Optimizing the closing rate with machine learning

In the world of entrepreneurship, there are three key challenges that can hinder the path to sustainable success. These three problems can limit the profitability of companies of any size at any given time:

There are numerous solutions for all three problems that can get your business back on track. In this Insight, we present a data-based approach that solved a stubborn sales problem for one of our customers.

Using classification algorithms from the field of machine learning and XAI (Explainable Artificial Intelligence) methods, we carefully analyzed sales data for patterns and interaction effects. This allowed us to identify key indicators such as sweet spots of response time windows that were most likely to lead to successful deals.

This data-based approach makes it possible to probe the decisive steps in the sales process in order to ultimately gain control over new customer acquisition and have more planning security. The results of this machine learning application were impressive. The close rate increased by 15% just a few days after implementation, so that a direct link with the now more effective sales team could be established.

This use case shows how machine learning not only explains patterns, but also provides concrete, measurable recommendations in sales – a key to sustainable success and fewer sleepless nights.


Dr. Kay Stankov
Head of Data Science, Ainovate GmbH