---
license: mit
language:
- en
tags:
- sales
- fashion
---
# Analyzing Consumption Efficiency Over Time: The Regret Gap and Logistical Burden in Global E-commerce

## Project Presentation
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## Research Question
> **"Analyzing Consumption Efficiency Over Time: What are the peak effective hours for e-commerce activity, and what is the logistical cost of high-risk nocturnal purchases?"**

---

## Project Overview
This project investigates the psychological and logistical boundaries of modern e-commerce. The research utilized a complex relational dataset consisting of 7 different tables. Through a process of strategic selection, the 4 most relevant tables (Orders, Order_Items, Products, and Users) were integrated to create a unified master dataset containing over 181,000 records.

The primary objective was to identify the "Regret Gap": a phenomenon where late-night, impulsive purchases—driven by lowered cognitive barriers—lead to higher return rates and significant logistical inefficiencies.

## Methodology and Data Pipeline
To transform raw data into actionable business insights, a rigorous cleaning and normalization process was implemented:

1. Data Integration and Integrity: Joined 4 tables using unique identifiers (Order ID, User ID).
2. Missing Value Imputation:
   - User Identity Logic: Identified identical User IDs to complete missing demographic details.
   - Temporal Completion: Derived day of the week and specific dates where timestamp data was incomplete.
3. Local Time Normalization: Converted UTC timestamps into the user's local time based on their geographic coordinates. This step was critical for enabling behavioral analysis relative to the user's actual time of day.
4. Outlier Removal: Filtered the top 2% of price points and removed illogical data points (e.g., ages 0 or 120, and transactions with zero profit).

## Statistical Overview
Broad statistical analysis revealed a significant trend: while afternoon hours (14:00-17:00) record the highest volume of "safe" transactions, the post-midnight window (00:00-05:00) accounts for a disproportionate percentage of total return costs.

## Visual Analysis and Findings

### 1. 24-Hour Business Health: Volume vs. Risk
- Visualization: A dual-axis chart comparing total order volume against the probability of return over a 24-hour cycle.
- Analysis: While sales volume remains relatively steady throughout the day, the probability of risk (cancellations and returns) spikes significantly between 00:00 and 06:00.
- Conclusion: Nocturnal sales often represent "illusory revenue"—they appear positive on real-time dashboards but translate into logistical burdens within the following week.

![24-Hour Business Health](https://cdn-uploads.huggingface.co/production/uploads/69de48d8576ce0533f5e973a/lcqiHzvft7kP0rl5H6oBe.png)


### 2. Global Order Density: The Biological Clock
- Visualization: A geospatial Choropleth map normalized to local time zones.
- Analysis: Regardless of geography or specific market, return density correlates with the local "night-time wave."
- Conclusion: Purchase efficiency is tied to the human biological clock rather than geography. Night-time regret is a universal behavioral trait.

![Global Order Density](https://cdn-uploads.huggingface.co/production/uploads/69de48d8576ce0533f5e973a/8K_hPg1h2HTG8_ZRNvsrs.gif)

### 3. Statistical Validation: Chi-Square Test
- Visualization: A statistical summary table/heatmap of the Chi-Square contingency test.
- Analysis: Tested the independence between the purchase time window and final order status.
- Conclusion: With a p-value < 0.05, the time of day is a statistically significant predictor of order success. This confirms the patterns observed are not coincidental.

![Statistical Validation](https://cdn-uploads.huggingface.co/production/uploads/69de48d8576ce0533f5e973a/SP6qCOznkHBBLsyh6UZqT.png)

### 4. Behavioral Mirror: Complex Fit vs. One-Size
- Visualization: A "Mirror" Lollipop chart comparing different product categories.
- Analysis: Categories such as Jeans and Sweaters (Complex Fit) experience a massive spike in returns at night due to sizing errors made under fatigue. Conversely, Accessories (One-Size) remain stable.
- Conclusion: Night-time risk is category-dependent. Retailers should avoid promoting products requiring high cognitive effort (such as precise sizing) during low-energy hours.

![Behavioral Mirror Chart](https://cdn-uploads.huggingface.co/production/uploads/69de48d8576ce0533f5e973a/PmdhM9721zGiRzrt5mLyS.png)

### 5. Purchase Price Distribution and the Regret Gap
- Visualization: Analysis divided into two parts: Distribution Density and Median Price Trends.
- Analysis: Paradoxically, median prices are higher at night, as consumers tend to make more expensive impulse purchases.
- Conclusion: This represents the core of the Regret Gap. Consumers commit to expensive items when their cognitive barriers are low, leading to "buyer's remorse" the following morning.

**Distribution Density**


![Price Distribution Analysis](https://cdn-uploads.huggingface.co/production/uploads/69de48d8576ce0533f5e973a/Zf_UUrQ3QryJaoGc0mA3l.png)

## Strategic Business Conclusions
- Advertising Optimization: Reallocate marketing budgets away from nocturnal hours for categories requiring complex fit.
- Cooling-off Period: Implement a 4-hour processing delay for high-value night orders to allow for morning cancellations before the logistics chain is activated.
- Dynamic User Interface: Adjust the digital storefront to feature low-risk items (such as accessories) during late-night hours.

## Future Research Questions
- Social Media Influence: How do specific platforms (TikTok vs. Instagram) influence impulse purchasing behavior across different time windows?
- Demographic Deep-Dive: Does the Regret Gap vary significantly based on the shopper's age or gender?
- AI Intervention: Can real-time AI sizing assistants mitigate the night-time return spike in the apparel category?