Real-time insights for improved customer retention
Data-driven strategies in the highly competitive energy sector bring new agility to the decision-making process. In a sector where customer loyalty is low and the purchase decision usually comes down to price, process changes need to be understood and implemented in real time.
A customer facing service sector like the energy industry generates a significant amount of data on a daily basis. If all these data lines are gathered, organised and modelled correctly, they can be used to predict the behaviour of thousands of customers to drive significant service improvements and cost savings.
Data science is changing fast and can offer tangible benefit to the energy industry. We operate at the leading edge of the industry and can provide guidance to the energy sector on best practice.
Predictive data modelling informs agile, fact-based decision making.
- Tangible return on investment: improved debt recovery systems through data modelling of predicted customer behaviour.
- Real time business insights: allow fast, agile and accurate decision making based on predicted outcomes.
- Reduced customer churn: data-driven decisions allow precision targeting of red-flagged customers with relevant marketing messages.
- Talent acquisition strategies: large-scale hiring models attract the best in talent based on objective, data-led frameworks.
- Capability development: upskill staff with knowledge of advanced data modelling to create state-of-the-art analytical models.
Case study: Digital transformation (energy / utilities)
Challenge: We were engaged by a large UK energy supplier with over 3 million customers and multi-billion dollar revenues to help start and grow their analytics capability.
Solution: We designed and built a production-grade data lake and constructed ELT pipelines to create data assets by integrating large multi-billion row datasets, from diverse sources and formats. We built advanced machine learning models on top of these data assets to deliver value via analytical insights, proofs of concept and visualisations across the business, from front line managers to C-level stakeholders. We also advised on appropriate database schemas, software, and technical implementation of analytical solutions for the data lake.
Technologies: Python, Java, supervised and unsupervised machine learning, deep learning, h2o.ai, Spark (EMR), AWS
Result: We built a cross-functional data science team from scratch to deliver enterprise-level data-driven solutions, enabling understanding of the customer journey and improvement of operational performance and resilience.