X Education is an online education company that provides courses to industry professionals. The company utilizes various marketing strategies to attract potential customers to its website, including advertising on search engines like Google. Visitors to the website may browse courses, watch videos, or fill out forms to express their interest in a particular course. Those who provide their contact information, such as email addresses or phone numbers, are considered leads.
The company's sales team attempts to convert these leads into paying customers by communicating with them via phone, email, or other means. However, the lead conversion rate, which is the percentage of leads that become paying customers, is currently low, at around 30%.
To improve the efficiency of the lead conversion process, X Education wants to identify the most promising leads, or "hot leads," who are more likely to become paying customers. The company is seeking assistance in building a model that assigns a lead score to each lead based on their likelihood of converting. The goal is to increase the lead conversion rate to around 80%.
The lead scoring model would enable the sales team to focus on the most promising leads and tailor their communication efforts accordingly. This would result in a higher conversion rate and increased revenue for the company.
Develop a logistic regression model that assigns a lead score ranging from 0 to 100 to each lead, based on their characteristics and behavior. This score will help the company identify the most promising leads for conversion. A high score would indicate that the lead is "hot" and has a higher likelihood of becoming a paying customer, while a low score would indicate a "cold" lead that is unlikely to convert.