Turning data into positive experiences for energy retail customers

Retail energy providers collect and store a tremendous amount of data about their customers.

This data holds the key to understanding each and every customer and how they truly feel about the company. It can show you which ones are happy, which ones are unhappy, and which ones are about to say good-bye.

Unfortunately 80% of this data, is left untouched or inaccessible across multiple and siloed systems. Very little of it is used to understand the customer experience nor provide insights on how to improve it. AI and customer churn analytics, however, allows energy retailers to tap into this data and obtain the necessary insights to offer highly personalised customer experiences.

What can your data tell you about your customers

People form opinions about the experiences they have with your company on a collection of different factors. The more you can understand those factors the deeper the insight you will gain into the experience of every customer and their sentiment towards the company.

Your data holds signals that when deciphered correctly, will highlight what factors are negatively or positively impacting the experience of your customers. Insights gleaned from your data can expose the frictions or seams in the customer journey that lead to frustration and dissatisfaction. It will also reveal the issues that explain why customers are leaving.

Customer churn is a major challenge for every energy retailer in Australia. Those who can develop affective personalisation and churn reduction strategies have the opportunity of saving millions of dollars in lost revenue by increasing the lifetime value of each customer.

Knowing your data

The data you hold about your customer can be placed into two categories:

  1. Operational or transactional data that stores information about how the business is delivering its services. This includes:
  • Service connection and onboarding data
  • Service orders
  • Recent sales engagement
  • Outages that have impacted the customer
  • Customer age and length of tenure
  • Average close time on service cases
  • Service level breach
  1. Behavioural data which highlights what actions customers are taking that might give us a measure of how they are feeling (their sentiment). This includes:
  • Customer not paying on time
  • Method of payment
  • Calls or emails to contact centre
  • NPS (Net Promoter Score), CSAT (Customer Satisfaction) or CES (Customer Effort Score) survey responses
  • Unsubscribing from Marketing
  • Meter data request
  • Any past compensation
  • Self Service/ My Account activity

These data sources sit across multiple systems that are managed by different departments within the organisation. Even though marketing, billing, service, and operations may all function separately, customers interact with your brand, not your organisation. They should always encounter a single seamless interface no matter where they may be in their journey.  Customers value positive, seamless and highly personalised experiences.

By combining these data sources and listening for the right signals, we can obtain a more extensive measure of customer happiness.  A customer that is regularly slow to pay could be having financial difficulty or their slow payment could be a signal they are dissatisfied. If you combine this signal with the amount of times they have sent an email or called the contact centre, you may find this customer is dissatisfied and hence a churn risk rather than a financially struggling customer.

We often focus on just one or two areas of the business to measure customer experience when in fact everything they do with our products and every interaction they have with the company will impact how they are going to feel about us.

Creating seamless and personalised experiences

Why try to win back dissatisfied customers after they’ve left when you can predict the risk of churn and address their issues before they leave?

Energy retailers have the data that can power the personalised and omnichannel experiences that customers expect. With this data they can understand who their customers are, how best to engage them and how best to retain them. The longer they can keep customers the more revenue and profit they will generate from increasing CLV (Customer Lifetime Value).

If you need help to get the most value from your customer data please contact Libby Dale [email protected].

Read our article Why AI and Customer Churn Analytics is a game changer for energy retailers