Lead scoring in automotive CRM

he automotive industry faces numerous challenges, including an inefficient sales process, specifically the conversion of inquiries, known as leads, into customers. There are relatively fewer vehicle dealers compared to the number of customer inquiries about purchasing a new vehicle. A dealer can respond to only a portion (approximately 50%) of incoming leads on the same day, and this selection is made randomly, resulting in inefficiencies in the sales process. In the selected company, this inefficiency results in a conversion rate of 27%, meaning 73% of business opportunities and potential revenue remain unutilized. The primary goal of the project is to increase the conversion rate by 5–10 percentage points, which will lead to an increase in revenue by 15–37 percentage points. The project achieves this goal by applying machine learning to identify inquiries with the highest likelihood of conversion. This approach allows dealers to allocate their limited time to responding to the right leads, thus eliminating the random selection approach and enhancing their efficiency, which in turn increases the conversion rate.