Decision augmentation tools for smarter decisions
Smart meter data, subscriber data, extensive historical data, and more – energy companies are the envy of most industries. Yet, low retention, high churn, and soaring acquisition costs create complex choices for utilities. Atlas provides decision support tools for business decision makers and prebuilt AI models for churn, yield, retention, and acquisition that are powered by rich data.
Discover the difference data makes
Reduction in churn
Predict churn and churn drivers, then automate for real time management.
Increase in LTV
Understand and improve tenure using dynamic clustering and uplift modelling.
Improvement in yield
Experiment and optimise all your cross sell, upsell, and loyalty strategies.
AI trained on energy data
Atlas’ AI models use the unique data available to energy providers, along with competitive data collected over multiple years. These models are developed to enhance subscriber lifecycle, optimise acquisition and cost to acquire by leveraging retention and yield indicators, and improve subscriber lifetime value. With accelerated data ingestion and ecosystem connectors, the path to a guaranteed 95% accurate prediction is <60 days.
Pricing and competitive data
Multi-year history of front book pricing, back book pricing, and competitive insights across electricity and gas in the Australian region. Competitiveness ranked and engineered into acquisition, retention, and churn models.
Forecasting and anomaly detection
AI is applied to learn complex, over-time trends, and seasonal changes to predict future business performance across all KPIs and all product and / or audience types. The application of AI allows real-time forecasting and up-to-the-minute prediction on whether KPIs will be achieved.
With Atlas’ Horizon capability, KPI insights are explained at a population level to provide strategic insights across acquisition, retention, and churn. Each model is explained in the context of revenue and opportunity, causes or reasons, and customer clusters that can be targeted.
Small-scale testing to validate AI insights and treatments to improve the probability of success. Experiments can be easily run through a client’s digital channels to elicit a statistically reliable prediction of success before committing to budgets and resources to deploy large-scale treatments or optimisations.
Atlas’ Flight Path capability allows for the automated management of a KPI so that many strategies can be simultaneously ‘on’. The application of AI allows self-learning so continuous improvement is automated. Uplift modelling is applied for maximum impact.
Prebuilt connectors with major cloud ecosystem vendors to automate experiments and optimisations.
47% reduction in churn over 18 months
“I didn’t believe it was possible to have this much impact on churn”
– Leading Australian MVNO
+50% increase in tenure
“With an average tenure of 23 months, our prepaid base looked more like a post-paid base.”
– Australian MVNO
+1 products per customer held by 49% of customers
“Massive impact to the bottom line.”
– Australian MVNO