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Revenue Analytics is a tech-enabled consulting firm that helps some of the world’s biggest companies make their biggest revenue decisions – like what to charge, what to stock, and what to promote when.

The challenge is that these questions can have millions of variables and many unknown outcomes. How do you eliminate the unknowns so you can increase revenue without increasing risk?


Our team dedicate her best science and math resources in order to complete this project successfully.

When client came to us we didn’t have ready to use solutions, formulas or prototypes. We’ve created, experimented and developed algos and formulas together.

The project got two biggest challenges:

  • using millions of rows of data about company sales to generate actionable reports and summarise it all for company analysts
  • make complex graphs and UI work fast and look slick. We used 2 different charts libraries to accommodate most of 25+ app graphs

Our team successfully developed:

  • More than 25 graphs and complex charts
  • Optimised performance with data mining and Python Pandas techniques and tools
  • Heavily customised D3.js widgets to meet project requirements

Key Technologies used:

  • Python and Django web framework
  • PostgreSQL databases
  • Celery for background tasks
  • React.js for front-end app
  • D3.js
  • Python Pandas

More Screenshots:



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Posted on

April 22, 2018