Sir – A common theme has emerged in the past year regarding the impact of welfare reform; the conclusion that housing providers need to devote more resources to support tenants to avoid problems with arrears. We’ve heard landlords describe the need for increased process efficiencies to enable front-line colleagues to spend more time with tenants, and I’ve written previously in Housing Technology about the role of integrated systems and data in supporting this outcome.
An obvious, but seldom-used approach to make the most of finite front-line resources is the use of analytics to target activity where it’s most needed. For more than a decade other industries, from financial services to travel agents, have used modelling and segmentation techniques to predict behaviour and tailor the content and timing of communications and offers.
Some industries suffer a lack of data regarding their customers and prospects, and have to rely on third-party geo-demographic datasets to enrich what they have and help drive predictive analytics. In contrast, housing providers have a comparative wealth of data, including demographic information, transactional histories, and details of repairs, complaints, and enquiries. Using even fairly basic statistical techniques and tools, this rich seam of data can be mined to provide robust and predictive models to identify the tenants most at risk of arrears and, therefore, those where proactive support will pay dividends.
While some housing providers have created segmentation models, and some have occasionally brought together the necessary data to support analysis exercises, very few have dynamic models using real-time (or even regular) data feeds to monitor changing situations and provide early warning alerts – especially valuable when it comes to reacting to recent payments, or lack of them. Anecdotally, the primary hurdle is the age-old problem of siloed, heterogeneous datasets, that are time-consuming and painful to collate, and difficult to standardise in readiness for analysis.
Which brings me full-circle to the ERP concept, where datasets are naturally common across all processes, and where transactional updates are reflected in real time. Apply some analytics tools across this data foundation – no rocket science is required – and you have the potential to scientifically tackle the impact of welfare reform.
Paul Swannell, Sales Manager for Social Housing, Ciber UK