In moving towards an ‘always on’ style of customer experience, nearly every utility company is now focusing their efforts into AI generated support, in particular chat bots. A study conducted by Salesforce revealed that 68% of users prefer chatbots due to their quick response time, mostly down to their 24/7 nature (as implemented by Scottish Power) and ability to resolve simple customer challenges such as checking their bills, reporting meter readings and asking for energy saving tips.
They also have internal benefits to the industry. The vast amount of customers and calls means that chatbots are a much more efficient and cost-effective way to serve a massive customer base. However finding the right solution and strategy for bot vs. human, particularly for those from vulnerable backgrounds, needs to be clearly identified when developing Customer Experience strategies.
The integration of personalised services in the utilities sector has fundamentally transformed the way companies interact with consumers, optimising experiences, enhancing efficiency, and fostering a stronger customer-provider relationship.
Traditionally, utility services were often perceived as standardised, one-size-fits-all offerings. However, with evolving consumer expectations and technological advancements, personalised experiences have become an essential aspect of utility service provision.
EDF Energy employs AI-powered systems to analyse individual energy usage patterns, allowing them to offer bespoke advice to customers on reducing consumption and optimising energy efficiency. By providing personalised insights, they empower customers to make informed choices about their energy usage.
Octopus Energy integrates AI-powered voice assistants into their services, allowing customers to manage their energy accounts and get real-time updates using voice commands. This seamless integration of AI with smart devices simplifies interactions, making utility management more accessible and convenient.
So how do you achieve true customer personalisation? First, you must get your basics right: achieving genuine customer personalisation hinges upon a deep understanding of customer data. It's the cornerstone upon which tailored experiences are built. By meticulously gathering and analysing customer data—demographics, behaviour, preferences, and feedback—businesses can unravel unique insights into individual preferences and needs. This comprehensive understanding allows companies to craft hyper-personalised experiences, offering tailored recommendations, bespoke services, and targeted communications that resonate with each customer. However, the true essence lies not just in data collection, but in the adept utilisation of this information to create seamless, anticipatory experiences that foster a profound sense of connection and satisfaction for every individual user. Ultimately, the journey towards authentic customer personalisation involves not just data comprehension but also the art of weaving this knowledge into meaningful, individualized interactions that resonate deeply with each customer.
The power of data predictive modelling, especially concerning Predictive Maintenance, is a transformative force driving enhanced reliability in the utilities sector. By harnessing sophisticated algorithms and machine learning techniques, predictive models analyse vast streams of data sourced from sensors, historical maintenance records, and equipment performance indicators. These models forecast potential equipment failures or downtimes, allowing utilities companies to proactively intervene before issues escalate. This proactive approach minimizes disruptions in service, optimizes asset performance, and reduces maintenance costs significantly. Predictive Maintenance empowers suppliers to transition from a reactive to a proactive operational paradigm, ensuring the continuous and reliable delivery of essential services while maximizing asset lifespan and efficiency. As a result, this adoption of data-driven predictive modelling bolsters reliability and also paves the way for streamlined operations, cost savings, and, ultimately, enhanced customer satisfaction within the sector.
National Grid has started utilising AI algorithms to monitor and predict potential faults in its power transmission networks. By analysing data patterns, they can foresee potential disruptions and swiftly address issues before they escalate, ensuring reliable energy supply across the country.
But once again, predictive modelling comes down to your data quality and integrity. The essence of these models lies in the precision and reliability of the input data. High-quality, trustworthy data ensures that the predictive algorithms generate meaningful and actionable insights. Inaccuracies, inconsistencies, or incomplete datasets can significantly impair the efficacy of predictive models, leading to flawed predictions and erroneous outcomes. Therefore, maintaining data integrity by validating, cleaning, and ensuring consistency across datasets becomes paramount.
If you're interested in hearing more about our work around AI innovation in the utilities sector, please email us at Transformation@TransformUK.com and one of our experts will be in touch to answer your questions.
