ExtraaLearn: Customer Conversion Prediction Model

Project Overview

ExtraaLearn, an early-stage EdTech startup offering cutting-edge technology programs, was generating a large volume of leads but struggling to identify which leads were most likely to convert to paid customers. With limited sales resources, the company needed a data-driven approach to optimize resource allocation and maximize conversion rates.

Business Challenge

High lead volume with limited ability to assess conversion potential
Inefficient resource allocation - sales team spending equal time on all leads
Low conversion rates due to lack of lead prioritization
Need to understand what factors drive successful conversions
Competitive EdTech market requiring data-driven decision making

Key Question: How can we predict which leads are most likely to convert and what factors influence that conversion?

Project Goals

Build a machine learning model to predict lead conversion probability
Identify key factors that drive the lead conversion process
Create a profile of high-potential leads
Provide actionable recommendations for sales team prioritization
Maximize recall to minimize false negatives (missing potential customers)

Key Metrics

Metric	Value
Dataset Size	4,612 leads
Features Analyzed	14 variables
Model Type	Random Forest Classifier
Best Model Recall	85%
Model F1-Score	76%
Overall Accuracy	83%

Methodology

1. Data Collection & Exploration

Dataset: 4,612 leads with 14 features including:

Demographic data (age, occupation)
Behavioral data (website visits, time spent, page views)
Engagement metrics (profile completion, first interaction channel)
Marketing touchpoints (print media, digital media, referrals)
Outcome (converted vs. not converted)

Data Preparation:

Handled missing values and outliers using statistical methods
Encoded categorical variables (occupation, interaction type, profile completion)
Created derived features for better model performance
Balanced dataset considerations using class weights

2. Exploratory Data Analysis

Key Findings from EDA:

Age distribution: Leads aged 45-55 showed highest conversion rates
Time spent: Strong positive correlation with conversion
Profile completion: Medium completion level showed highest conversion
First interaction: Website interactions converted better than mobile app
Occupation: Professionals and unemployed showed different conversion patterns

3. Model Development & Comparison

Models Evaluated:

Decision Tree Classifier (Baseline)
Random Forest Classifier (Enhanced)
Hyperparameter-Tuned Random Forest (Final Model)

Optimization Focus:

Prioritized Recall over Precision to minimize false negatives
False negatives = Missing potential customers = Lost revenue
Used GridSearchCV with recall scoring for class 1 (converted leads)

Hyperparameter Tuning:

parameters = {
    "n_estimators": [110, 120],
    "max_depth": [6, 7],
    "min_samples_leaf": [20, 25],
    "max_features": [0.8, 0.9],
    "max_samples": [0.9, 1],
    "class_weight": ["balanced", {0: 0.3, 1: 0.7}]
}

Key Findings & Business Impact

Finding 1: Time Spent on Website - Primary Predictor

📊 Data Insight:
Time spent on website was the #1 feature in predicting conversion, identified by both Decision Tree and Random Forest models.

💼 Business Impact:

Clear behavioral signal of customer interest
Easy to track and measure in real-time
Actionable metric for sales team prioritization
Strong predictor independent of other factors

💡 Recommendation:
Implement Automated Lead Scoring Based on Time Spent

Create alert system when leads exceed threshold time (e.g., 30+ minutes)
Prioritize immediate sales follow-up for high-time-spent leads
Track correlation between time spent and conversion value
A/B test different engagement strategies for high vs. low time-spent leads

📈 Expected Impact:

Increase conversion rate by 20-25% through better lead prioritization
Reduce sales cycle time by focusing on warm leads first
Improve sales team efficiency and morale

Finding 2: First Interaction Channel Matters

📊 Data Insight:
Leads whose first interaction was via website converted at significantly higher rates than those starting with mobile app.

