Better pricing with Machine Learning for a global industrial organization
Better pricing with Machine Learning
A global industrial organization wanted to acquire a better understanding of their B2B sales process. They wanted to investigate the main factors affecting the probability that a B2B client would accept a proposal. In addition, they wanted to know if they could find an optimal quotation price for each individual opportunity, taking into consideration the expected profitability.
We helped them understand:
* What is the probability of proposal acceptance per sales opportunity?
* What is the best price considering expected profitability?
* What factors affect quotation acceptance?
The outcome of the project is a sales assistant tool currently used by the client's sales team.