e-commerce-project-case-study-maeju
Categories: |2.2 min read|
Sector: Ecommerce

Project Context

A growing ecommerce brand relied heavily on paid and organic channels, but decision making was constrained by unreliable analytics. GA4 was installed, yet the data lacked structure, key events were missing, attribution was inconsistent, and revenue reporting did not align with platform reality.

The objective was to rebuild measurement so that marketing, merchandising, and management could act on accurate, stable data.

Services:
  • Analytics

  • Tracking

  • Measurement Framework

  • Signal Stabilisation

Commercial Problem

Three issues limited performance visibility and optimisation:

  1. Unstructured event tracking
    Events triggered in irregular patterns. Some were duplicated, others never fired. GA4 could not differentiate between meaningful actions and noise.
  2. Incorrect ecommerce configuration
    Transactions, product interactions, and checkout events did not reflect real customer behaviour. As a result, revenue reports fluctuated and could not be trusted.
  3. Attribution inconsistency across channels
    Paid search, email, and direct traffic appeared disconnected. Smart Bidding lacked accurate conversion signals and undervalued key channels.

The result was uncertainty. Marketing decisions were reactive rather than data led.

Strategic Approach – Event Structure

The goal was to create a stable, interpretable measurement layer that supported both performance marketing and wider business decisions.

A clean event architecture was established:

  • 1
    standardised ecommerce events
  • 2

    removal of redundant events

  • 3

    uniform naming conventions for consistency

This reduced ambiguity across all traffic sources.

Ecommerce Tracking Repair

The Shopify integration was corrected to ensure:

  • consistent transaction reporting

  • accurate product and variant data

  • correct checkout events

  • alignment between storefront behaviour and GA4 records

GA4 now reflected what customers actually did, not what the system approximated.

Attribution Alignment

GA4 was connected to Google Ads and Search Console with proper attribution settings, allowing:

  • clearer channel contribution

  • accurate reporting of assisted conversions

  • stronger bidding signals for paid campaigns

Data Quality Controls

Data retention settings, user provided data, internal traffic rules, and referral exclusions were configured to ensure long term reliability.

clear approach

Stable, interpretable measurement layer

clear approach

project outcome

Accurate revenue reporting

project-outcome

Outcome

Following the overhaul, the business gained clarity in four areas:

  1. Accurate revenue reporting
    Platform mismatches were removed. Revenue in GA4 aligned with the store’s real figures.

  2. Clear attribution across channels
    Paid search, social, email, and direct traffic were now weighted correctly, supporting better budget decisions.

  3. Reliable foundation for performance marketing
    Google Ads received consistent, trustworthy conversion data. This strengthened Smart Bidding and reduced waste.

  4. Improved intelligence for merchandising and inventory
    With product level tracking operational, the business could see which items drove value, not only which items drove traffic.

The brand moved from uncertainty to clarity, with analytics now functioning as an operational asset rather than a reporting tool.

Clear Approach

  • event structure

  • signal reliability

  • channel alignment

Project Outcome

  • accurate data

  • improved attribution

  • stable foundation for marketing and product decisions

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