Updated: March 15, 2024
The lowest-performing sector is the verdict given to utilities in the UK by recent research of Utility and PayPal.
Recent data from the UK Customer Satisfaction Index (UKCSI) as of July 2023 highlights a significant downward shift in customer satisfaction within the utilities sector, with scores falling from 74.1 to 69.5.
This significant drop places the utilities sector at the bottom of the satisfaction rankings, 7.1 points below the national average across all sectors.
In detail:
- The energy sector experienced a notable decrease in satisfaction, primarily due to significant price increases, with a decline of 5.3 points bringing it down to 67.9.
- The water sector, although performing slightly better, also saw a decrease in satisfaction by 3.5 points to 72.6, influenced by incidents related to storm overflows.
Jo Causon, CEO of the Institute of Customer Service (ICS), suggests that utility companies should prioritise digital enhancements to boost customer satisfaction.
At MaxBill, we realise the importance of customer loyalty in the utility business. That’s why we came up with a customer churn prediction model. It identifies the customers about to leave before they announce their decision and provides recommendations to make them stay.
Keep on reading as we dive into the ins and outs of such models and help you understand if they will work for your specific business case.
ter that draws your attention. Without further ado, let’s get started.
What is the Churn Prediction Model Developed by MaxBill?
MaxBill’s model was initially crafted for the telecom sector. The model was initially crafted for the telecom sector. Further, it evolved into addressing the challenges posed by stiff competition and the trend of customers switching for better deals.
Today, MaxBill’s churn prediction model is versatile and serves multi-service providers, including telecommunications, internet, TV, and multi-utility companies. It comes as a stand-alone solution, besides MaxBill’s modern utility billing software.
MaxBill’s churn prediction model is not an out-of-the-box solution. It’s a testament to how MaxBill provides machine learning as an additional service, catering to the unique needs and requirements of each business.
This approach recognises that the model should align with the specific use cases of the business, rather than expecting businesses to adapt to pre-existing churn models. MaxBill can factor in the distinct nature of each business, leveraging their access to data to customise the model.
The Importance of Predicting Customer Churn
Drawing on our experience in the utility sector across the UK, Spain, Northern Ireland, and Eastern Europe, we’ve pinpointed four key challenges utilities face today and, consequently, why churn prediction can be a game-changer for Utilities:
1. Financial Performance: Losing customers harms financial stability and the capacity to fund operations.
2. Customer Acquisition Cost: It’s seven times more costly to attract a new customer than to retain an existing one.
3. Operational Strain: Increased customer churn leads to higher call center loads, raising costs and reducing loyalty.
4. Regulatory Compliance: The need to meet regulatory standards while ensuring customer satisfaction is a delicate balance.
Additionally, the oversight from consumer watchdogs, such as Ofgem, poses a risk of rating downgrades and reputational damage, highlighting the critical role of exceptional customer service.
MaxBill’s Churn Prediction Model in Action
MaxBill’s model is built upon an impressive array of over 50 parameters, meticulously selected for their impact on customer churn prediction (CCP). The method features:
Seamless Integration: the churn model for utilities seamlessly integrates into a client’s system through a responsive API. This integration allows for the analysis of data from any period in time, ensuring businesses have access to the most relevant insights.
High Accuracy: Churn modelling boasts high accuracy in predicting customer attrition thanks to utilising XGBoost and Flask, a powerful technology stack.
Local Interpretability: Predicting churn incorporates the Shapley value mechanism, enabling local interpretability. It pinpoints which features have the most significant impact on churn and which have a lesser influence.
Customisable Parameters: The churn model offers a range of parameters that can be tailored to suit the specific needs of the industry and the client. Flexibility is key since the model’s efficacy hinges on its adaptability.
Anticipating the Future: ‘What-If’ Scenarios in Predicting Churn
One of the churn modelling’s compelling features is its ability to simulate ‘what-if’ scenarios. The model is trained on specific churned customer data of specific businesses, each having particular parameters.
With the system, companies can explore how changes in parameters affect attrition. For example, a customer changing to a basic internet tariff with a slower speed reduced the probability of churn from 53% to 6%.
It’s pivotal to mention the role of automation in proactive churn prediction. Once the system detects a customer or group at risk of churn, automated churn prevention actions can be initiated, eliminating the need for human intervention.
Under the Hood: How the Model Predictive Churn Model Operates
Understanding how the model works is essential for businesses looking to harness its power to retain customers and reduce churn. Let’s delve into the core functions of the churn model, which are instrumental in achieving these goals:
Selecting Contracts with High Attrition Probability: Based on predefined parameters, the model identifies contracts with a high likelihood of churn.
Data Analysis: It draws its insights from historical data, typically spanning 12 months and evaluated over 3 months.
Parameter Determination: The approach allows determining the parameters that significantly influence the possibility of customer churn.
Recommendations and Customer Retention: The system behind it not only identifies at-risk customers but also recommends changes to retain them.
A Glimpse into the MaxBill Churn Prediction Model for Energy & Utilities
Strategic Implementation: Customer Retention Use Cases
Understanding how to effectively deploy a churn prediction model is paramount. Without a clear grasp of how to predict customer churn, even the most sophisticated models can fall short. Let’s explore how our model can be strategically leveraged:
Per-Customer Insights to Predict Customer Churn
When seamlessly integrated with the company’s CRM, the churn prediction engine offers a valuable tool for customer service representatives (CSRs). It provides these frontline agents with deep insights into each client’s likelihood of ending their relationship with our company. Armed with this intelligence, our CSRs can take proactive measures, tailoring their interactions to reduce churn risks and foster lasting client relationships.
Bulk Reporting
The customer churn prediction model generates comprehensive reports on specified dates, pinpointing the list of customers within the ‘at-risk’ category. This data serves as the foundation for targeted customer retention marketing campaigns. This enables organisations to not only mitigate churn but also engage customers with their services and enhance brand loyalty.
Interactive Management
Our model’s capabilities extend beyond mere identification to predict customer churn. It is a dynamic, interactive tool that empowers management to proactively influence customer retention. This tool empowers management to implement new pricing strategies, offer discounts, and launch special promotions. Then, it allows them to assess the anticipated results, taking into account any newly introduced variables in the model.
This versatility is invaluable for crafting tailored offers for specific customer segments while striking the optimal balance between marketing ROI and customer churn, all while maintaining a low false-positive rate.
The icing on the cake for helping to predict customer churn is the automation built into our system. As soon as the model detects a customer or group at risk of churn, the system automatically triggers the preferred churn prevention actions, eliminating the need for human intervention. This ensures that an organisation is always one step ahead in retaining valued customers.
Related:
Proactive Path: How We Deliver a Predictive Model for Utility Churn Reduction
Utility Debt Prediction Model to Protect Revenue and Enhance Customer Retention
Utility Debt Management With Optimised Collection Systems
Are you willing to adopt MaxBill’s predictive model as a separate service or integrate it seamlessly with our utilities billing solution? Contact our experts today to explore how MaxBill can empower your utility organisation to thrive in the competitive market!
Request MaxBill’s Webinar on Innovative Models!
Discover MaxBill’s webinar on how leveraging machine learning and AI can revolutionise utilities and energy management, enhancing operational efficiency, reducing customer churn, and ensuring revenue protection. Gain exclusive insights from industry experts into predictive modelling for optimised operations, tailored solutions, and real-world applications. This is a pivotal opportunity for C-level Utility executives to drive innovation, foster transformative business outcomes, and lead their organisations into a new era of technological advancement and customer satisfaction.
