E-Commerce Funnel Analysis: Optimizing Customer Journey for Conversions

Overview

This project analyzes customer behavior in an e-commerce sales funnel to identify areas of improvement and optimize conversions. By leveraging the Customer360 Insights dataset, I analyzed the customer journey and provided recommendations for improving the user experience and increasing sales.

View the Tableau Dashboard
Access the Google Sheets (Original and Updated Data)
Kaggle Dataset - Customer360 Insights

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Table of Contents

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act
7. Next Steps & Impact
8. Acknowledgments

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Ask

The goal of this analysis is to answer the following key questions:

1. Funnel Performance: How efficiently are customers moving through the e-commerce funnel?
2. Drop-Off Points: Where are customers leaving the sales process?
3. Behavioral Patterns: What behaviors can be observed at each funnel stage?
4. Optimization Opportunities: How can we improve the sales funnel to increase conversions and reduce drop-offs?

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Prepare

Data Collection

• The dataset used for this analysis, Customer360 Insights, was obtained from Kaggle and includes customer interaction data such as session times, purchases, and demographic information.

Data Cleaning

• I cleaned the data by removing duplicates, handling missing values, and segmenting it into distinct funnel stages for easier analysis.

Access the Google Sheets (Original and Updated Data)

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Process

I performed Exploratory Data Analysis (EDA) using R and Tableau to identify key patterns and trends. Below is a breakdown of the stages:

Funnel Analysis

• Session to Cart Conversion: 100% of customers added items to their cart after visiting the website.
• Cart to Order Conversion: 85% of users completed their purchase, while 15% abandoned their carts.
• Order to Payment Conversion: 100% of confirmed orders were paid for after checkout.

Visualization: Funnel Analysis

Analyze

Key Insights from EDA Visualizations

1. Product Distribution:

   • Action figures and microwave ovens are the best-performing products, while table lamps and plush toys underperform.

2. Gender Distribution:

   • A balanced customer base of roughly 1000 individuals from both genders, allowing for targeted marketing campaigns.

3. Product Categories:

   • The home appliances and toys categories are the most popular, suggesting potential for promotions in these areas.

4. Monthly Income:

   • The majority of customers earn between $6000-$7000 monthly, offering a guide for pricing strategies.

5. Age Distribution:

   • Most customers are between 20-40 years old, with notable groups in the 50-60 and 70-80 age ranges.

6. Credit Score Distribution:

   • Most customers have credit scores between 600-750, indicating opportunities for financing options.

7. Country Distribution:

   • Canada has the largest customer base, followed by India, Australia, and China, highlighting a need for region-specific marketing.

8. Conversion by Gender:

   • Both genders have similar conversion rates, with males slightly outperforming females during the cart-to-order stage.

9. Campaign Effectiveness:

   • Instagram Ads perform the best, followed by Google Ads and Facebook Ads, suggesting an opportunity to optimize ad spend.

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Share

Key Findings:

• Product & Category Focus: Prioritize marketing for home appliances and toys, which are the highest-performing categories.
• Gender & Income Insights: Tailor marketing campaigns based on gender and income demographics.
• Geographic Strategy: Focus on Canada for region-specific marketing initiatives.

View the Tableau Dashboard

Act

Actionable Recommendations:

1. Simplify the Checkout Process:

   • Streamline the checkout flow to reduce abandonment. Implement guest checkout, add security badges, and optimize the number of steps.

2. Improve Product Pages:

   • Enhance product descriptions, include high-quality images, and add customer reviews to reduce decision fatigue.

3. Target Canada Market:

   • Run targeted marketing campaigns in Canada, potentially offering bilingual options for a more inclusive experience.

4. Test Different Checkout Flows:

   • Conduct A/B testing to experiment with different checkout flows and identify the most efficient design.

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Next Steps & Impact

Next Steps:

1. Investigate Product Returns: Analyze reasons for high return rates and improve product descriptions and quality.
2. User Testing: Conduct user testing on the checkout process to pinpoint pain points and improve conversion rates.
3. Run A/B Tests: Try different designs for the checkout flow and product pages to determine what works best for customers.

Impact:

By addressing issues such as cart abandonment and improving product descriptions, businesses can increase conversions, reduce drop-offs, and ultimately enhance profitability. This analysis highlights the value of data-driven decisions for improving both the customer journey and the business outcome.

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Acknowledgments

I would like to thank the following for their support:

• Kaggle for providing the Customer360 Insights dataset, which served as the foundation of this analysis.
• Google Sheets for its easy-to-use functionality, enabling quick data preparation and calculations.
• Tableau for offering powerful visualization tools that allowed me to present the data in an engaging, interactive format.
• R for providing statistical capabilities that helped deepen the analysis and uncover critical insights.