Analyzed leads data for ExtraaLearn, developed a machine learning model to predict lead conversion, and provided actionable insights. Recommended enhancements for lead engagement, marketing optimization, and targeted messaging. Deployed model for efficient resource allocation, improving conversion rates and overall marketing strategy.
The EdTech industry has experienced significant growth, particularly during the COVID-19 pandemic, as online education becomes increasingly preferred over traditional methods. ExtraaLearn, a startup in this sector, generates a large volume of leads from various digital marketing channels. The challenge is to identify which leads are most likely to convert into paid customers, enabling more efficient resource allocation.
In this project, I analyzed leads data for ExtraaLearn and developed a machine learning model to predict lead conversion likelihood. I identified key factors influencing lead conversion and created detailed profiles of high-converting leads. Additionally, I provided actionable insights and recommendations to enhance lead engagement, optimize marketing channels, improve profile completion rates, and target specific occupations more effectively. These recommendations included deploying the model within the CRM system, ensuring continuous improvement through regular feedback, and optimizing resource allocation based on model predictions. These strategies aimed to improve ExtraaLearn's marketing effectiveness and overall conversion rates.
Mbaaoum_Lead_Conversion_Analysis_ExtraaLearn..ipynb: Jupyter Notebook containing the analysis and model development.ExtraaLearn.csv: Dataset used for the analysis.Mbaaoum_Lead_Conversion_Report_ExtraaLearn.html: Rendered HTML report of the analysis.
The data contains the different attributes of leads and their interaction details with ExtraaLearn. The detailed data dictionary is given below.
ID: ID of the lead
age: Age of the lead
current_occupation: Current occupation of the lead. Values include 'Professional','Unemployed',and 'Student'
first_interaction: How did the lead first interacted with ExtraaLearn. Values include 'Website', 'Mobile App'
profile_completed: What percentage of profile has been filled by the lead on the website/mobile app. Values include Low - (0-50%), Medium - (50-75%), High (75-100%)
website_visits: How many times has a lead visited the website
time_spent_on_website: Total time spent on the website
page_views_per_visit: Average number of pages on the website viewed during the visits.
last_activity: Last interaction between the lead and ExtraaLearn.
Email Activity: Seeking for details about program through email, Representative shared information with lead like brochure of program , etc
Phone Activity: Had a Phone Conversation with representative, Had conversation over SMS with representative, etc
Website Activity: Interacted on live chat with representative, Updated profile on website, etc
print_media_type1: Flag indicating whether the lead had seen the ad of ExtraaLearn in the Newspaper.
print_media_type2: Flag indicating whether the lead had seen the ad of ExtraaLearn in the Magazine.
digital_media: Flag indicating whether the lead had seen the ad of ExtraaLearn on the digital platforms.
educational_channels: Flag indicating whether the lead had heard about ExtraaLearn in the education channels like online forums, discussion threads, educational websites, etc.
referral: Flag indicating whether the lead had heard about ExtraaLearn through reference.
status: Flag indicating whether the lead was converted to a paid customer or not.