How a large ecommerce brand rebuilt trust in its analytics

When ecommerce analytics tells different stories

Analytics played a central role in the organisation’s acquisition performance, merchandising decisions, conversion optimisation, retention strategy and revenue forecasting. Over time, confidence in that data declined.

The team regularly compared Shopify, GA4, Meta Ads, Google Ads and internal business intelligence dashboards, but each system provided a different view of performance. In one example, Meta attributed €1.8 million in revenue while GA4 reported €1.2 million, with Shopify showing a figure between the two.

These discrepancies made attribution harder to explain internally. Acquisition, CRM and finance teams were looking at different signals when assessing marketing performance, while leadership wanted to understand which channels were contributing to profitable growth.

Why confidence in the data declined

The team identified several factors behind the problem:

• Attribution inconsistencies
• Consent mode limitations
• Tracking gaps
• Cross-device fragmentation

The consequences reached beyond reporting. Uncertainty about the revenue journey affected budget allocation, forecasting, acquisition strategy, conversion rate optimisation and retention investment.

At the same time, the company was increasing its paid social spend. Traffic was growing and add-to-cart rates appeared healthy, but other commercial metrics were moving in the opposite direction.

Marketing Efficiency Ratio was declining, contribution margins were tightening, returning customer growth had slowed and revenue growth was flattening despite higher spend.

The team responded by refreshing creative, increasing retargeting budgets and testing new landing pages. These changes did not solve the underlying measurement problem.

Rebuilding the ecommerce revenue funnel with Matomo

As confidence in reporting continued to decline, the organisation reassessed its analytics stack while continuing to use GA4.

It evaluated several enterprise analytics platforms, including Matomo, Piwik PRO and Piano Analytics, against six areas:

1. End-to-end funnel visibility
2. Data accuracy
3. Operational usability
4. Privacy resilience
5. Governance requirements
6. Long-term scalability

Matomo stood out for its ability to provide a more complete view of the ecommerce funnel across teams and markets.

The team rebuilt the revenue funnel in Matomo from product view through add to cart, checkout, payment and purchase. This made it easier to analyse the complete revenue path instead of reviewing acquisition and product data separately.

Looking beyond traffic volume

The fuller view of the revenue funnel changed how the team assessed acquisition performance.

Google Ads generated nearly half of all sessions and most add-to-cart activity, but it produced disproportionately little revenue.

SEO brought less traffic, yet those visits were associated with:

• Higher average order value
• Stronger repeat purchase behaviour
• Better margin contribution

The analysis showed that acquisition volume alone did not provide enough information to judge channel performance. Revenue quality and customer behaviour also mattered when deciding where to invest.

Finding a checkout issue that fragmented reporting had obscured

The team also identified a significant drop in checkout completion rates affecting Safari users and iPhone traffic shortly after an update to its payment provider.

Previously, information about the problem was spread across several systems. Paid media platforms used their own attribution logic, GA4 made the behaviour difficult to isolate clearly, and product and acquisition data sat in separate environments.

Bringing the funnel together made the pattern easier to identify.

The checkout issue was resolved within days. Combined with the finding that SEO had been undervalued as a growth channel, the recovered revenue helped justify the investment in the analytics migration and informed later budget decisions.

Improving measurement in consent-sensitive markets

Privacy and compliance requirements were also becoming more important, particularly in France and Germany.

In France, the Matomo implementation was configured to align with CNIL consent-exemption requirements. According to the case study, this helped the organisation regain visibility into a substantial share of previously unmeasured traffic and increased measurable traffic visibility by an estimated 35%.

The additional visibility helped teams understand:

• Where people dropped out of the revenue funnel.
• Which acquisition channels brought profitable customers.
• Where checkout friction affected revenue.
• How customer quality differed between channels and countries.

The example shows how a privacy-focused analytics setup can support more complete measurement when the implementation meets the relevant requirements.

What changed after the analytics setup improved

The case study describes several outcomes following the implementation:

• Around 35% more measurable traffic visibility in France.
• Faster identification of checkout issues.
• Greater confidence in investment decisions.
• More focus on profitable growth rather than traffic volume alone.
• Less time spent reconciling conflicting reports across teams.

These changes gave marketing, ecommerce, finance and leadership teams a clearer basis for assessing performance across acquisition, conversion, retention and revenue.

What you’ll learn in the case study

Download the full case study for a closer look at the challenges behind the findings, the analysis that uncovered them and how the team used Matomo to build a clearer view of the ecommerce revenue funnel.

You’ll get more detail on:

• The attribution and tracking issues that reduced confidence in reporting.
• How the team evaluated its analytics setup.
• What changed once acquisition, conversion and revenue data were viewed together.
• How checkout friction affecting Safari and iPhone traffic was identified.
• Why SEO proved more valuable than traffic volume alone suggested.
• How the setup in France improved measurable traffic visibility.
• How more reliable data supported faster analysis and better investment decisions.