Mathematical steering of Pricing: The 3d Generation of Data Science
From Data Analysis, to Predictive Analysis and finally now 3d Generation Data Science: Impacting human behaviour in daily business operations. The data science solutions of MRD enhance negotiations of sales forces, steer for churn improvement and optimize share.

Adding mathematics to pricing is a leading edge proprietary technology of MRD within the pricing arena. It provides real-time calculated target and predictive prices for each new deal or new quote.
Both in retention, acquisition as well as retail sales channels the predictive price represents the price optimum or willingness to pay per deal. This is used for directing and steering sales forces towards a price optimum and to compose price guidelines with risk and value optima.
Especially large sales forces with a transactional pricing character and pre-defined product offerings are benefitting of this complementary and new domain of data science.

Our Solutions: Business Insight

Price Driver Discovery
The key in predictive pricing is the determination of price and profit drivers. In a series of meetings both business as well as data scientist are researching, profiling and validating drivers in data sets. These approaches reveal discoveries that could not be seen by experience or judgments.
Data Analysis
The journey of Data Science can only start with Data Sets. The structuring and completion of data sets often requires in-depth data analysis. Not only the Extract, Transform and Load process requires expertise but cooping on a smart yet pragmatic way with the incompleteness and errors in data sets requires experience to complete this in a fast and effective way.

Our Solutions: Profit Improvement

Predictive Pricing Modelling
The mathematical modelling of the Price Drivers of a data set result in advanced algorithms. These algorithms are the essential engine to operationalize the predictive pricing.

Our Solutions: Business Expansion

Churn Optimisation
The essential parameters that are of influence to Churn can be detected and modelled into an algorithm in order to find the optimum set-point or targets on these parameters. This Modelling to optimise Churn parameters has led to remarkable churn improvements.