IBARAKI

DATA ANALYTICS OF ONLINE RETAIL STORE PERFORMANCE FROM JAN 2024 TO JUNE 2025

INTRODUCTION AND BACKGROUND

In recent years, the global e-commerce industry has grown rapidly, with multinational online merchants catering to millions of consumers in many regions. As customers look for ways to access a greater range of products, competitive prices, and quicker delivery options, cross-border e-commerce has emerged as a key driver of this expansion. International orders accounted for a sizable portion of the growth in worldwide e-commerce revenues, which exceeded $6 trillion in 2024, according to industry projections.

Success for global online firms hinges on maintaining profitability and operational effectiveness in addition to acquiring new customers. Diverse consumer preferences, cross-border logistics, regional acquisition expenses, and increased product return risks are some of the particular difficulties faced by international e-commerce sellers in contrast to domestic-only platforms. These factors emphasise the necessity of using data to inform supply chain, pricing, and marketing strategy decisions.

THIS PROJECT

We examine 15,000 e-commerce transactions from January 2024 to June 2025 as part of the project. for our client. Pricing, discounts, sales channels (organic, paid search, social advertisements, email, marketplaces, etc.), acquisition expenses, shipping costs, and return policies are just a few of the important variables covered in the dataset. The analysis intends to offer practical insights for scaling lucrative product-category × channel combinations by utilising these dimensions.

THE COMPANY AND QUESTIONS ASKED

ABCDE is an international online retailer  that operates across North America, Europe, and Asia-Pacific. The company sells consumer goods across multiple categories such as electronics, apparel, home & living, and beauty products, with both organic and paid marketing channels driving traffic. Despite robust sales, management faces key strategic questions:

• Which product categories and channels deliver the highest profitability?

• How can the business optimize marketing spend across paid channels while maintaining healthy ROAS (Return on Ad Spend)?

• Which areas present risks due to high return rates that could erode margins?

GOAL OF THIS PROJECT

As part of answering the questions, the goal of this project is to

• Recommend the top 3 product-category × marketing-channel combinations to scale next quarter (based on data through Jun 2025) that will maximize gross profit while keeping:
  • Return rate ≤ 8%
  • ROAS (Revenue / Acq. Cost) ≥ 2.5 for paid channels

• Deliverables:

  • 1. a clear KPI table,
    2. a short written recommendation with 2–3 concrete actions.

METHODOLOGY AND TOOLS USED

This project analyses 15,000 e-commerce transactions between Jan 2024 and Jun 2025 from an our client's online retail business to provide insights and actionable recommendations for scaling of their marketing efforts. The methodology followed a structured data analytics workflow:

Data Collection & Understanding

• Source: Historical e-commerce order data including: Order ID, Product Category, Channel, Date Net Revenue, COGS, Shipping Cost, Acquisition Cost Return Flag (returned vs. not returned)
• Timeframe: Jan 2024 – Jun 2025
• Goal: Understand sales performance, customer acquisition efficiency, and product-channel profitability.

Data Cleaning & Preparation

The data cleaning and preparation was done in both EXCEL and Python to ensure a clean and readily-usable data was available for our analysis. We;

• Converted order_date into monthly periods for trend analysis.
• Created derived metrics:
• Gross Profit = Net Revenue – COGS – Shipping – Acquisition Cost
• Return Rate = % of orders returned
• ROAS (Return on Ad Spend) = Revenue ÷ Acquisition Cost (for paid channels only)
• Classified marketing channels as Paid (Paid Search, Social Ads, Email, Marketplace) vs. Organic (Direct, SEO, Referral).
• Handled missing/zero values in costs (e.g., avoiding divide-by-zero errors in ROAS).

Exploratory Data Analysis (EDA)

Data visualisation was first done in Python codes and then latewr transfered into Tableau for proper and clearer visualisation. We;

• Visualized monthly revenue & gross profit trends.
• Analyzed return rate by product category to identify quality or satisfaction issues.
• Assessed channel & category performance using scatter plots and bubble charts.
• Compared ROAS vs. Orders to evaluate marketing efficiency.

Business KPI Definition

Key Performance Indicators (KPIs) tracked:

• Orders (volume)
• Net Revenue (sales)
• Gross Profit (profitability)
• Return Rate (customer satisfaction)
• ROAS (paid channel efficiency) To recommend scalable strategies, we applied thresholds:
• ✅ Return Rate ≤ 8% (to ensure product/customer quality)
• ✅ ROAS ≥ 2.5 (for paid channels to justify ad spend) Only product-category × channel combinations meeting these thresholds were shortlisted.

Dashboard Development

Built an interactive dashboard using Streamlit + Plotly:

• Filters for channels and categories
• KPIs displayed as metrics
• Interactive trend charts and comparison plots
• Detailed aggregated data table

HIGHLIGHTS OF FINDINGS

Electronics × Organic Search showed the highest gross profit among eligible combos Electronics × Direct also showed a strong profit and healthy return rate Electronics × Paid Search passed thresholds with excellent ROAS (≈44) (All three meet return rate ≤ 8%; the paid one also meets ROAS ≥ 2.5.)

CONCLUSION

The analysis of 15,000 international e-commerce transactions (Jan 2024–Jun 2025) highlights both opportunities and risks in scaling the business. While total revenue growth remains strong, profitability is uneven across product categories and channels. Some channels deliver healthy margins and efficient acquisition costs, while others generate sales at the expense of returns and marketing inefficiencies. Overall, the findings demonstrate that scaling decisions cannot be made solely on sales volume. Instead, success depends on balancing three critical levers: gross profit, ROAS efficiency, and return-rate control. By applying these metrics, the business can better allocate marketing budgets, strengthen product-category focus, and protect long-term profitability in competitive international markets.

RECOMMENDED ACTIONS FOR NEXT QUARTER

Scale Marketing Spend on Paid Search for Electronics Increase budget to amplify gross profit while monitoring ROAS to stay above 2.5. Optimize Organic Search & Direct Channels Maintain high-performing SEO and email campaigns for Electronics; focus on sustaining low return rates. Pilot Small Campaigns for Other High-Potential Categories. Test Marketing Channels in lower-performing categories to discover additional growth opportunities without major risk. Enhance Data-Driven Decision Making Across Regions Implement a standardized KPI dashboard (orders, revenue, gross profit, ROAS, return rates) to be monitored by regional managers. This will allow real-time tracking of category and channel performance across North America, Europe, and Asia-Pacific.

Strategic Outlook

By focusing on scaling high-margin, low-return, and efficient channel combinations, the business can achieve sustainable international growth. Meanwhile, addressing return-related inefficiencies and channel underperformance will safeguard margins. Stakeholders should view these insights not as one-time findings, but as the foundation for a continuous optimization cycle powered by data analytics.