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Lead Score Case Study

Executive Summary

Target:
To find the potential lead for the company out of all leads.

Steps:

  1. Cleaning Data:

    • Partial cleaning of the data, replacing null values and irrelevant options.
    • Filtered data to include only leads from India, USA, and UAE.

  2. Dummy Variables:

    • Created dummy variables for categorical features.
    • Standardized data using StandardScaler.

  3. EDA (Exploratory Data Analysis):

    • Quick EDA performed to check data condition.
    • Outliers in numerical variables were identified and removed.

  4. Train-Test Split:

    • Data split into 70% train and 30% test sets with random_state = 100.

  5. Model Building:

    • Used Recursive Feature Elimination (RFE) to select top 15 relevant variables.
    • Removed remaining variables based on VIF and p-value criteria.

  6. Model Evaluation:

    • Confusion matrix created.
    • Optimum cut-off value determined using ROC curve.
    • Accuracy, sensitivity, and specificity evaluated (approx. 90% each).

  7. Prediction:

    • Predicted on the test dataset using an optimum cut-off of 0.42.

Conclusions

  • Manual segregation of leads required for nurturing potential customers.
  • Model meets CEO's expectations with a target lead conversion rate of approx. 90%.
  • Features contributing to lead conversion probability identified.

Summary

Train Set:

  • Accuracy: 0.8945
  • Specificity: 0.8846
  • Sensitivity/Recall: 0.9083
  • Precision: 0.8491

Test Set:

  • Accuracy: 0.8914
  • Specificity: 0.8724
  • Sensitivity/Recall: 0.9181
  • Precision: 0.8358

The model predicts conversion rate well, providing confidence to the CEO in making informed decisions.

Data Dictionary

Variable Description
Prospect ID A unique ID with which the customer is identified.
Lead Number A lead number assigned to each lead procured.
Lead Origin The origin identifier with which the customer was identified to be a lead. Includes API, Landing Page Submission, etc.
Lead Source The source of the lead. Includes Google, Organic Search, Olark Chat, etc.
Do Not Email An indicator variable selected by the customer indicating whether they want to be emailed about the course or not.
Do Not Call An indicator variable selected by the customer indicating whether they want to be called about the course or not.
Converted The target variable. Indicates whether a lead has been successfully converted or not.
TotalVisits The total number of visits made by the customer on the website.
Total Time Spent on Website The total time spent by the customer on the website.
Page Views Per Visit Average number of pages on the website viewed during the visits.
Last Activity Last activity performed by the customer. Includes Email Opened, Olark Chat Conversation, etc.
Country The country of the customer.
Specialization The industry domain in which the customer worked before. Includes 'Select Specialization' for customers who had not selected an option while filling the form.
How did you hear about X Education The source from which the customer heard about X Education.
What is your current occupation Indicates whether the customer is a student, unemployed, or employed.
What matters most to you in choosing this course Indicates the main motivation behind choosing the course.
Search Indicates whether the customer had seen an ad in any of the listed items.
Magazine Indicates whether the customer had seen an ad in any of the listed items.
Newspaper Article Indicates whether the customer had seen an ad in any of the listed items.
X Education Forums Indicates whether the customer had seen an ad in any of the listed items.
Newspaper Indicates whether the customer had seen an ad in any of the listed items.
Digital Advertisement Indicates whether the customer had seen an ad in any of the listed items.
Through Recommendations Indicates whether the customer came in through recommendations.
Receive More Updates About Our Courses Indicates whether the customer chose to receive more updates about the courses.
Tags Tags assigned to customers indicating the current status of the lead.
Lead Quality Indicates the quality of lead based on data and intuition of the employee assigned to the lead.
Update me on Supply Chain Content Indicates whether the customer wants updates on Supply Chain Content.
Get updates on DM Content Indicates whether the customer wants updates on DM Content.
Lead Profile A lead level assigned to each customer based on their profile.
City The city of the customer.
Asymmetrique Activity Index An index and score assigned to each customer based on their activity and profile.
Asymmetrique Profile Index An index and score assigned to each customer based on their activity and profile.
Asymmetrique Activity Score An index and score assigned to each customer based on their activity and profile.
Asymmetrique Profile Score An index and score assigned to each customer based on their activity and profile.
I agree to pay the amount through cheque Indicates whether the customer has agreed to pay the amount through cheque.
A free copy of Mastering The Interview Indicates whether the customer wants a free copy of 'Mastering the Interview' or not.
Last Notable Activity The last notable activity performed by the student.

Libraries Used

  • numpy
  • pandas
  • matplotlib.pyplot
  • seaborn
  • sklearn
  • statsmodels.api
  • warnings