Analytics & Optimization

Data-Driven E-Commerce: From Raw Numbers to Revenue Growth

Most e-commerce businesses generate enormous amounts of data every day. Orders flow in from multiple channels, marketing campaigns drive traffic from dozens of sources, inventory moves between warehouses and storefronts, and customers interact with your brand across devices and touchpoints. Yet the majority of this data sits unused, trapped in disconnected systems or buried in spreadsheets that nobody opens after Monday morning.

The gap between having data and using data effectively is where revenue leaks happen. Without proper e-commerce analytics, you are making decisions based on gut feeling, reacting to problems instead of preventing them, and missing optimization opportunities that compound over time.

At Duxly, we help European e-commerce businesses build measurement frameworks that turn scattered data into clear, actionable insights. We connect your systems, define the metrics that matter, build dashboards your team will actually use, and implement optimization processes that drive measurable improvements. Whether you sell through a single Shopify store or manage a complex multi-channel operation spanning marketplaces, wholesale, and direct-to-consumer, we design analytics solutions that match your reality.

This page covers the full scope of our analytics and optimization practice: from identifying the right KPIs, through building integrated dashboards, to running structured optimization programs that deliver results.

Key E-Commerce KPIs and What They Actually Tell You

Not all metrics deserve dashboard space. The KPIs you track should directly inform decisions. Here is a structured breakdown of the metrics that matter most for e-commerce operations, organized by the business question they answer.

Revenue and Profitability Metrics

• Gross Merchandise Value (GMV): Total sales volume before deductions. Useful for tracking growth, but meaningless without margin context.
• Average Order Value (AOV): Revenue divided by number of orders. A small AOV increase often yields more profit than chasing new traffic.
• Customer Lifetime Value (CLV): Projected revenue from a customer over their entire relationship with your brand. Essential for determining how much you can spend on acquisition.
• Gross Margin per Order: Revenue minus cost of goods sold, shipping, and transaction fees. The number that actually matters for profitability.
• Revenue per Visitor (RPV): Combines conversion rate and AOV into one efficiency metric. Useful for comparing channel performance.

Acquisition and Marketing Metrics

• Customer Acquisition Cost (CAC): Total marketing and sales spend divided by new customers acquired. Compare this against CLV to assess sustainability.
• Return on Ad Spend (ROAS): Revenue generated per euro spent on advertising. Track per channel and per campaign to allocate budgets effectively.
• Cost per Acquisition by Channel: Breaks down CAC by traffic source. Reveals which channels deliver customers profitably and which drain budget.
• Blended vs. Incremental ROAS: Blended ROAS counts all revenue from ad-exposed users. Incremental ROAS isolates the revenue that would not have occurred without the ad. The difference is often significant.

Conversion and Experience Metrics

• Conversion Rate: The percentage of visitors who complete a purchase. Industry benchmarks range from 1-4%, but your baseline is what matters.
• Cart Abandonment Rate: Percentage of users who add items to cart but do not complete checkout. Typically 60-80% across e-commerce.
• Checkout Completion Rate: Of those who begin checkout, how many finish? This isolates checkout friction from browsing behavior.
• Bounce Rate by Landing Page: Identifies pages that fail to engage visitors. High bounce on product pages suggests mismatched expectations or poor content.
• Site Search Conversion Rate: Users who search convert at 2-3x the rate of non-searchers. If your search converts poorly, the search experience needs work.

Operational Metrics

• Order Processing Time: Time from order placement to shipment. Directly affects customer satisfaction and repeat purchase rates.
• Inventory Turnover: How quickly stock sells and is replaced. Low turnover ties up capital; high turnover risks stockouts.
• Stockout Rate: Percentage of time products are unavailable. Every stockout is lost revenue and potentially a lost customer.
• Return Rate: Percentage of orders returned. High return rates indicate product quality issues, poor descriptions, or sizing problems.
• Fulfillment Accuracy: Percentage of orders shipped correctly. Errors are expensive to fix and damage trust.

Retention and Loyalty Metrics

• Repeat Purchase Rate: Percentage of customers who buy more than once. Acquiring a new customer costs 5-7x more than retaining an existing one.
• Purchase Frequency: Average number of orders per customer per year. Combined with AOV, this drives CLV.
• Net Promoter Score (NPS): Measures customer satisfaction and likelihood to recommend. A leading indicator of future growth or churn.
• Cohort Retention Curves: Track how different customer groups behave over time. Reveals whether your retention is improving or declining.

