Case study

Predictive Analytics

Cost Forecasts

The Challenge

Construction projects regularly exceed calculated budgets.

By leveraging historical data it is possible to radically improve the accuracy of price forecasts.

The Solution


1. Input parameter, such as project size and product type are analyzed and matched with a predefined ruleset.

2. A classification model trained 3.5 on historical data identifies necessary cost items.

3. A bayesian algorithm learns the relation between parameter and cost items while accurately modeling uncertainty

No items found.


  • Better decision making based on improved forecast accuracy
  • Transparent forecasts by modeling cost uncertainties
  • Automated calculations speed up the proposal process
  • Continuous improvements by using online learning and human feedback

Further Use Cases

  • Real Estate: Calculating projected home values
  • Healthcare: Predicting the effectiveness of a treatment
  • Advertising: Finding new market segments or forecast the success of activities
  • Customer Service: Predicting your demand and staff need

... and many more

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