Insights ¦ Automating access to water social tariffs

Key Take Aways

1. Rising water bills in April 2025 are expected to drive an increase of 600,000 households relying on social tariffs, with potential growth to nearly 3 million by 2030, highlighting a significant affordability challenge.

2. Thames Water’s current outreach efforts to promote social tariff take-up face low awareness and application completion issues, indicating a need for innovative engagement strategies.

3. Auto-enrolment using data sharing agreements, particularly with benefits data, presents a viable approach to proactively identify and support households in water arrears, improving targeting accuracy.

4. The pilot demonstrates that matching water arrears data with benefits data can accurately identify eligible households — notably, 42.1% of matched customers in arrears were found to be eligible but not claiming social tariffs.

5. Auto-enrolment could increase social tariff take-up by approximately 9.1% among arrears households, translating into over £390,000 annual benefit for residents in the pilot area.

6. When factoring in rising bills and expanding to households at risk of arrears, the total potential impact of auto-enrolment could reach over £900,000 annually, representing significant financial relief for vulnerable households.

7. The process of dataset matching highlights challenges, such as address mismatches and benefits claim gaps, but also reveals high potential for improved targeting through enhanced data sharing—especially if data on all Universal Credit recipients were accessible.

8. The implementation of auto-enrolment not only enhances financial support but also reduces administrative costs for water companies by decreasing reactive application processing.

9. The inclusion of additional support such as debt advice and income maximisation is under consideration for households at risk, complementing auto-enrolment strategies.

10. Data integration efforts reveal that only a fraction of households claiming means-tested benefits are currently on social tariffs, suggesting substantial untapped potential for policy enhancement.

11. There is a notable disparity in data coverage, with only 45% of Universal Credit claimants in Richmond and Wandsworth captured in council data, underscoring the importance of expanding data sharing mechanisms.

12. The report advocates for enhanced data access by the Department for Work and Pensions (DWP), proposing that broader sharing of Universal Credit data could double the number of households eligible for tailored support, improving overall programme efficiency.

Key Statistics

• Average household water bill set to rise by £133 annually from April 2025.

• The number of households relying on social tariffs expected to grow from 1.6 million to 2.2 million, potentially reaching nearly 3 million by 2030.

• Of 2,934 matched households in arrears, 42.1% (1,234) were eligible for social tariffs but not claiming them.

• Auto-enrolment in March targets 1,234 households, saving each an average of £316 annually, totalling over £390,000.

• April auto-enrolment could include an additional 345 households (total in arrears), increasing the annual savings to £664,000.

• Including households at risk of arrears, potential auto-enrolment could benefit 2,203 households, saving over £933,000 annually.

• 27,621 households in Richmond and Wandsworth claim means-tested benefits; 13,142 (47.6%) are eligible for WaterHelp support.

• Match rate for households in arrears to benefits data: 24.4%; at risk of arrears: 9.4%.

• 668 households in council tax arrears also in or at risk of water arrears, indicating need for integrated debt support.

• Only 45% of Universal Credit claimants are covered by local authority benefits data, revealing gaps in data sharing.

• Over 20,000 households claim Universal Credit but are not captured in council data, representing a substantial outreach opportunity.

• Match rates for household address data improved to 87.6% from Thames Water and an additional 7.1% via address matching tools.

Key Discussion Points

• The expected increase in household water bills necessitates proactive and scalable support mechanisms.

• Auto-enrolment driven by data matching enhances targeting precision and reduces administrative costs.

• Current outreach and application processes suffer from low awareness, suggesting a need for better communication channels.

• Data sharing agreements, with access to benefits data, hold significant potential to improve social tariff uptake.

• Households eligible for support are often not claiming due to awareness or application barriers, which auto-enrolment can address.

• The current limitations in data sharing, notably around Universal Credit, restrict the full reach of support programmes.

• Implementing auto-enrolment could act as a preventative measure, identifying households before they fall into arrears.

• Broader data access could enable support for households beyond those already in arrears, including those at risk.

• The pilot underscores the importance of integrating data from multiple sources (benefits, water usage, council tax) for effective targeting.

• Government and DWP policy adjustments, notably increased data sharing, could dramatically scale support efforts.

• The approach offers a blueprint for water companies and local authorities to collaborate and help vulnerable customers more efficiently.

• Further research is needed to validate predictive measures and develop early intervention models for arrears prevention.

Document Description

This article reports on a pilot study conducted in partnership with Thames Water, Richmond, and Wandsworth Councils, exploring the automation of access to water social tariffs through data-driven identification of eligible households. It examines the potential for auto-enrolment based on benefits data to increase uptake, reduce hardship, and lower administrative burdens. The report details the methodology for matching water arrears data with benefits datasets, the estimated impact of rising water bills, and policy recommendations for expanding data sharing and engagement strategies. Designed for senior managers in financial services and utility sectors, it offers insights into innovative approaches for targeted social support leveraging cross-sector data collaboration.