Funnel & Cohort Analysis for Business Growth

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Introduction

Understanding customer behavior and retention patterns is crucial for business growth. This project leverages Python, SQL, and Tableau to analyze customer interactions, identify trends, and provide actionable insights. By applying Funnel Analysis and Cohort Analysis, businesses can optimize conversion rates and improve customer retention.

This article presents the key findings and recommendations derived from Funnel & Cohort Analysis, showcasing how data analytics can drive strategic decision-making.

Data Pre-Processing

Before conducting the analysis, the dataset underwent cleaning and transformation to ensure accuracy.

Funnel Data Pre-Processing

  • Handling Missing Values:
    • user_id was replaced with 0 when missing.
    • age was filled with the median, while gender, city, and country were filled with the most common values.
  • Duplicate Removal:
    • Checked for duplicates and removed them to maintain data integrity.
  • Exporting Data:
    • The cleaned dataset was exported as an Excel file for Tableau visualization.

Cohort Data Pre-Processing

  • Handling Missing Values:
    • Only 0.01% of values in product_name were missing; these were removed due to their minimal impact.
  • Duplicate Removal:
    • No duplicate records were found.
  • Exporting Data:
    • The cleaned dataset was saved in CSV and Excel formats for SQL and Tableau processing.

SQL-Based Cohort Analysis

The cohort analysis was conducted using SQL, following these key steps:

  1. Define Cohort Month: Identified when users made their first purchase.
  2. Calculate Cohort Data: Counted returning users in subsequent months.
  3. Determine Initial Users: Established a baseline of users per cohort.
  4. Compute Retention Rates: Measured how many users remained active over time.

This structured SQL approach provided valuable insights into customer retention patterns.

Dashboard Insights & Recommendations

1. Customer Engagement by City

  • Insight: Shanghai has the highest number of events, indicating strong customer engagement.
  • Recommendation: Focus marketing efforts on Shanghai while exploring strategies to boost engagement in other cities.

2. Funnel Conversion Drop-offs

  • Insight:
    • 100% of users engage with products.
    • Only 75% add items to the cart, and just 50% complete the purchase.
  • Recommendation:
    • Implement cart abandonment emails and discount offers to encourage conversions.
    • Simplify the checkout process to reduce friction.

3. Traffic Source Performance

  • Insight: Email marketing drives the most traffic, while YouTube and Facebook have lower engagement.
  • Recommendation:
    • Continue email campaigns due to their high ROI.
    • Increase investment in social media ads to diversify traffic sources.

4. Customer Demographics (Age & Gender)

  • Insight:
    • Women aged 40-59 are the most active customers.
    • Male engagement is consistently lower across all age groups.
  • Recommendation:
    • Create targeted campaigns for women aged 40-59 to maintain engagement.
    • Introduce male-focused promotions to attract a broader audience.

5. Cohort Retention Trends

  • Insight: Retention drops sharply after the first month, stabilizing below 1% in later months.
  • Recommendation:
    • Improve onboarding experiences with personalized emails and exclusive offers.
    • Analyze recent cohorts to replicate successful retention strategies.

6. Seasonal Sales Trends

  • Insight: Sales peak in November and December due to holiday shopping.
  • Recommendation:
    • Launch seasonal marketing campaigns to maximize sales during peak months.
    • Use cohort-based re-engagement campaigns to bring back dormant users.

7. Product Performance Analysis

  • Top-Selling Product: NIKE WOMEN’S PRO CO. generates the highest revenue.
  • Recommendation: Expand stock for high-demand products and introduce similar offerings.

8. Sales & Profit Breakdown

  • Total Sales: $1,138,015
  • Total Profit: $590,634 (indicating a healthy profit margin).
  • Recommendation: Focus on high-margin products while optimizing costs for low-profit items.

9. Sales Contribution by Age Group

  • Insight: Customers aged 20–39 contribute the most to sales.
  • Recommendation: Personalize offers for younger demographics while creating engagement strategies for older customers.

Conclusion

The Funnel & Cohort Analysis provides a comprehensive understanding of customer behavior, conversion patterns, and retention trends. By leveraging these insights, businesses can:
✅ Increase conversion rates through optimized funnels.
✅ Enhance customer retention with improved onboarding and engagement strategies.
✅ Maximize sales and profitability by focusing on high-performing products and seasonal trends.

🔗 Explore the full project and dashboards:
📌 GitHub Repository
📌 Tableau Dashboard

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