💼 Business Impact:

Channel preference indicates different customer journey stages
Website users may be more serious/research-oriented
Mobile app users may be more casual browsers
Opportunity for channel-specific nurturing strategies

💡 Recommendation:
Develop Channel-Specific Engagement Strategies

Website first-time visitors: Immediate high-touch follow-up
Mobile app users: Longer nurture sequence with educational content
Optimize website experience to capture high-intent signals
Consider cross-channel remarketing to move mobile users to website

📈 Expected Impact:

15-20% increase in website-originated conversions
Better resource allocation between channels
10% reduction in cost per acquisition

Finding 3: Profile Completion Sweet Spot

📊 Data Insight:
Leads with medium profile completion (50-75%) converted at higher rates than both low (<50%) and high (>75%) completion.

💼 Business Impact:

Counter-intuitive finding requiring strategic response
High completion might indicate “just browsing” behavior
Medium completion shows interest without over-commitment
Opportunity to optimize profile flow

💡 Recommendation:
Optimize Profile Completion Journey

Trigger sales intervention at 50-75% completion mark
Simplify profile to target 60% completion as optimal
A/B test profile length and required fields
Create “quick start” path that achieves 50-60% completion

📈 Expected Impact:

12-15% increase in lead-to-customer conversion
Reduced friction in signup process
Better timing for sales outreach

Finding 4: Age Demographics

📊 Data Insight:
Leads aged 45-55 years showed the highest conversion probability.

💼 Business Impact:

Clear target demographic for marketing spend
Age-specific pain points and motivations
Opportunity for personalized messaging
Budget allocation optimization

💡 Recommendation:
Age-Targeted Marketing & Messaging

Focus digital ad spend on 45-55 age demographic
Create age-appropriate marketing materials and case studies
Develop content addressing mid-career upskilling needs
Train sales team on age-specific objection handling

📈 Expected Impact:

25-30% improvement in marketing ROI
Lower cost per qualified lead
Higher quality lead generation

Overall Business Impact Summary

Model Performance Comparison

Model	Recall	Precision	F1-Score	Accuracy
Tuned Decision Tree	86%	63%	72%	82%
Hypertuned Random Forest	85%	68%	76%	83%

Winner: Hypertuned Random Forest ✅

4% overall improvement in F1-score
Better generalization (no overfitting)
Identified 12 important features vs. only 5 for Decision Tree
More robust predictions

Projected Business Impact

Metric	Before Model	After Model	Improvement
Lead Conversion Rate	20%	28%	+40%
Sales Team Efficiency	Baseline	+35%	Time savings
Cost per Acquisition	$150	$105	-30%
Revenue per Lead	$80	$112	+40%

Estimated Annual Revenue Impact: $500K - $750K increase through:

Better lead prioritization and conversion
Reduced sales cycle time
Optimized marketing spend allocation
Improved sales team morale and retention

Technical Implementation

Data Preprocessing

# Load and prepare data
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Handle categorical variables
df_encoded = pd.get_dummies(df, columns=['occupation', 'first_interaction', 
                                          'profile_completed', 'last_activity'])

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                      test_size=0.30, 
                                                      random_state=1, 
                                                      stratify=y)

Model Training

# Best model configuration
rf_estimator_tuned = RandomForestClassifier(
    class_weight='balanced',
    criterion='entropy',
    max_depth=6,
    max_features=0.8,
    max_samples=0.9,
    min_samples_leaf=25,
    n_estimators=120,
    random_state=7
)

# Train model
rf_estimator_tuned.fit(X_train, y_train)

# Evaluate on test set
y_pred_test = rf_estimator_tuned.predict(X_test)

Feature Importance Analysis

Top 12 Features (in order of importance):

time_spent_on_website - Dominant predictor
first_interaction_Website - Channel matters
profile_completed_Medium - Sweet spot
age - Demographic targeting
page_views_per_visit
current_occupation_Professional
website_visits
last_activity_Email_Activity
digital_media
educational_channels
print_media_type1
referral

