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How we Define an Operational Model and the Size of a Sales Force

We support our clients in the redesign and optimization of their existing sales processes, enabling their commercial teams to save time thanks to Machine Learning and task automatization in order to better split the commercial effort depending on the prospects’ value.

Redesign and optimize existing sales processes. Our solid experience with sales functions in all industries, especially B2B, enables us to better understand the needs of our clients’ sales teams and identify their pain points to improve existing processes.


Equip sales teams with the right tools. We find out the needs of field teams to select the best tool, thanks to our exhaustive knowledge of the sales CRM solutions market and we build this tool with an agile approach in collaboration with the sales teams.


Automate clients’ low value-added tasks. Thanks to Machine Learning and automation, we can reduce the time that sales teams dedicate to tedious and low value activities, such as updating customer data in their CRM (we own, among other tools, an AI solution capable of automatically cleaning CRM databases), segmenting their market, scoring customers/prospects or detecting new opportunities.


Sizing sales teams and allocating sales effort according to the prospects’ value. Our Machine Learning algorithms will enable you to calculate a score for each of your prospects. Our Data Visualization tools will facilitate decision making by helping teams to reprioritize prospects according to their score.

Our Work in the Consumer Products Sector

Find out how we helped a leading consumer product company manage a Data Science project for their sales forces.

While the Data Science teams of this consumer product leader had developed a platform allowing innovative use cases to automate the recurring and time-consuming activities of their sales forces, the deployment of this platform within the various international subsidiaries did not succeed. This is why Sia Partners was appointed; in order to improve the management of these Data Science projects, starting with a diagnosis of the existing processes, the identification of projects to be pursued in order to highlight the business benefits of this platform, then convincing and supporting subsidiaries in its use. In collaboration with local IT and Business teams, we were able to monitor the results of the deployment of this platform through KPIs.