For any energy retailer in 2023, how and where you deploy AI will shape the future of your customer operations. If AI is not at the core of your technology strategy, it may be time to think again. With the pace of AI development, companies that don’t will fall behind within months and struggle to catch up.

Savvy operators are already making significant gains from using generative AI.

This month, Octopus Energy founder Greg Jackson revealed that 44% of the company’s customer emails are now answered, at least in part, by generative AI. And his customers are happy with the results, with a reported 80% satisfaction rate from AI-generated emails vs 65% for those written by staff alone.

An impressive performance for a program still in its early stages.

But this is just one example of AI’s potential for energy retail. With a relevant use case in mind, and the right expertise in your corner, AI can offer incredible advantages across your customers’ journey.

Here are three more use cases that you can start tackling now with AI.

1. Stop churning customers

Customers churning at the end of their contracts is an all too familiar revenue drain for many energy retailers. 

When your customers reach the end of a contract, they’re left with a choice: renew or look to your competitors. Their decision can often come down to what kind of experience they have when dealing with your customer service team.

Training an AI model to match your best-performing customer service agents with customers who are most at risk of churning is an ideal solution. This approach saved a company we work with over £10m in revenue that would have otherwise been lost to end-of-contract customer churn. 

The model we developed for them classifies incoming customer calls as ‘easy’ or ‘hard’ based on historic customer data. This includes a range of factors, from the value of their contract to their payment history.

The same model analyses the historic performance of every customer service agent, earmarking those who have a higher success rate in retaining customers. By ensuring these high-performing agents deal with the ‘hard’ customer calls, customer churn has been dramatically reduced.

This advanced AI model gave our customer a clear competitive advantage. Not long ago, this approach would have only been available to advanced operators. But today, every energy retailer can maximise their customer service team and retain more customers.

2. Truly personalise your marketing and comms content

Delivering personalised content is a promise that digital marketing made decades ago. With the right generative AI solution in place, energy retailers can make good on that promise.

Customer data can be used to train an AI solution to produce tailored content for each individual. Solutions such as GPT-4 from our partners OpenAI can supercharge your content production. Anything from personalised recommendations on lowering bills, service updates email copy, choosing which offers to show them, and more.

The real magic then comes from looking at the results and feeding your best-performing content back into your generative AI model to produce new, optimised versions on demand. 

For example, if one landing page proves to be a hit with a particular customer segment, you can now roll out four or five new variants on that ‘winning’ theme in record time.

3. Train superstar call centre teams

Using an appropriately-trained large language model (LLM) you can analyse the transcripts of your team’s customer calls. And get insight into what they are good at and where they need to improve.

This kind of feedback loop can guide your staff training and help get new recruits up and running faster. By analysing their performance data, AI can identify areas where each trainee needs improvement, customising their training to focus on these areas. 

AI can also simulate real customer interactions, mimicking a wide range of potential scenarios, such as organising a smart meter installation or dealing with a challenging complaint.

In other words, offer a safe environment for your trainees to make mistakes and sharpen their service skills on an AI customer. Long before they ever speak to a real one.

This is only the beginning

These are a few of the more relevant emerging use cases for energy retailers. And as generative AI continues to proliferate, the opportunity in this space will expand quickly.

There are a number of factors to consider to make sure your AI-powered solution hits the mark. Our CCO, John Gibson covers these in more detail in his guide to generative AI in an enterprise context here.

But a well-trained AI solution applied to the right kind of problem breeds an incredible opportunity to eat your competitors’ lunch and drive revenue for your business.

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