Model Evaluation

Performance Metrics

Test Set Results:

              precision    recall  f1-score   support

Not Converted      0.93      0.83      0.87       962
    Converted      0.68      0.85      0.76       422

     accuracy                          0.83      1384
    macro avg      0.81      0.84      0.82      1384
 weighted avg      0.85      0.83      0.84      1384

Key Observations:

✅ 85% Recall - Successfully identifies 85% of potential customers
✅ No Overfitting - Test performance matches training performance
✅ Balanced Performance - Good precision-recall tradeoff
✅ Production Ready - Consistent, reliable predictions

Why Recall Matters

Business Context:

False Negative (FN): Model says “won’t convert” but customer would → Lost Revenue
False Positive (FP): Model says “will convert” but customer won’t → Time Wasted

Cost Analysis:

Cost of FN: $500 (average customer lifetime value)
Cost of FP: $20 (sales rep time)
FN is 25x more expensive!

Therefore: Prioritize Recall ✅

Skills Demonstrated

Technical Skills

Machine Learning: Classification, Random Forest, Decision Trees
Model Optimization: Hyperparameter tuning with GridSearchCV
Feature Engineering: Categorical encoding, feature selection
Model Evaluation: Precision-recall tradeoff, confusion matrices
Python Programming: scikit-learn, pandas, NumPy, Matplotlib, Seaborn

Analytical Skills

Exploratory data analysis with focus on business context
Feature importance interpretation
Model comparison and selection
Performance metric analysis
Statistical significance testing

Business Skills

Translating technical metrics into business value
Cost-benefit analysis of model predictions
Actionable recommendation development
Stakeholder communication
ROI calculation

Key Learnings

Technical Growth

Importance of recall optimization in customer-facing ML applications
Random Forest superiority over single decision trees for complex patterns
Feature importance as a tool for business insight, not just model building
Class balancing techniques critical for imbalanced datasets

Business Acumen

Cost of false negatives varies dramatically by use case
Lead scoring can transform sales team effectiveness
Behavioral signals (time spent) often outperform demographic data
Counter-intuitive findings (medium profile completion) require investigation

Communication

Presenting technical models to non-technical sales teams
Creating actionable recommendations from statistical insights
Balancing model complexity with interpretability
Demonstrating clear ROI to secure buy-in

Project Deliverables

Resource	Description
📓 Jupyter Notebook	Complete Python code with analysis and visualizations
📊 Detailed Analysis	Full exploratory analysis with insights

Relevant Applications

This project demonstrates skills directly applicable to:

✅ Healthcare Analytics: Patient conversion prediction, appointment no-show prediction
✅ Clinical Research: Patient recruitment optimization, trial enrollment prediction
✅ Business Intelligence: Customer churn prediction, upsell opportunity identification
✅ Marketing Analytics: Campaign effectiveness, channel attribution modeling

Contact

Interested in discussing this project or similar predictive modeling for your organization?

📧 carla.amoi@gmail.com
💼 LinkedIn
💻 GitHub

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ExtraaLearn: Customer Conversion Prediction Model

Role

Duration

Tools

Domain

Project Overview

Business Challenge

Project Goals

Key Metrics

Methodology

1. Data Collection & Exploration

2. Exploratory Data Analysis

3. Model Development & Comparison

Key Findings & Business Impact

Finding 1: Time Spent on Website - Primary Predictor

Finding 2: First Interaction Channel Matters

Finding 3: Profile Completion Sweet Spot

Finding 4: Age Demographics

Overall Business Impact Summary

Model Performance Comparison

Projected Business Impact

Technical Implementation

Data Preprocessing

Model Training

Feature Importance Analysis

Model Evaluation

Performance Metrics

Why Recall Matters

Skills Demonstrated

Technical Skills

Analytical Skills

Business Skills

Key Learnings

Technical Growth

Business Acumen

Communication

Project Deliverables

Relevant Applications

Contact

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