Assessing a price range for a quote is an important process for business operations at Setpoint and LVS. A correct benchmark and the speed in which this is determined are important factors to win a quotation. To quickly determine the correct price, historical quotations can be used to do a pricing analysis. 

For this analysis, we used a lot of historic quoting data. This data includes the timing of the quote, the quoter, customer details, the amount of the quote and whether or not the quote was accepted. For the first phase of this analysis, we created multiple Tableau dashboards.

In the first place we only created one single dashboard, for the quoters, in which they could see which historical quotes were accepted (and which were not) and use this as an input to come to a good quoting number for the new quote. But during the proces, we also created a customer view and a general management view. Which both have the option to zoom in on different aspects of the quote. All of the dashboard are based on the same data, but is displayed in a different way, which best fits the user. 

Currently, we are working on a second phase of this project, to make the proces even smarter by using Machine Learning.