The specific KPIs you prioritize depend on your business model, growth stage, and strategic objectives. A marketplace seller optimizing for Buy Box percentage has different priorities than a DTC brand focused on subscription retention. We help you identify and define the metrics that align with your goals.

Building a Measurement Framework

Tracking everything is not a strategy. A measurement framework gives structure to your analytics practice by defining what you measure, why you measure it, and what actions different metric values should trigger.

The Measurement Hierarchy

A well-structured framework operates on three levels:

Level 1: North Star Metrics (1-2 metrics) These are the top-level outcomes your business optimizes for. Examples: monthly recurring revenue, contribution margin per order, or customer lifetime value. Every team should understand how their work connects to these.

Level 2: Driver Metrics (5-8 metrics) These are the levers that move your North Star. For an e-commerce business focused on profitability, drivers might include AOV, conversion rate, CAC, and gross margin. Changes here directly predict changes in your North Star.

Level 3: Diagnostic Metrics (15-30 metrics) These explain why driver metrics move. If conversion rate drops, diagnostic metrics like page load time, add-to-cart rate, and checkout error rate help you identify the cause.

Defining Metric Specifications

Each metric in your framework needs a clear specification:

• Definition: Exact calculation formula, including what is included and excluded
• Data source: Where the data comes from and how it is collected
• Refresh frequency: How often the metric updates (real-time, daily, weekly)
• Owner: Who is responsible for monitoring and acting on this metric
• Thresholds: What values are acceptable, concerning, or critical
• Action triggers: What happens when the metric crosses a threshold

Without these specifications, metrics become vanity numbers that nobody trusts or acts on. We work with your team to document and implement these definitions across your analytics stack.

Aligning Metrics Across Teams

Marketing, operations, finance, and product teams often track different numbers that tell conflicting stories. A measurement framework creates shared definitions so everyone works from the same truth. Revenue means the same thing in the marketing dashboard as it does in the finance report. Customer count uses the same deduplication logic everywhere.

This alignment is particularly important when your data flows through integrations connecting multiple systems. Consistent definitions ensure that automated data pipelines produce trustworthy outputs.

Data Sources and Integration

E-commerce analytics is only as good as the data feeding it. Most businesses have valuable data scattered across a dozen or more platforms. The challenge is bringing it together cleanly.

Core Data Sources

Web Analytics (GA4, Matomo, Piwik PRO)

• User behavior: pageviews, sessions, events
• Traffic sources and campaign attribution
• E-commerce events: product views, add-to-cart, purchases
• Site performance: page load times, Core Web Vitals

E-Commerce Platforms (Shopify, WooCommerce, Magento, Lightspeed)

• Order data: items, quantities, discounts, totals
• Customer records: contact info, order history, segments
• Product catalog: pricing, stock levels, categories
• Checkout funnel: step completion, payment methods

ERP and Accounting Systems (Exact, Multivers, SAP)

• Financial data: revenue, costs, margins
• Inventory: stock levels, purchase orders, lead times
• Fulfillment: shipping costs, processing times
• Returns and refunds: reasons, costs, timelines

Advertising Platforms (Google Ads, Meta, TikTok, Amazon)

• Campaign performance: impressions, clicks, costs
• Audience data: demographics, interests, lookalikes
• Attribution: conversion paths, assisted conversions
• Creative performance: which ads drive results

Marketplace Platforms (Amazon, Bol.com, Zalando, Kaufland)

• Sales and revenue by marketplace
• Buy Box percentage and pricing data
• Reviews and ratings
• Inventory and fulfillment metrics

Customer Service (Zendesk, Freshdesk, Gorgias)

• Ticket volume and resolution times
• Common issue categories
• Customer satisfaction scores
• Impact on retention and reviews

Data Integration Architecture

Raw data from these sources needs to be extracted, transformed, and loaded into a central location before it becomes useful for analytics. We typically implement one of three architectures depending on complexity and budget:

Direct API connections: For simpler setups, we connect data sources directly to your dashboard tool using native connectors or lightweight middleware. Suitable when you have fewer than five data sources and standard reporting needs.

