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PROJECT OVERVIEW

EOY Customer Analysis & Insights 2024–2025

Category
Dashboards & Reporting
Type
Customer Analysis & Reporting
Status
Completed

THE SITUATION

Two years of customer records needed to explain change, not remain separate snapshots.

Year-end customer data included customer counts, revenue, retained relationships, net-new customers, churned customers, and the contribution of top accounts. The value came from comparing those dimensions across 2024 and 2025.

THE ANALYSIS GAP

Overall totals could not explain how customer composition and revenue contribution changed.

  • Customer count and revenue needed year-over-year comparison
  • Retained, net-new, and churned groups needed separation
  • Top-customer contribution needed context within total revenue

THE APPROACH

Segment the customer base, compare periods, and connect composition to revenue.

The analysis aligns 2024 and 2025 customer and revenue totals, separates retained, net-new, and churned segments, calculates revenue-per-customer context, and compares top-customer revenue share and composition.

VISUAL PROOF

Year-over-year analysis in view

See customer count, revenue, retention, acquisition, churn, and concentration compared across 2024 and 2025.

2024 and 2025 customer, revenue, retention, churn, and top-customer analysis

THE OUTCOME

Historical customer data became a structured explanation of composition and change.

INSIGHTS SURFACED

  • Year-over-year customer-count and revenue movement
  • Retained, net-new, and churned customer composition
  • Revenue contribution from each customer segment
  • Average revenue per customer across the compared groups
  • Top-customer revenue share and composition by year

SYSTEM DETAILS

Project Details

Category
Dashboards & Reporting
Type
Customer Analysis & Reporting
Status
Completed
Platform
Google Sheets

Period

  • 2024
  • 2025

Analysis

  • Year-over-year comparison
  • Customer segmentation
  • Revenue share

Segments

  • Retained
  • Net-new
  • Churned
  • Top customers

THE TAKEAWAY

Analysis becomes valuable when historical data explains what changed and which customer groups shaped the result.

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