Business Performance Diagnostic · E-commerce Case Study
Revenue fell 64.6%. Customer loss explained most of it.
The diagnosis: Acquisition and retention weakened simultaneously, shrinking the active customer base while delivery problems and product returns created additional friction.
my role
- Revenue-driver decomposition
- Customer-base reconciliation
- Annual cohort retention
- Customer order sequencing
- Multi-level root-cause analysis
- Profit-leakage reconciliation
- KPI calculations across different data grains
-64.6%
Revenue
€1.57M in 2023 → €556K in 2025
-58.1%
Active customers
7,539 in 2023 → 3,157 in 2025
8.3% → 4.0%
Site conversion
Less than half the 2023 conversion rate
12.5%
Next-year retention
639 of 5,119 customers returned in 2025
The situation
A retailer reached €1.57M in recognized revenue in 2023. By 2025, revenue had fallen to €556K. Discounts were rising, refunds were consuming more revenue and customer counts were falling but topline reports did not explain where the customer journey was breaking. The business risk was that management would respond with broader promotions or additional marketing spend without fixing the sources of customer loss.
The task
The objective was to answer four management questions: What changed? Why did it change? How much did each driver matter? What should management do first? The diagnostic had to distinguish acquisition weakness from retention weakness, connect customer behaviour to service and product signals and size the opportunity without presenting assumptions as guaranteed outcomes.
The analyses
• Reconciled orders, order lines, refunds and customer records to establish one reliable revenue and customer baseline.
• Decomposed the 2023-2025 revenue change into active customers, purchase frequency and realized value per order.
• Built annual retention and first-purchase cohort views to separate new-customer loss from repeat-customer loss.
• Compared traffic, conversion and cost per conversion by acquisition channel.
• Linked returns, reviews, delivery performance and payment friction to customer outcomes.
• Ranked findings by financial effect, controllability and evidence strength then converted them into a management action plan.
The insights
The diagnostic showed that lower active-customer volume accounted for approximately €912K of the €1.013M revenue decline, while lower order frequency accounted for a further €119K. Higher average order value partly offset the damage but it did not solve it. New buyers fell 65.4%, returning buyers fell 49.1%, and next-year retention fell from 23.4% for the 2023 base to 12.5% for the 2024 base. The analysis replaced a vague concern about customer loss with a prioritized agenda covering acquisition efficiency, retention, Smart Home product category quality and fulfilment capacity.
The recommendations
• Repair acquisition economics. Audit paid-search and paid-social targeting, landing pages and checkout. Set channel-level conversion and cost-per-conversion guardrails.
• Rebuild repeat purchasing. Create cohort-based onboarding, replenishment and win-back journeys. Rebuild email reach, test offers against a sample group.
• Contain Smart Home quality loss. Quarantine flagged SKUs, investigate supplier and defect causes and review contribution after refunds and return handling.
• Stabilize W001 warehouse fulfilment. Review capacity, cut-off rules, carrier mix and promise dates, route overflow when service thresholds are breached.
• Install a customer-loss scorecard. Track customer reconciliation, conversion, repeat cohorts, refunds, quality and service exceptions in one management view.
Business Performance Diagnostic
You know performance is slipping. Let’s find out why.
I help growing businesses turn fragmented customer, sales and operational data into a clear diagnosis,showing what is changing, what is driving it and where management should act first.
Pavlos Poli
BUSINESS INSIGHTS & ANALYTICS
Revenue · Profitability · Customer Retention · Management Reporting