Cloud data warehouse: For more complex operations, we implement a proper ETL pipeline feeding a cloud data warehouse (BigQuery, Snowflake, or similar). Data is extracted on schedule, transformed to match your metric definitions, and made available for flexible querying and visualization.

Custom integration layer: For businesses with unique data structures or real-time requirements, we build custom data processing solutions that handle extraction, validation, transformation, and delivery to your analytics tools.

Regardless of architecture, data quality is non-negotiable. We implement validation checks, deduplication logic, and monitoring to ensure the numbers in your dashboards reflect reality. Learn more about how we approach system integrations.

Dashboard Tools and E-Commerce Data Visualization

A dashboard that nobody opens is worse than no dashboard at all. Effective e-commerce reporting requires choosing the right tool for your team and designing visualizations that drive action.

Tool Selection

Looker Studio (Google Data Studio) Best for: teams already in the Google ecosystem, standard web analytics reporting, budget-conscious setups. Native integration with GA4, Google Ads, and BigQuery. Free to use with generous limits. Limitations appear with complex data transformations and large datasets.

Power BI Best for: organizations using Microsoft 365, complex data modeling, enterprise-scale reporting. Strong DAX formula language for advanced calculations. Better handling of large datasets and complex relationships. Requires Power BI Pro licenses for sharing.

Tableau Best for: data-heavy organizations needing advanced visual analytics. Superior visualization capabilities and exploratory analysis. Higher cost and steeper learning curve. Best suited for teams with dedicated analysts.

Custom Dashboards Best for: unique requirements that off-the-shelf tools cannot meet. Built with frameworks like React and D3.js, connected to your data warehouse via APIs. Full control over design, functionality, and user experience. Higher development cost but unlimited flexibility. We build these as part of our custom software practice.

Dashboard Design Principles

Regardless of tool, effective dashboards follow consistent principles:

One purpose per view: Each dashboard page should answer a specific question. Do not combine acquisition metrics with operational metrics on the same screen.

Progressive disclosure: Start with high-level KPIs, then allow drill-down into details. Executives need the summary; analysts need the granularity. Both should be served.

Context over raw numbers: A conversion rate of 2.3% means nothing without context. Show trends, comparisons to previous periods, and targets. “2.3% vs. 2.1% last month and 2.5% target” tells a story.

Actionable annotations: When metrics move significantly, the dashboard should help explain why. Annotations for campaigns launched, site changes deployed, or seasonal events provide context.

Appropriate refresh rates: Real-time data is not always necessary. Marketing dashboards updating daily are fine. Inventory availability may need hourly updates. Match refresh frequency to decision frequency.

Common Dashboard Views

For most e-commerce businesses, we recommend building these core views:

1. Executive Summary: North Star metrics, revenue trend, key driver metrics, alerts
2. Marketing Performance: CAC, ROAS, channel comparison, campaign performance
3. Conversion Funnel: Traffic to purchase funnel, drop-off analysis, segment comparison
4. Product Performance: Top sellers, margin analysis, inventory health, pricing insights
5. Customer Analytics: Cohort analysis, CLV trends, retention curves, segment behavior
6. Operations: Order processing, fulfillment accuracy, return rates, customer service metrics

Conversion Rate Optimization Methodology

Conversion rate optimization (CRO) is not about random A/B tests or copying competitors. It is a structured process for identifying friction, prioritizing improvements, and validating changes with data.

The CRO Process

Step 1: Quantitative Analysis Start with the data. Identify where users drop off in your funnel, which pages have high exit rates, where errors occur, and which segments underperform. GA4 funnel reports, session recordings, and heatmaps provide the raw material.

Step 2: Qualitative Research Numbers tell you what happens. Research tells you why. Customer surveys, usability testing, session replay analysis, and customer service ticket analysis reveal the friction points that quantitative data alone cannot explain.

Step 3: Hypothesis Formation Combine quantitative and qualitative findings into testable hypotheses. A good hypothesis follows this format: “Because we observed [data/insight], we believe that [change] will cause [outcome], which we will measure by [metric].”

Step 4: Prioritization Not all hypotheses are equal. We use the ICE framework (Impact, Confidence, Ease) or similar prioritization models to rank opportunities. High-impact, high-confidence, easy-to-implement changes go first.

