I have always been impressed by the way that new IT developments evolve from some high technology test-bed into crucial business applications. By offering to deliver hitherto unattainable business benefits, these IT developments quickly become an agent for change, as existing systems start to incorporate the next big thing.
As we all recall, mobile workforce technology itself was originally ground-breaking and unproven, yet it’s now at the heart of most housing providers’ technology estate. Rolling forward to much later technologies, we are seeing how IoT can potentially deliver huge benefits in areas such as boiler management, security and tenant broadband.
However, while it’s one thing to look at how technology affects your own industry once it arrives, it’s possible a far more useful task is to study how technological advances that have worked well elsewhere might bring benefits to social housing. By studying what has worked well in other sectors, there are often benefits that we can translate into our own sector as an agent for change. This near-future predictive analysis is incredibly valuable because it reveals a rich landscape of opportunities, both in terms of driving efficiency and boosting customer service.
I think there are fundamental lessons to be learnt from high-profile social media, app developers and data scientists in the consumer world. Potentially, these hold considerable promise for housing providers and their tenants. 1st Touch is currently studying how we can incorporate some of these ideas into our own systems, with our ‘2020 vision’ based around the idea of how great technology from one sector could be used to excellent effect in social housing.
For example, let’s look at Uber, famed as being the world’s largest and fastest growing driver hire operator. While not actually owning any hire vehicles themselves, their online app-based ordering model has been incredibly disruptive to traditional taxi service providers around the world.
To me, the really impressive thing is that the whole online operation behind Uber uses near-live interaction. Customers who book a cab using their smartphone or other GPS-enabled handheld device are sent details of the selected car and driver and receive an estimated arrival time alongside other information relevant to their trip.
Surely, this approach can also drive standards in social housing. For example, it could make a huge difference to appointment scheduling systems. Tenants should be able to go online 24/7/365 via a housing provider’s portal or app and enter a request for, say, a responsive repairs operative to attend or a visit from a housing officer. Like Uber, this appointment would then be confirmed to tenants by text. On the day of dispatch, using the Uber model, tenants could also be sent a photo of the mobile operative, their name, an estimated arrival time and even the registration number of their vehicle. This would greatly reduce the number of failed visits and significantly boost customer perceptions.
Such approaches prove the validity of productive self-service apps for customer use. One only has to look at the online bank Atom. It has no branches. All its customer’s financial transactions are made through an app and consequently the service levels are the same quality 24/7/365. This shines a clear light for the potential of future app developments in social housing.
One can see other lessons that can be learnt from the mainstream. For example, Experian is using big data and extremely clever algorithms to predict the likelihood of people falling into debt. To achieve this, they use trend analysis to extremely good effect. Surely then, it’s very possible for housing providers to use similar techniques to identify those people who are vulnerable, likely to fall into arrears, require maintenance or that have boilers in need of replacement.
Interestingly, from our own research, we’ve seen that missed gas appointments have a strong correlation to rent arrears, so the use of statistical analysis is very relevant to social housing. To ensure that that this data analysis is truly useful, it’s essential that the data is both cleaned and validated to a high level of quality and it’s equally important that sophisticated algorithms are designed to predict and analyse behaviour. Once this is done, the clever part is interpreting the data effectively and coding it into an app.
Given this, there is huge potential for predictive analytics, based on trends and algorithms, to make a real difference both to tenants in customer service terms and to housing providers looking to drive value for money.
Overall, the continuous evolution of technology is not just an agent for change, it’s also an agent for continuous improvements to service delivery. As the ultimate beneficiary is the tenant, then in keeping with our 2020 vision strategy, we will keep looking for new technologies that improve their tenancies even further.
Greg Johns is CEO of 1st Touch.
