Dynamic pricing data hub underpining a large energy retailer’s price-setting process

Situation

  • One of Australia’s largest energy retailers, in a highly competitive and heavily regulated energy retail market
  • Complex pricing structures with multiple energy generators and distributors Australia wide
  • A very manual and time consuming price setting process. Not easily auditable leaving the client exposed to regulator scrutiny
  • The price setting process is not easily repeatable or transparent in the current environment

Challenges

  • Being a heavily regulated industry client was subject to extensive audit and compliance checks
  • Price change requirements and deadlines vary from state to state and must comply with regulator imposed pricing limits
  • Lack of governance, processes and robust source data sources across the end to end price setting process
  • Large, complex consumption data set that had not been modelled in a way that supported price change
  • Lack of visualisations and historical data capture to support decision making

Solution

  • Optivia worked collaboratively with client and third party vendors assisting in the strategic design and requirement definition of a new data driven pricing platform
  • Comprehensive assessment of all data sources to determine gaps or data quality issues and integration with strategic data platforms where available
  • Implemented strategic, business user friendly data integration logic for performing initial data migration activities as well as on-going data engineering activities.
  • Created an analytical models to predict customer consumption where the data was incomplete or missing
  • Developed complex data models to transform and aggregate the large volumes of data required for the pricing optimisation process
  • Built several dashboards on consolidated analytical data sets providing the business with relevant and timely data insights

Value Delivered

  • Provided a reliable and repeatable framework for updating/loading data into the new pricing platform which in turn enabled the pricing team to spend more time to focus on pricing optimisation and less time gathering data and questioning data accuracy
  • Enabled greater accuracy in the price setting process through advanced analytics and complex data engineering thus reducing the risk of breaching regulator limits and increasing overall gross margin
  • Significant reduction in data related issues, easily repeatable and auditable data sets that have removed the guess-work from the price setting process

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