Step 5: Testing Implement changes as A/B tests or controlled rollouts when traffic volume supports statistical significance. For lower-traffic sites, sequential testing or before/after analysis with statistical adjustments may be necessary.

Step 6: Analysis and Iteration Evaluate test results against pre-defined success criteria. Winning variants get implemented permanently. Losing variants provide learning for future hypotheses. Document everything.

Common Conversion Opportunities

Based on our work with European e-commerce businesses, these areas consistently yield improvements:

• Checkout simplification: Reducing steps, eliminating unnecessary fields, adding guest checkout options
• Page speed: Every 100ms of load time improvement can increase conversion by 1-2%
• Product page content: Better images, size guides, reviews, and availability information reduce uncertainty
• Search and navigation: Helping users find products faster dramatically improves conversion
• Trust signals: Reviews, security badges, clear return policies, and local payment methods (especially important for cross-border European commerce)
• Mobile experience: With mobile traffic often exceeding 60%, mobile-specific optimization is essential

Testing Infrastructure

Effective CRO requires proper tooling. We implement and configure testing platforms (Google Optimize alternatives, VWO, AB Tasty, or custom solutions) with correct goal tracking, audience segmentation, and statistical rigor. We also ensure test results feed back into your analytics framework so you can measure long-term impact beyond the test period.

Multi-Channel Attribution

When customers interact with your brand across multiple touchpoints before purchasing, understanding which channels actually drive revenue is critical for budget allocation. Multi-channel attribution answers the question: “Where should I invest my next euro?”

Attribution Models

Last-Click Attribution: Credits the final touchpoint before purchase. Simple but misleading - it ignores the awareness and consideration phases entirely.

First-Click Attribution: Credits the first touchpoint. Useful for understanding acquisition channels but ignores the nurturing that leads to conversion.

Linear Attribution: Distributes credit equally across all touchpoints. Fair but unsophisticated - not all interactions contribute equally.

Time-Decay Attribution: Gives more credit to touchpoints closer to conversion. A reasonable default for most e-commerce businesses.

Data-Driven Attribution: Uses machine learning to assign credit based on actual conversion patterns in your data. Requires significant data volume but provides the most accurate picture. Available in GA4 for properties with sufficient conversion data.

Incrementality Testing: The gold standard. Uses controlled experiments (geo-tests, holdout groups, or platform-level lift studies) to measure the true causal impact of each channel. More complex to implement but eliminates the guesswork of model-based attribution.

Practical Attribution for E-Commerce

For most European e-commerce businesses, we recommend a pragmatic approach:

1. Use data-driven attribution in GA4 as your primary model for digital channels
2. Supplement with incrementality tests for your largest spend categories (typically paid social and paid search)
3. Track blended metrics (total revenue / total spend) alongside channel-specific ROAS to maintain perspective
4. Account for offline touchpoints where relevant (retail, events, phone orders)
5. Re-evaluate quarterly as channel mix and customer behavior evolve

Cross-Device and Cross-Channel Challenges

European e-commerce faces specific attribution challenges:

• Privacy regulations (GDPR) limit tracking capabilities, making server-side tracking and first-party data strategies essential
• Cookie consent reduces the observable user journey, creating data gaps that attribution models must account for
• Multiple marketplaces (Amazon, Bol.com, Zalando) provide limited customer journey data, making it harder to understand how brand advertising drives marketplace sales
• Cross-border complexity means customers may interact with different language versions of your site before purchasing

We help you build attribution approaches that work within these constraints, using server-side tracking, enhanced conversions, and statistical modeling to fill the gaps left by browser-side limitations.

Automated Reporting and Alerts

Manual reporting wastes analyst time and introduces inconsistency. Automated reports ensure stakeholders get the right information at the right time without requiring someone to pull numbers every morning.

Reporting Cadence

Daily: Key operational metrics (orders, revenue, stockouts, errors). Delivered automatically to Slack, email, or a shared dashboard. Focused on detecting anomalies quickly.

Weekly: Performance trends, marketing channel updates, conversion funnel changes. Provides enough data for meaningful comparisons while maintaining actionable frequency.

Monthly: Deep-dive analysis, cohort comparisons, strategic metrics (CLV, market share, margin trends). Supports monthly planning and budget allocation decisions.

