Section 1:
Funnel Overview and Data Structure
This analysis uses a GA4 Funnel Exploration built on the official Google Merchandise Store demo. The funnel tracks user progression across the e-commerce journey, from session start through purchase, using event-based steps.
Funnel Steps
Screen / Page view
View item
Add to cart
Add shipping info
Add payment info
Purchase
Each step represents a required event in sequence. Users must complete the previous step to be counted in the next one.
Segments and comparisons
Organic Traffic
Direct Traffic
Paid Traffic​​
These segments are compared side by side within the same funnel to highlight differences in progression and abandonment patterns across traffic sources.
​Breakdown dimension
Desktop
Mobile
Tablet
Smart TV
Device category is used because it is consistently available across all e-commerce events in GA4 and supports reliable comparison within funnel exploration.
​Table structure and metrics
Segment
Device Category
Active Users
Completion Rate
Abandonments
The chart and table show both the overall funnel shape and how different traffic sources and devices behave at each stage of the journey.
Section 2:
Funnel Analysis and Drop-Off Patterns
Identifying the primary drop-off point
The largest drop-off occurs between Screen / Page view and View item.
While nearly all sessions progress to at least one page view, only a significantly smaller proportion of users continue to a product detail page. This indicates that most users disengage before reaching an individual product.
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This pattern positions the discovery-to-decision transition as the most critical point of friction in the funnel.
Organic Traffic
Direct Traffic
Paid Traffic
Behaviour after product view
Organic Traffic
Direct Traffic
Paid Traffic
Once users reach a product detail page, progression through subsequent steps improves noticeably:
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A meaningful proportion of users move from View item → Add to cart
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Completion rates remain relatively stable through shipping information, payment information, and purchase
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This suggests that users who view a product page demonstrate higher intent and are less likely to abandon due to checkout complexity or payment barriers.
In other words, checkout friction is not the primary issue in this funnel.
Device category patterns
Desktop
Mobile
Tablet
Smart TV
Breaking the funnel down by device category shows some variation in completion and abandonment rates across desktop and mobile, particularly at early stages. However, the overall funnel shape remains consistent across devices.
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The same discovery-stage drop-off is present regardless of device, indicating that the issue is structural rather than device-specific. This reinforces the conclusion that optimisation efforts should focus on how users are guided toward products, not on isolated device fixes.
Interpreting abandonment correctly
Organic Traffic
Direct Traffic
Paid Traffic
It is important to distinguish between lack of interest and lack of progression. Engagement metrics and early step completion rates indicate that users are willing to browse pages. The issue lies in converting that browsing behaviour into product-level exploration.
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This type of abandonment is often associated with:
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Insufficient product visibility from entry pages
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High cognitive load when choosing between many options
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Delayed access to product value cues such as price, uniqueness, or relevance
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These interpretations are explored further in the optimisation section.
Key Findings
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The dominant drop-off occurs before product detail views
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Users who reach product pages show stronger intent through checkout
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Device differences exist but do not change the overall pattern
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Checkout and payment stages perform relatively well
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The primary opportunity lies in improving product discovery and decision-stage guidance
Section 3:
Opportunities to Reduce Abandonment Before It Occurs
The funnel analysis indicates that the primary opportunity for improvement lies before users reach a product detail page. As a result, optimisation efforts should prioritise guiding users from general browsing into product-level engagement.
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The goal of this section is not to redesign the entire experience, but to identify targeted, realistic interventions that can reduce early-stage abandonment and increase progression to the decision stage.
Improving product discoverability at entry points
A significant proportion of users exit the funnel after general page views without interacting with specific products. This suggests that users may not be encountering products quickly or clearly enough.
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Potential improvements include:
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Surfacing featured or recommended products earlier on landing and category pages
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Reducing reliance on users scrolling extensively before encountering product options
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Highlighting clear product groupings to reduce choice overload
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These adjustments aim to shorten the distance between arrival and product exposure, especially for new or exploratory users.
Strengthening decision cues before product detail views
Before users click into a product, they often make a preliminary judgement about relevance and value. If these cues are unclear or delayed, users may disengage without progressing further.
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Opportunities at this stage include:
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Making key product attributes visible earlier, such as uniqueness, price range, or use context
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Reducing ambiguity around what differentiates one product from another
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By lowering cognitive effort at the browsing stage, users are more likely to take the next step into product detail pages.
