Usecase WESP

WESP is the largest Business Intelligence (BI) supplier within the car industry in the Netherlands. They are specialized in analyzing and advising Automotive companies. Thanks to the 8,000 connected garages, WESP has an enormous amount of data at its disposal. This data contains useful information about the lifespan of certain parts per type of car, the expected maintenance costs per year and the average wage component per invoice. Based on data analyses, WESP provides both garages and parts manufacturers with advice.

Challenge

The problem is that car garages charge different prices per type of maintenance/repairment and per car. As a result, customers pay at one garage half of the amount they have to pay at another garage for the same maintenance/repairment. Datacation's data scientists were tasked with calculating a market-based price for each type of car and maintenance/repairment that takes place in the garages in the Netherlands.

Process

The collaboration between the WESP and Datacation teams is a good example of the co-creation that Datacation always strives for. Through close contact between the two teams, the WESP team was able to translate their automotive knowledge into useful input for the Datacation team. Subsequently, the Datacation team was able to apply its theoretical knowledge of statistics in practice.

Solution

The Datacation team has constructed an algorithm that calculates a market-based price for the garages, for every type of maintenance/reparation and for every type of car. This market price is supported by a confidence interval so that garages can decide whether they want to be at the top or bottom of the price level. By applying Machine Learning the Datacation team provides WESP with short term useful insights, but also ensures that the models keep improving in the long term.