SENA Phase 1 – Project Recap

Why were we doing this? 

Our motivation for doing this was looking at the data we have around our services, we saw a 150% increase in Universal Credit (UC) claimants in our district from March 20 – September 20. This was driven from both Covid-related job and income losses (some driven from furlough). Our data indicates that there may be a correlation showing that those directly affected by Covid-19 financially had around six months of contingency funds available, with an almost immediate uplift in UC claims followed, six months later, by a similar increase in the need for support from foodbanks. 

The last event that looked like this was the 2008 financial crisis which continued to impact on the demand for public sector services for seven years afterwards. So, if we can engage with people early and help them early we can hopefully reduce this demand, as we can’t easily handle an increase in demand over the next 7 years coupled with reduced income.  

So what did we do? 

Risk modelling: We looked at the people that had needed support during the pandemic based on our outbound calling approach and we compared this to what we knew about people. These things didn’t match. So, we followed up with people (c. 75) and interviewed them to create “life journey maps” and used this to try and assess people’s likelihood of needing support. 

What did we learn? 

  • The people that needed support during COVID didn’t match our initial view of who would, in fact there wasn’t a lot of additional need in the people we had ongoing relationships with, but a lot of new people needed support.  
  • Our improved “life events” based risk model didn’t work well either and more work is needed in trying to create predictive models. 
  • We did find that there were links between people who experienced many negative events in quick succession and those needing support even if those events weren’t that significant. 
  • The information needed to create these “life journey maps” was spread across many organisations and existing data sharing provisions didn’t allow us to access this data to create these maps without having to ask the people questions again.  

Tested how to engage: We tested different call scripts, email messages and letters to work out which got the best engagement from our residents. Here we were trying to engage people with our self-service support platform and then onwards to dedicated support services that could help them.  

What did we learn? 

  • It’s easy to understand what works in call scripts and that if we call people and reach out, they are very amendable to being directed to services thathelp them help themselves. 
  • People want to talk about what has happened to them and use their language not ours, they want to tell us about the outcomes they need.  
  • It’s hard to track digital engagement without a lot of planning and the correct digital tools in place, so we need to design in the needed data gathering functionality from the ground up in our digital solutions, and being able to track people across channels is going to be hard but worthwhile.  

What next? 

We applied for and were successful in receiving funding for phase 2, which is a bit different but linked to the lessons learnt.  

We are going to write up our risk modelling approach and share it to see if others can help improve it.  

We bid into the Local Data Accelerator fund to look into resiliency factors and how communities and community infrastructure can support families.  

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