Aligning acquisition messaging with on-site experience
The consistency of the drop-off across organic, direct, and paid traffic suggests that the issue is not isolated to a single channel. However, it remains important that entry-point messaging aligns with what users encounter on-site.
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Opportunities include:
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Ensuring paid or organic messaging accurately reflects what users see upon arrival
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Directing traffic to pages that surface products sooner, rather than broad or content-heavy pages
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This alignment helps reduce friction caused by expectation gaps.
Supporting re-engagement beyond the first session
Not all early-stage abandonment represents failure. Some users may require additional time or reassurance before committing to a product decision.
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Based on this funnel behaviour, re-engagement strategies can play a complementary role by:
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Bringing users back to specific products they are more likely to consider
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Reinforcing value and clarity through follow-up messaging
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Reducing the need for users to restart the discovery process from scratch
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These strategies work best when informed by behavioural insights rather than applied uniformly.
Summary
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Focus optimisation efforts on pre–product detail stages
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Reduce friction between browsing and product exploration
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Surface product value and differentiation earlier
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Maintain alignment between acquisition channels and on-site experience
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Use re-engagement strategically to support undecided users
Section 4:
Connecting GA4 Insights to Email and SMS Campaigns
From behavioural insight to lifecycle action
GA4 funnel analysis highlights a recurring behavioural pattern: users frequently disengage before progressing from product exploration to purchase, while a group of returning customers shows prolonged inactivity after the first transaction. These patterns point to hesitation and relationship distance at different lifecycle stages.
This strategy uses GA4 to distinguish between active consideration, post-hesitation conversion, and long-term inactivity, enabling email and SMS to deliver context-appropriate messaging.
Email as the Primary Channel for Decision-Stage Support
Email is positioned as the primary channel for addressing decision-stage hesitation, where users have demonstrated interest but pause before committing: Viewed an item
Abandoned Checkout Email: Responding to Active Hesitation
Triggered after a product view with a short delay, the abandoned checkout email supports users who disengage during active consideration. The messaging avoids immediate incentives and focuses on:
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Reinforcing product uniqueness and limited availability
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Reducing friction through reassurance, such as saved checkout and free shipping
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An A/B test compares urgency-led framing with reassurance-led framing. This allows the brand to evaluate whether light urgency or calm reassurance better supports progression into the high-intent portion of the funnel.
Post-Purchase Reinforcement: Aligning Timing, Channel, and Intent
GA4 event sequencing highlights purchase as a clear behavioural transition. Customers who convert after hesitation represent a distinct emotional moment, where reinforcement is more appropriate than further persuasion.
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Post-purchase messaging is therefore only triggered for users who complete a purchase after receiving the abandoned checkout email, ensuring relevance and avoiding overlap with earlier buyers.
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SMS is used selectively for opted-in customers as a short, conversational thank-you
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Email delivers the same reward to customers without SMS consent, maintaining fairness
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In both cases, incentives are framed as future-valid appreciation, aligning with slow fashion values and preventing discount dependency.
Email as the Primary Channel for Decision-Stage Support
GA4 is also used to identify lapsed customers with prior purchase history who have not returned for a period. These users are no longer in an active decision state and benefit more from renewed brand context than from repeated reminders.
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The winback email reflects this shift by:
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Being triggered by time-based inactivity
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Offering an optional, low-pressure incentive (e.g., discount code with a longer period of validity)
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Using a small product grid as inspiration, not a conversion push
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This ensures winback messaging is reserved for relationship repair with the inactive customers.
Aligning lifecycle messaging with funnel behaviour
Together, the email and SMS campaigns are designed to address the same decision-stage hesitation identified in GA4, but across different contexts and levels of attention.
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GA4 identifies where and why users disengage
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Email provides space for explanation and reassurance
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SMS provides timely, low-effort reminders
This approach uses behavioural insight to coordinate lifecycle messaging around a shared objective: guiding users back into product-level engagement.
Summary
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GA4 reveals hesitation before purchase and inactivity after prior conversion
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Email addresses both active consideration and long-term re-engagement with different tones and triggers
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A/B testing explores urgency vs reassurance at the point of hesitation
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SMS is reserved for post-purchase reinforcement, not persuasion
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Analytics and lifecycle messaging work together to respect intent, timing, and brand values