Optimization of the completion rate through machine learning.


In the dynamic world of entrepreneurship, companies face challenges that can affect their profitability. One of these problems is the classic sales problem: highly interested customers from the right target group, but too few deals. One of our agency clients in the marketing sector was also aware of this challenge.
To solve this problem, we took a data-driven approach. By applying classification algorithms from the field of machine learning and Explainable Artificial Intelligence (XAI), we carefully analyzed sales data for patterns and interaction effects.
Our focus was on identifying key indicators, in particular sweet spots of response time windows in the sales process. This enabled us to derive precise recommendations for action in order to optimize the closing process.
The insights gained were quickly implemented, allowing the sales team to control the decisive steps in the sales process in a targeted manner. This led to more control over the acquisition of new customers and increased planning security.
The results speak for themselves. Within a few days of implementation, the closing rate increased by an impressive 15%. This direct correlation highlighted the effectiveness of the now more precisely managed sales team. Our data-driven machine learning process went beyond pattern recognition and delivered concrete, measurable recommendations. This use case demonstrates how machine learning not only offers insights into patterns, but also provides the key to sustainable success and fewer sleepless nights.


Would you like to learn more about the opportunities of process optimization through AI? We would like to invite you to a non-binding initial consultation and show you further possible applications for your company.