In the past four years, instability in the utilities market has become the new normal, with suppliers in the UK losing on average £4 billion, according to Ofgem. Meanwhile, Gartner predicts that by 2025, 40% of energy and utilities companies will face a 50% increase in capital demands triggered by resource scarcity and soaring demands. Businesses are in desperate need of accurate and transparent -analysis to enable them to make fundamental financial decisions quickly and effectively.

The good news is that the utilities sector can take proactive steps to stem margin erosion and foster long-term profitability. Digitalisation and crucially implementing sophisticated and granular data analysis are critical to empowering utilities to prevent financial losses, improve business optimisation, and identify profitable new revenue streams.

While the utilities industry is aware of the importance of digital transformation, the reality of implementing this at scale is not so simple. According to McKinsey, “while almost all major utilities are utilising digital, data, and analytics in some fashion, it appears that few executive teams can articulate a cohesive strategy on how a comprehensive digital, data, and analytics platform could provide “best-in-class” outcomes across reliability, safety, resiliency, affordability, and customer experience with no trade-offs.” The problem is that utilities are relying on disparate data from multiple siloed legacy systems to inform critical business decisions.

The role of data in utilities today

Complex data analysis already forms a huge part of the financial planning process for the utilities industry. Retailers will be familiar with the gruelling month-end procedure of assessing profit and risk –with teams of people employed to manage the planning process. However, when they are only ever analysing bad data, throwing more people power at the problem does not resolve the underlying issue.  Insights are only ever as effective as the data you have available, and with utilities relying on assumed customer behaviour and historic trends, the reports are likely to be inaccurate.

Consequently, assumption-led insights are used to inform C-level decisions such as setting the annual budget, which can result in poor strategic decisions that impact the business for years into the future. Emphasis is now placed on forward-looking data to predict usage, manage costs and cash flow and serve existing and new customers.

Power distribution supply chain challenge

The UK power distribution supply chain is considered one of the most complex global markets, comprising of aggregated, multi-counterparty industry invoicing processes, which drives a blended view of purchase costs and renewable subsidies right through the financial process. As industry invoices are not at meter point granularity, it means that the energy retailer must replicate complex network charging mechanisms to derive site level costs.

Lengthy reconciliation processes, further complicated by adjustment factors applied at metering level are set to continue beyond MHHS (Meter Half Hour Settlements). These drive opaqueness in the industry where clarity is required to understand costs more fully but more importantly the impact customer behaviour has on them. The industry charging processes often aggregate data for supplier billing purposes further hindering the ability of suppliers to effectively and accurately assess these costs, at the right granularity.  

For the energy retailers this is a challenge as it means that they never see the “true cost” breakdown of a customer, thus preventing them from seeing which ones are profitable and which they can extract maximum customer lifetime value, and similarly which ones are costing them money and where they’ll need to take further action to correct.

The next generation data solution

Overhauling IT infrastructure and auditing data across disparate systems is a complex and time-consuming task for retailers. Gentrack as a next generation utilities software provider has access to the granular level of customer insight that utilities so desperately need. Every transaction with a customer passes through the system, from the customer agreement, through to invoices, payment preferences, timeliness of collection, demographic data, meter readings and even complaints.

Gentrack’s solution is designed to support our utilities customers by consolidating large and complex data sets into actionable insights.

Gentrack Gross Margin solution

Gentrack Gross Margin solution consolidates fragmented and complex utilities energy market data, providing the true cost profile of an energy customer. This is achieved by taking raw data inputs from the industry and then interpreting and transforming this into a usable and logical form.

Analytics and bottom-up replication of industry processes are applied and or modelled – and with the addition of Gentrack’s core proprietary billing system, trading and forecast data – a gross margin model is created. This combined with Gentrack’s expertise and ML (machine learning) capabilities, consistently optimises this model on an ongoing basis, and all via accessible real-time reports and dashboards.

“Jobs to be done” – collaboration for growth

Finance user

For financial users such as finance managers, being able to view forecasts against actual costs, track YTD performance relative to Business Plan, and derive real-time insight into a segment, product and or customer performance ensures greater management reporting accuracy and potential budget -adjustments.  Providing a single source of financial data enables the entire business to take a cohesive view of the customer and develop strategies that align across finance, sales, and marketing.

Commercial users

For sales users, being able to leverage data-driven approaches to account management, assessing in real time if clients are on the right tariffs, whilst identifying profitable target customer segments ensures a steady sales pipeline for growth. For marketing users being able to target customers via personalised campaigns with new tariffs, products and services enables the retailer to extract maximum customer lifetime value.

For example, for energy B2C consumers who are using more energy than expected based on their average usage and thus causing the retailer to procure more costly energy, being able to incentivise the customer through energy saving tips such as not using energy during peak hours for activities like washing, and similarly offering a new tariff based on new energy usage are benefits that granular customer profitability enable.

Operations users

For operations users this means an accurate and consolidated single source of financial data that is robust and can be used confidently in management reporting. Being able to access real-time data to deliver a snapshot of customer profitability, is the only way utilities can maintain an ongoing 360-degree view of the business in a complex and dynamic energy landscape. Coupled with the continued retailer requirement to adjust data reporting according to changing regulations, this means that relying on historic data to make assumptions no longer suffices.

Summary

Leveraging profitability data for advanced insights enables retailers to get the most accurate view and “true cost” of their customer. By understanding which account is profitable and achieving target versus an account which is costing money, enables retailers to take the next best action to ensure that they remain profitable. As retailers seek personalised data insight into their customers business performance, utilities relying on legacy systems simply won’t be able to keep track of data in any meaningful way.