Ecommerce Orders –
Statistical Analysis
Chi-Square | Correlation | Regression |
ANOVA
Subhasree R-2527854
Shreyash Hiremath - 2527850
Sibin Biju- 2527851
Nikita. A-2527860
Sree Maanvi - 2527856
Subh Sanket Singh-2527852
Introduction: Decoding Ecommerce Data
E-commerce businesses generate massive amounts of transactional data daily. Understanding this data is key to strategic
decision-making and operational efficiency. This presentation will delve into a comprehensive analysis of 1,200 ecommerce
orders to uncover hidden patterns and actionable insights.
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Dataset Snapshot
Key Variables
Our Purpose
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1,200 ecommerce orders
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Payment Method
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Analyze behavioral patterns
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Multi-variable analysis
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Returns
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Extract actionable insights
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Time on Site
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Support data-driven decisions
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Order Value
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Shipping Method
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Delivery Days
Problem Statement: Key Questions
To optimize ecommerce operations and customer satisfaction, we need to answer critical questions about customer
behavior and logistics. Our analysis focuses on understanding causal relationships and predictive capabilities within our
dataset.
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Returns & Payments: Do payment methods influence product returns?
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Browsing & Spending: Does browsing time increase spending?
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Predictive Power: Can browsing time predict order value effectively?
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Shipping Efficiency: Does shipping method significantly affect delivery speed?
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Our Goal: Provide actionable business insights to drive performance.
Methodology: Statistical Approaches
We employed a suite of statistical tests to address our core questions, each suited to the specific type of data and relationship
being examined.
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Chi-Square Test: Used to analyze the relationship between
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Regression Analysis: Developed a model to predict Order
categorical variables, specifically Returns vs. Payment
Value based on Time on Site, assessing its predictive
Method, to determine if they are independent.
power.
Correlation Analysis: Explored the strength and direction
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ANOVA (Analysis of Variance): Compared the means of
of the linear relationship between Time on Site and Order
Delivery Days across different Shipping Methods to
Value.
determine if significant differences exist.
Tools Utilized
Microsoft Excel (Data Analysis ToolPak, Pivot Tables, Charting)
Chi-Square Test: Returns vs. Payment Method
We investigated whether the payment method chosen by a customer has any statistical association with the likelihood of a product return.
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Null Hypothesis (H₀): Returns are independent of Payment Method.
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Test Statistic: χ² = 0.7058
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P-value: p = 0.872 (which is > 0.05)
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Decision: Fail to reject H₀
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Interpretation: The chosen payment method has no significant impact on the
rate of product returns.
Correlation Analysis: Time on Site vs. Order Value
This analysis aimed to determine the extent to which time spent browsing the site is linearly related to the monetary value of an order.
Regression Analysis: Predicting Order Value
We used regression to model the relationship and determine if Time on Site can predict Order Value, even with a weak correlation.
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Null Hypothesis (H₀): Time on Site does not predict Order Value.
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Regression Equation: ŷ = 365.45 + 3.88 × (Time on Site)
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Intercept: 365.45 (baseline order value)
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Slope (β₁): 3.88 (for every minute increase on site, order value increases by $3.88)
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P-value: p < 0.001 (statistically significant, reject H₀)
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R²: 0.021 (indicates weak predictive power)
ANOVA Test: Delivery Days vs. Shipping Method
This analysis assessed whether there are statistically significant differences in average delivery days across various shipping methods offered.
Key Findings: Summary
Our statistical analyses reveal distinct insights into customer behavior and logistical performance within the ecommerce dataset.
Chi-Square
Correlation
Returns are independent of payment type. Focus on other
Weak link between browsing time and spending. Optimize
return drivers.
other conversion factors.
Regression
ANOVA
Time on site predicts spending, but its predictive power is
Shipping method strongly impacts delivery speed. Clearly
weak.
communicate options.
Recommendations: Actionable Insights
Based on our findings, we propose targeted actions to enhance customer experience and operational efficiency.
Streamline Checkout
Optimize User Experience
Since payment method doesn't affect returns, prioritize speed and
Given the weak correlation, focus on optimizing the browsing
ease of checkout over specific payment options.
experience for engagement and conversion, not just extended
time.
Refine Shipping Strategies
Further Research
Leverage distinct delivery speeds. Offer tiered shipping options
Explore other variables influencing order value and returns for
with clear expectations to meet diverse customer needs.
deeper insights into customer behavior.
These insights empower data-driven decisions for continuous growth.
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