Big Pricing Data

Big Pricing Data

Business-wise, when faced with large data sets of price information, some of the best practices include:

  • Focus on key performance indicators: being a great communication tool in themselves KPIs can also help condense vast amounts of data into initial insights. With the right tools you can then later drill into the underlying data
  • Clarify what you want to achieve: is it about analysing pricing data, or is it about optimisation or simulation of future pricing? Especially with large amounts of data the objective is important to keep in mind.
  • Start with manageable subsets of data to begin with, and prove value to the organisation.
  • Get the right tools: with millions or even billions of price elements to analyse and simulate you need a strong IT tool. Excel can in many cases still be used for some of the front-end analysis, but the crunching is best left to a dedicated tool.

From an IT perspective the key to success in pricing analytics and pricing simulation of large quantities of data is scalable architecture. Stratinis Pricing Suite uses a proprietary simulation engine that can be installed in parallel to multiple instances and thus can calculate large quantities of data.

At Stratinis we have been working with Big Pricing Data for a long term. Some examples include:

  • Global consumer goods manufacturer: analysis and simulation of price waterfalls across 40000 products, 60000 direct customers, 40 countries and 80 different discount types
  • Manufacturer of medical devices: 300000 customers, 20000 products, 35 countries
  • Electronics manufacturer: price planning across 90000 products, sold in 80 countries
  • Online trading platform: 300000 prices quoted every week
  • Software vendor: 2 million product configurations (PC, Mac, Linuc etc)

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