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Project 02BeginnerBusiness4-6 focused hoursCommercial performance analysis
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E-Commerce Sales Analysis

Analyze revenue trends, products, customers, and business opportunities in a realistic online retail dataset.

PandasSQL conceptsEDAVisualization
FRAME→DATA→ANALYZE→MODEL→EVALUATE→COMMUNICATE

BUSINESS CONTEXT

Why this project matters

An e-commerce team wants to understand what is driving revenue growth, which products and customer segments contribute the most value, and where there may be untapped opportunities across channels, regions, and order behavior.

PROJECT OBJECTIVE

What you are expected to accomplish

Explore order-level sales data, surface the most important commercial patterns, and produce a concise set of insights and recommendations for a business review.

DATASET OVERVIEW

Expected dataset structure

Expect transactional order data with customer, product, pricing, discount, channel, and date fields. Some projects may also include product and customer reference tables for richer joins.

Main entities / rows

One row per order or order line item

order_id

string

Transaction identifier for each order.

order_date

date

Date or timestamp when the order was placed.

customer_id

string

Customer identifier used for repeat-purchase analysis.

product_category

categorical

High-level grouping for products sold.

quantity

integer

Units purchased in the order line.

unit_price

float

Selling price per unit before or after discount, depending on source design.

discount_pct

float

Promotional discount applied to the order or item.

sales_channel

categorical

Website, app, marketplace, or campaign source.

Data quality issues to expect

  • Orders may include cancellations or refunds that must be separated from completed revenue.
  • Discount fields can be missing or encoded differently across channels.
  • Date fields may need parsing and timezone normalization before trend analysis.

QUESTIONS TO ANSWER

Focus the work around meaningful decisions

  1. 01

    How does revenue trend over time across months or weeks?

  2. 02

    Which product categories and products generate the most revenue?

  3. 03

    Which customer segments or channels drive the highest order value?

  4. 04

    How do discounts relate to sales volume and revenue quality?

  5. 05

    Where are the clearest opportunities for growth or operational follow-up?

PROJECT ROADMAP

Move through the case study in a practical sequence

  1. 01

    Step 01

    Frame the commercial questions

    Decide whether the focus is revenue, order volume, customer value, or product mix.

  2. 02

    Step 02

    Inspect the data grain

    Confirm whether rows represent orders or order lines before calculating metrics.

  3. 03

    Step 03

    Clean transactions

    Handle refunds, date parsing, missing discounts, and any impossible revenue values.

  4. 04

    Step 04

    Build core sales metrics

    Calculate revenue, order count, average order value, and repeat-customer metrics.

  5. 05

    Step 05

    Compare segments

    Analyze performance by category, customer segment, region, and channel.

  6. 06

    Step 06

    Visualize time and mix

    Use charts that make trends and concentration easy to spot.

  7. 07

    Step 07

    Recommend business actions

    Turn findings into product, pricing, or campaign recommendations.

TASKS / MILESTONES

Concrete work to complete

  • Create a clean revenue metric and document how refunds or cancelled orders are handled.
  • Aggregate revenue by week or month to identify trend direction.
  • Rank product categories and top products by revenue contribution.
  • Calculate average order value and compare it across channels or regions.
  • Measure repeat-customer behavior using customer-level order counts or revenue.
  • Evaluate whether high-discount segments are genuinely valuable or only high-volume.
  • Summarize the strongest business opportunities in a short executive-ready section.

SUGGESTED VISUALIZATIONS

Visuals worth creating

  • Revenue trend line chart
  • Top categories or products bar chart
  • Average order value comparison by channel
  • Customer revenue distribution plot
  • Discount versus revenue scatter or grouped comparison chart

DELIVERABLES

What the learner should produce

  • Cleaned sales table with documented metric definitions
  • Analysis notebook or slide-style report
  • 5-8 relevant business visualizations
  • Segment and product insights
  • Final commercial recommendations

SUCCESS CRITERIA

What strong completion looks like

  • Revenue and order metrics are calculated at the correct grain.
  • The project distinguishes between volume and value, not just raw counts.
  • At least two actionable business opportunities are supported by analysis.
  • The final write-up is clear enough for a non-technical stakeholder to follow.

GUIDANCE

Support when you need a nudge

Reveal practical hints, checkpoints, and framing help without exposing a full finished solution.

Complete this project when the work is yours

Use this only after you have worked through the roadmap, milestones, and your own analysis.

KEY LEARNING OUTCOMES

Skills reinforced by this project

  • Build trustworthy business metrics from transactional data.
  • Use aggregation and segmentation to answer commercial questions.
  • Distinguish high-volume patterns from high-value patterns.
  • Communicate product and revenue insights clearly.

RELATED LESSONS

Revisit published lessons that support this build

Published lesson

Handling Missing Values in Pandas

Open lesson→
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