Simply Energy leads the way with saving customers before they churn 

Reducing customer churn has always been a major priority for energy retailer, Simply Energy. Given the fiercely competitive market in this country, all energy retailers would attest to being serious about reducing churn. What makes Simply Energy a standout, according to Andrea Bernard, General Manager for Marketing and Sales, is they have recognised that their previous strategy based on churn propensity modelling was failing short. 

Andrea explains how Simply Energy innovated their approach to customer churn reduction and what this has meant for the business as well as their customers.

Andrea Bernard

General Manager Marketing & Sales

Simply Energy has been making energy simple and affordable for Australian households and businesses since 2005. Simply Energy is proudly backed by ENGIE – one of the largest independent power producers in the world and a global leader in the transition to a zero-carbon economy. 

As General Manager for Marketing and Sales, Andrea looks after all of the customer interaction points and channels for residential and small business gas and electricity as well as new energy products like solar, batteries and EVs. Ultimately, it’s Andrea’s responsibility to ensure Simply Energy’s customers are engaged and remain loyal to the brand. She comments,

The challenge:
How to cost-effectively reduce churn in a highly competitive market

Since the implementation of contestability in most areas of eastern Australia between 2007 and 2016, new entrants have flooded the energy retail market. There are about 70 energy retailers authorised to operate in each of the major states.  

According to the Energy Council of Australia, Australian energy retailers are churning an average of  20 – 25% of their customers annually. On average each customer is worth $1,576* pa and there are roughly 8.8 million households in Australia. The revenue walking out the door each year is around $3.5b

Simply Energy has always had a focus on customer churn reduction. Each year they would devote resources to building churn propensity models. Over time Simply Energy identified a range of churn indicators such as low NPS scores or high bills and could accurately predict the level of customer churn.

Andrea observed that these propensity models had no way of identifying at-risk customers, rather they grouped whole clusters of customers and treated them as one homogeneous group, which significantly limited the effectiveness of treatment strategies to save customers.

The solution:
AI and machine learning platform from SmartMeasures

When Andrea was presented with the SmartMeasures solution she felt it may be a good fit for Simply Energy given the maturity of their propensity models. SmartMeasures machine learning technology not only identifies customers most at risk of churning, but it also segments and groups customers so, fit for purpose save treatments can be applied. The SmartMeasures technology goes beyond an academic exercise of producing volumes of information that largely sit on shelves, by enabling businesses to constructively use that information to save customers.

SmartMeasures first AI engine processes thousands of signals across various data sources within the business.  No matter how ‘messy and disconnected’ those data sources may appear to be, the AI is able sort it and build an accurate picture of an individual customer’s health and the risk of them churning.

It was not the churn prediction capabilities of the SmartMeasures that delivered the results Simply Energy wanted, but the second AI engine that groups at-risk customers and guides the development of tailored and effective treatment plans. These groups are based on different aspects about the customer’s behaviours, profitability, adoption of digital services and so on.

The grouping can also be dovetailed into existing segmentation models businesses have already identified, thereby optimising their business data, and maximising the benefits from marketing spend on such models. Moreover, both the SmartMeasures AI engines continue to learn over time opening up the opportunity for ongoing improvements in a business’s capability to save customers.

The implementation:
Not all treatment plans are equal

An initial trial project was established that included 50,000 active customers. The project was implemented in three distinct phases:

• Phase 1: Collecting data and training the AI to accurately predict customers at risk of churning. Blind test results showed AI prediction achieved 83% accuracy.
• Phase 2: Develop a range of treatment plans to retain ‘at risk’ customers
• Phase 3: Run treatment plans for ‘at risk’ customers with control groups to measure effectiveness. Customers were treated with four different treatment plans using email and SMS over 16 weeks.

Upon reflection on the implementation of SmartMeasures and how people felt about the solution, Andrea observed that:

Implementing treatment of at-risk customers was a new operational activity and cost to the business which required some re-thinking. One of the major hurdles for Simply Energy to overcome was the amount of resources required for treatment plans based on phone calls or physical mail. In many cases the costs associated with making phone calls or sending letters would eliminate any ROI obtained from preventing a customer from churning. Further efforts on designing and running treatment plans focused on SMS and email for communication.

The SmartMeasures team are equipped to utilise any channel of communication for the delivery of the treatments and adapt to the needs of the customer. Another key aspect of the SmartMeasures solution is its ability to identify topics or relevant reasons for contacting customers. 

It was found that using repurposed marketing material in the communications actually resulted in increased churn. If customers are dissatisfied or are considering leaving, a generic brand message will only alienate them further.