Quarterly: Business reviews, goal tracking, competitive analysis, strategy adjustments. Informs quarterly planning and investment decisions.

Alert Systems

Beyond scheduled reports, proactive alerts notify your team when metrics cross critical thresholds:

• Revenue alerts: Daily revenue falls below expected range (accounting for day-of-week and seasonal patterns)
• Conversion alerts: Conversion rate drops significantly, indicating potential site issues
• Inventory alerts: Stock levels approaching zero for high-velocity products
• Error alerts: Checkout error rate spikes, payment failures increase, 404 pages multiply
• Budget alerts: Ad spend pacing ahead or behind targets
• Performance alerts: Page load times exceed acceptable thresholds

Alerts should be actionable and rare enough to avoid fatigue. We configure thresholds based on your historical data and business tolerance, ensuring alerts represent genuine issues rather than normal variation.

Integration with Workflows

The best alerts do more than send notifications. They can trigger automated responses:

• A stockout alert triggers a purchase order in your ERP
• A conversion drop alert creates a ticket for your development team
• A budget alert pauses or adjusts campaign spending
• A shipping delay alert sends proactive customer communications

These automated workflows connect your analytics layer to your operational systems through the integrations we build.

From Insights to Action: The Optimization Roadmap

Analytics without action is just expensive observation. The real value comes from systematically turning insights into improvements. Here is how we structure optimization engagements.

Phase 1: Audit and Foundation (Weeks 1-3)

• Audit current tracking implementation and data quality
• Document existing data sources and access
• Define measurement framework (North Star, drivers, diagnostics)
• Identify quick wins from existing data
• Establish baseline metrics for future comparison

Phase 2: Infrastructure (Weeks 3-6)

• Implement or fix tracking (GA4 configuration, enhanced e-commerce, server-side where needed)
• Set up data pipelines connecting your sources
• Build core dashboards matching your measurement framework
• Configure automated reports and critical alerts
• Validate data accuracy across sources

Phase 3: Optimization (Ongoing)

• Run monthly analysis cycles to identify opportunities
• Prioritize and implement CRO experiments
• Monitor test results and scale winners
• Refine attribution model and budget allocation
• Expand measurement to new channels or touchpoints

Phase 4: Maturity (Quarters 3+)

• Predictive analytics: forecasting demand, CLV prediction, churn risk scoring
• Advanced segmentation: behavioral clusters, personalization triggers
• Automated optimization: algorithmic bidding, dynamic pricing inputs, inventory optimization
• Custom tooling for unique analytical needs via our custom software capabilities

Measuring the Impact of Analytics

How do you measure whether your analytics investment is working? Look for:

• Faster decision-making: Time from question to data-backed answer decreases
• Fewer surprises: Issues are caught by alerts before customers report them
• Better allocation: Marketing spend shifts toward proven high-performers
• Reduced waste: Stockouts, over-ordering, and ineffective campaigns decrease
• Revenue growth: Conversion rate, AOV, and CLV trend upward as optimizations compound

Our case studies include examples of analytics implementations that delivered measurable returns for European e-commerce businesses.

Working With Duxly on Analytics and Optimization

We work with e-commerce businesses across Europe, from growth-stage DTC brands to established multi-channel retailers. Our analytics practice is built on three principles:

Pragmatism over perfection: We start with what delivers value fastest and build toward sophistication over time. A simple dashboard that gets used beats a complex one that gets ignored.

Integration expertise: Analytics is only as good as the data feeding it. Our deep experience with e-commerce integrations means we can connect systems that other analytics consultants cannot.

Ownership transfer: We build solutions your team can maintain and extend. Documentation, training, and clear handover are part of every engagement.

Whether you need a focused conversion audit, a full measurement framework, or an ongoing optimization partnership, we structure engagements to match your needs and internal capabilities.

Start Making Better Decisions With Your Data

If your e-commerce data is scattered across disconnected tools, your team is spending hours on manual reporting, or you suspect there are conversion opportunities you are not seeing, we should talk.

Get in touch to discuss your analytics needs. We will start with a brief assessment of your current setup and identify where the biggest opportunities lie. No obligations, no generic proposals. Just a practical conversation about turning your data into growth.

You can also explore our integration services to understand how we connect the systems that feed your analytics, or browse our blog for practical e-commerce optimization insights.