Energy retailers need to change their game if they are to maintain profitability and grow their business.
Energy retailers are feeling enormous pressure from new competitors entering the market combined with increasing customer churn rates. Costs to retain and acquire new customers are escalating at the same as energy retailers are under increased pressure to reduce prices.
In this highly competitive and deregulated market, the ability to prevent customer churn is of critical importance. According to research from McKinsey, energy retailers can greatly reduce churn by offering customers far greater personalised experiences. AI (Artificial Intelligence) and customer churn analytics allows energy retailers to harness the reservoirs of data they collect about their customers to offer very personalised experiences.
Personalised customer experiences
Data driven personalisation was pioneered by brands such as Amazon and Netflix, who track customers’ online and offline behaviour to capture valuable information about their preferences, attitudes and willingness to purchase. These companies can draw on a vast store of data about their customers and using advanced analytics, they can train AI algorithms and models to use this information to calculate the likelihood of a new purchase. Offers and promotions are then tailored for individual customers.
Likewise, energy retailers can use their data to track customers’ behaviour, their preferences, and identify triggers or events that may cause customers to churn. Though the energy market is very different from general retail, with a smaller set of subscription based offerings rather than individual products, energy providers have access to far greater information about their customers.
They have years of contractual relationships and behavioural data on energy consumption to draw on. However, according to McKinsey, they often struggle to know how to capture the right data on customer behaviour and generate valuable insights from it.
Yet this data offers tremendous potential!
Changing the game with AI and Churn Analytics
Just Imagine, if your business knew each of your customers at an individual level, knowing if they’re happy or not or knowing if they are considering switching to another provider. Imagine if you could act in real-time and connect with that customer before they leave.
Customers leave countless traces that can serve as triggers and provide clues as to when they may be reachable and receptive to offers or if they are likely to churn. AI and churn analytics can give energy retailers the ability to predict customer sentiment and churn risk, thereby triggering highly personalised treatment plans and promotions in real-time to prevent a particular customer from churning.
There are 3 sources of data which, when combined, are very effective in predicting customer sentiment and churn risk.
- Operational data – how the business is delivering for its customers
- Behavioural data – what actions customers are taking that might give us a measure of how they are feeling (their sentiment)
- External data – There are external data sources that could provide an early indicator that a customer may consider looking around
The potential rewards
Let’s say, on average, each energy customer a company loses costs between $300 to replace. If you are an energy retailer with a customer base of 500,000 and you are experiencing a churn rate each year of 20% that means it costs $40 million dollars to replace those lost customers.
Reducing your churn rate by a quarter means a saving of $10 million each year. Reducing customer is possibly the best way for energy retailers to grow their business and revenue.
To learn more on how churn analytics can save energy retailers $$$$$ please contact Libby Dale on 0400 633 729.
Next Article: Turning the tide on churn with AI, big data and your service team
