Customer / Retention
Customer Revenue & Retention Analytics System
Revealed revenue concentration and retention patterns.
Top 20%
Revenue Concentration
Cohort
Decay Tracked
Forecast
Layer Added
Business Problem
What was unclear
The business had transactional data but lacked visibility into customer behavior. There was no clear understanding of which customers drove revenue, how retention changed over time, or how future revenue might evolve.
Data & System Built
From sources to structure
Data sources
- Raw transactional history
- Customer first-purchase and repeat data
- Product and category metadata
System built
- 01Cleaned and modeled raw transactional data.
- 02Built fact and dimension tables for consistent analysis.
- 03Created reusable metrics for customer and product analysis.
- 04Enabled cohort analysis, revenue distribution tracking, and time-based insights.
Key Insights
What the data revealed
INSIGHT 01
Revenue is highly concentrated
A small group of customers drives a large share of revenue.
INSIGHT 02
Retention declines over time
Customer engagement reduces significantly across cohorts.
INSIGHT 03
Customer quality varies by acquisition period
Different cohorts show different long-term behavior.
INSIGHT 04
Revenue trends are predictable
Historical patterns can be used for forward-looking estimates.
Impact / Outcome
What decisions it enabled
- →Provided clarity on customer value distribution.
- →Identified retention gaps.
- →Enabled data-driven growth planning.
- →Created a scalable analytics foundation.
Visual Layer
Where the system surfaces clarity
VIEW 01
Cohort retention heatmap
VIEW 02
Revenue distribution chart
VIEW 03
Revenue trend + forecast
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