Anna - stock.adobe.com
Following hot on the heels of the Covid pandemic, the current cost-of-living crisis is putting increasing pressure on customers and their ability to balance competing financial demands.
The StepChange Debt Charity received 14,000 new clients for debt advice in June 2022, with around one in five of those citing an increase in the cost of living as a main reason for falling into debt. In fact, StepChange cites the cost-of-living hike as the single most commonly mentioned reason for debt among new clients.
The charity reported that 28% of new clients have responsibility for water bills. While households have not seen their water bills escalate in the same way as gas and electricity, the general squeeze on household funds means many are struggling with all of their outgoings.
StepChange’s findings are reflected in a study from data company Sagacity, which has found that two-thirds of the UK – about 18.6 million households – are worried about their ability to pay utility bills, yet just 6% of the people surveyed have specifically asked their utility supplier for help.
A number of initiatives have been launched, by government and regulators, for utilities to limit the impact of the cost-of-living crisis on the most vulnerable members of society.
Over a 20-month pilot period from 2020, water regulator Ofwat awarded £63m through its innovation fund. One of the winning projects, Water4All, a consortium led by Southern Water, aims to help customers who may be struggling with bills by improving how low-income and vulnerable households are identified so they can be better supported.
Southern Water is already working with Sagacity, another member of the consortium, to help it better identify properties that may be empty and where customers have moved without informing the utility companies.
Sagacity’s proprietary Occupier ID verifies, corrects and updates poor quality data to eliminate errors and complaints, resulting in more accurate billing and an improved service to customers. Rather than relying solely on matching different datasets, Sagacity uses analytics to clean customer data. The data it uses includes, but is not limited to, proprietary data, credit reference data, Land Registry and specialist third-party datasets.
When moving properties, contacting the water supplier is not always the highest priority. This renders data in the billing database inaccurate and means people living in such properties are often sent surprise bills addressed to “The Occupier”. This is becoming a growing issue for companies and is leaving many customers without access to the help they may need.
Describing the importance of clean and accurate customer data, Donna Howden, Southern Water head of customer service, says: “Water4All brings together water and data companies. With Sagacity, we are working together to look at how we find customers who are financially vulnerable. Some may be struggling to pay energy bills and won’t pay for water.”
By analysing data from Sagacity, she says it may be possible to identify who is having financial difficulties and may be at risk of having their water supply cut off.
Southern Water has seen a rising trend of properties without an identified occupier, which means bills cannot be issued and customers cannot be contacted. While some of these properties are empty, or void, others are not, which creates a range of challenges for the company and its customers. Where bills are not being paid or meters not being read, the water that is being used is not only lost revenue, but is also often lost water and seen as leakage.
As part of Ofwat’s 2019 price review, the water regulator stipulated that water companies must establish a performance commitment covering the management of site gaps (unbilled properties) and voids (vacant properties), for both household and non-household customers.
This means water companies need to understand who lives in the property, whether there has been a property development, and if data is missing, such as where the data for a given street has some numbers missing, says Howden. This can happen when two houses are combined to create a larger property.
The value of clean billing data
Identifying these gap properties is a key step in helping the water company understand if someone is struggling to pay the water bill. Referencing a study from StepChange, Howden says customers new to debt take, on average, 12 months to reach out to the supplier.
“Historically, we have always done some form of trace with search agencies,” she adds. “We could have a customer living in a property who is eligible for financial support from us or can register for our priority service.”
But, to identify these vulnerable customers, clean and accurate data is required. During a pilot of the data cleaning project, Sagacity cleansed and validated 98% of the accounts Southern Water supplied, significantly reducing instances of incorrect addresses. As a result, in 31% of the cases, Sagacity identified an occupier with a strong level of confidence, enabling Southern Water to contact those customers, who were previously either unknown or listed incorrectly, and bill them accordingly.
Putting aside the restrictions imposed by the General Data Protection Regulation (GDPR), Howden hopes that one day it will be possible for all utilities to share data in a way that would help them understand when a property has a new occupier and whether that person may be eligible for financial support.
“Identifying occupiers gives us a true picture of our region, customers and demographics,” she says. “It enables us to provide our full range of services to customers, especially those who need our support the most. This has never been more critical, with the current cost-of-living crisis impacting so many people.”
Read more about data cleansing
- SAP’s Kristin McMahon details data cleansing best practices and explains why a good data cleanse needs continual communication, collaboration and oversight.
- Trustworthy analytics outcomes depend on the right data, requiring data scientists to focus on these steps when they prepare data for use in machine learning applications.