E-Commerce Analytics: Segmentation, Cohort & Sales Dashboard

An end-to-end e-commerce analytics dashboard designed for a jewelry brand to integrate and transform transactional data across multiple sales channels (Website, TikTok Shop, Tokopedia, Shopee)

Key Outcomes

• Consolidated fragmented sales data from multiple distinct platforms into a Single Source of Truth
• Transformed raw transactional data into RFM personalize marketing strategies
• The comprehensive cohort analysis pinpointed critical drop-off patterns and Time to Churn

Summary & Data Understanding

An executive summary page mapping out data architecture and strategic frameworks. It processes historical datasets (Jan 2024 - Apr 2025) into 21 key metric visualizations. This section also defines metric metadata and custom strategic plays for each customer segment to ensure alignment across business teams.

Transaction Overview

Functions as the central hub for monitoring the business's North Star metrics. Key features include:

• Omnichannel Sales Distribution: Tracks overall revenue and breaks down performance across platforms (TikTok Shop, Website, Tokopedia, Shopee) and operational regions.
• Trend & Target Tracking: Visualizes sales, orders, and customer trends over time, complete with progress indicators against revenue targets.
• Product & Promotion Analytics: Analyzes top-performing jewelry items by sales value and weight (grams), featuring a scatter plot evaluating the direct correlation between promotional activities and sales volume surges.

RFM (Recency, Frequency, Monetary) Segmentation

A dedicated module for deep customer profiling to execute targeted marketing:

• Behavioral Metrics: Monitors average days since last purchase (Recency), order frequency (Frequency), and purchasing power (Monetary).
• Customer Grouping: Segments customers into strategic tiers (e.g., Promising, Hibernating, Lost) visualized via Tree Maps and Scatter Plots to evaluate revenue contribution.
• Actionable Targeting: Provides a user-level breakdown table ready for export by marketing teams for re-engagement or upselling campaigns.

Cohort Analysis & Retention

Focuses on long-term user lifecycle and loyalty analysis:

• Monthly Retention Heatmap: Tracks customer retention percentages from their initial acquisition month (First Purchase Date) across subsequent months.
• Purchasing Behavior: Analyzes time gaps (Time to Churn and days between first and second purchase) and compares Average Order Value (AOV) between first-time and repeat purchases.
• Platform Acquisition: Dissects which channels are most effective at acquiring high-value, first-time buyers.