Navigating the Shifting Utilities Landscape with AI and Automation

The utilities landscape in the UK has drastically changed in 2020. With COVID-19 increasing residential consumption and disrupting staffing, customer dissatisfaction is on the rise. Consumers have high expectations for a quick resolution, even in a unique global event, and sentiment analysis shows a recent surge in volume of frustration due to long call waiting times.

At the same time, new entrants into the market are continuing to disrupt the big six suppliers. If the current downslide turns out to be protracted, utilities companies could face serious financial stress. Challenger companies have a much lower cost to serve and are able to provide a cheaper alternative. In an industry where brand loyalty is low, price will undoubtedly be a deal breaker as we enter a recession; 49% of customers either switched supplier, changed tariff or compared tariffs in 2019, and this is only going to increase.

With a higher cost base, a newly dictated price point and ever-narrowing margins, the big six suppliers will need to retain their customers to survive, while tackling reduced staffing in their call centers due to social distancing measures. Utilities companies should look to augment their contact center operations with automation to minimize person to person contact, improve customer experience and help reduce their cost to serve.

Digital Self-Serve

The surge in call volumes from COVID-19 highlights limitations on focusing only on human interaction. Tools like voice biometrics and natural language speech recognition are moving chat interactions into the mainstream, and adoption will accelerate post-COVID-19. In fact, 70% of customers now expect a company’s website to include a self-service application.

The concept of digital self-serve is changing the contact center landscape for the utilities industry, ranging from simple website chatbots that answer billing-related enquires, to fully automated personal advisors. Automation tools can also provide agent enablement, assisting customer service staff in accessing appropriate answers more quickly.

Benefits of introducing these tools are numerous. Improved operating efficiencies will result in significant cost reductions – we’ve seen call centers reduce their overheads by 50% – as well as removing barriers for customers. For utilities companies with narrow margins, focusing on digital self-serve is the first step to lowering costs and improving satisfaction levels. Those that have delayed their journey to digitization in the past will need to transform quickly, in order to close the gap and compete in the changing market.

However, for large organizations with legacy systems, implementing new technology isn’t an instantaneous process. Our recommendation is to take a sequential approach, improving the operational configuration and optimizing agent performance first to reduce customer effort and cost. We would expect the journey from the most basic level of self-serve to advanced personal advisors to take 6 to 8 months.

Improved Customer Retention

Keeping existing customers is also critical in a challenging market. When it costs five times as much to attract a new customer than to keep an existing one, increasing retention rates by just 5% could improve profits by 25%.

As well improving customer experience on the front-end, utilities companies can harness technology for retention strategies in the back office. If an organization knows early enough that a specific customer is likely to leave based on their behavior and profile, they can take proactive steps to prevent it – as well as identifying their most vulnerable customers.

The more data about customers can be leveraged, the better. By collecting data from a wide range of touchpoints, including interactions with chatbots, companies can gain a 360-degree view of their engagement levels. This is where analytics and AI can play a transformational role, helping to pinpoint customers who are likely to leave, or those need additional support, and finding the best offers to retain or assist them.

One area of AI that is particularly exciting is going beyond traditional segmentation with Customer Clustering. Clustering is a form of unsupervised Machine Learning commonly used for building recommender systems, targeted marketing and customer segmentation. A clustering algorithm is tasked with sorting a dataset into a set of groups or clusters based on similarities found between samples, helping organizations determine their most valuable at-risk customers to deliver an intervention strategy.

Choosing a Platform

Utilities companies looking to harness AI and automation to reduce cost to serve have a number of tools available to them. One option is to build in-house with a dedicated data science capability; this would usually require a large investment and a longer time to market. Alternatively, there are a range of off-the-shelf analytics tools which take a one-size-fits-all approach.

At Infosys Consulting, we’ve developed a proprietary approach that helps you realize benefits in less than 90 days. Contact our experts to find out how we can help you better navigate the shifting utilities landscape.