i.e. the tenant’s view versus the provider’s view, and they do not really align. i.e. the satisfaction score represents the tenant experience, whereas M.I. performance scores represent the provider’s experience. [See TP09 vs CH02(1) chart, note distribution of points from trend line] 💡 This contradiction makes sense imho, in that one is qualitative, the other is quantitative, and the latter does not “capture” the whole story. Hitting timeline KPIs are simply no guarantee of tenant satisfaction. 💡 Moreover, whilst upscaling complaint handling capacity (as indicated by various providers) may improve internal MI/KPI performance, that may not drive TSM satisfaction score improvements significantly. 💡 There is a somewhat limited correlation between volumes of complaints and satisfaction with complaint handling. Again, tenants care about THEIR complaint, not how many others are complaining too. [See TP09 vs CH01(1) chart] TSM data, when fully released, is likely to represent an opportunity for further evidence-based learnings across the sector.  It will be interesting to see exactly what change and #innovation it might drive in due course. Flagging to contacts from prior TSM posts, including w.r.t. Complaints. Ian Wright Steve Dungworth Peter Hall Paul Harris Sue Hanlon Linda Colburn Steve Allcock Gemma Hounsell Monika Edwards Michael Rooney

Could results from Tenant Satisfaction Measures (TSMs) drive change and spur #innovation in the #SocialHousing sector? Well, I think they just might... From an early analysis of available data, representing over 2 million homes, its already possible to see emerging trends. 💡 Well maintained property is HIGHLY correlated to overall Tenant Satisfaction (Graphs 1, 4 - the latter shows the fit line) 💡 Unsurprisingly, there's also broad correlation between overall tenant satisfaction versus repairs and speed of repairs 💡 The largest providers appear to be "scoring" less well, relative to other smaller organisations. When all of the data is published, the obvious question for the sector then becomes, so what do we do about this...? Given Ian Wright's recent post around innovation within the sector, what do others think may or may not happen, on the back of TSMs...? NOTES :- 👉🏼 Providers are grouped by number of units, which I've arbitrarily grouped as follows (<10k, 10-30k, 30-50k, >50k) 👉🏼 The node size on each of the 3 coloured graphs represents the number of units under management. i.e. bigger node = larger provider. 👉🏼 This is just a subset of our TSM data analysis results 👉🏼 Whilst individual TSM performance metrics are useful, its likely the inter-relationships BETWEEN the metrics which will be of greater value

What can the #SocialHousing sector learn from Tenant Satisfaction Measures (TSMs), as results start to be published...? I recently participated in a Disruptive Innovators Network webinar on the "long tail of complex cases" around repairs, together with Ian Wright and Andy Belton. The session highlighted the "tails" centred on both repair delays, and frequency of repairs by property. In essence, the expectation is that customer dis-satisfaction is associated with these "long tails", which is where performance improvements can be most impactful. So with individual TSMs now being published by providers, I set out to see what could be gleaned from that data. Having looked at several dozen of the larger providers' publications, the published results were somewhat "high level", adhering strictly to the required metrics publication scheme identified by Regulator of Social Housing. But then, I came across one organisation, who imho are to be highly commended for their approach, their publication of results, and indeed learnings. In addition to the summary information, that organisation :- 🎯 Provided ALL of their statistics - across the entire range 🎯 Combined the results with internal categorical information, e.g. property characteristics to drive more insights 🎯 Produced correlations across the different satisfaction measures (correlation diagram and analysis) 🎯 Regression analysis of key factors to identify the main drivers of dis-satisfaction 🎯 Explained all of their methodology and workings, from survey questions to survey sample weightings. Some key takeaways from their analysis and report, that drivers of overall satisfaction are :- 💡 Tenant satisfaction that the property is well maintained 💡 Satisfaction with the repairs service generally 💡 Satisfaction with the time taken to undertake repairs more specifically 💡 Finally, that satisfaction is NEGATIVELY associated with more tenant contacts and more repairs work in the previous year. So it is great to see validation of our own work NolijWork, applying data and process science to analyse service performance from operational data, and highlighting these "long tail" issues. But most importantly of all perhaps, the analysis by that housing provider illustrates what can be achieved on the back of the Tenant Satisfaction Measures (TSMs), i.e. an evidence-based approach for targeted improvements. This is how learning happens. #SocialHousing #innovation