A US-based high-ticket marine eCommerce retailer needed a complete growth infrastructure across acquisition, conversion, retention, analytics, SEO, tracking, and commerce operations.

I helped build and manage an omnichannel growth system covering technical SEO, Google Search, Performance Max, Meta Ads, Klaviyo automation, CRO support, analytics, conversion tracking, merchant feed setup, and commerce infrastructure.

During the initial 90-day growth phase, the system generated:

• $65,000+ revenue
• $10,000 ad spend
• 6.5x blended ROAS
• $1,500–$2,000 AOV
• Up to 90% of early purchases influenced by email marketing
• 30,000+ SKU catalogue

Later, a website payment-processing issue disrupted performance. Instead of forcing more spend, I paused campaigns, diagnosed wider conversion issues, supported a CRO-led recovery process, and rebuilt the acquisition strategy.

By mid-June, a controlled Google Performance Max remarketing campaign reached approximately 22x ROAS, mainly driven by high-ticket product sales.

Initial Challenges

The brand had strong commercial potential but weak growth infrastructure:

• 30,000+ SKUs without structured navigation
• Weak homepage positioning
• Poor UX across key buying journeys
• Buggy checkout flow
• Minimal organic visibility
• No lifecycle automation
• No coordinated paid acquisition strategy
• Limited tracking and customer-behaviour visibility
• Disconnected marketing channels
• High-ticket products with longer buying cycles

Growth Infrastructure Built

Before scaling acquisition, I helped establish the foundation across SEO, analytics, tracking, commerce, and lifecycle marketing.

Search & SEO: Google Search Console, Bing Webmaster Tools, IndexNow API, technical SEO review, product/category indexation improvements, site-structure recommendations, and search visibility monitoring.

Analytics & Tracking: GA enhanced eCommerce tracking, Google Tag Manager, purchase and funnel-event tracking, Meta Pixel, server-side Meta CAPI, cross-channel conversion monitoring, and revenue/customer-journey analysis.

Commerce Infrastructure: Google Merchant Center, product feed monitoring and optimization, Facebook Commerce Manager, Facebook Shop, Google Business Profile, and Bing Places.

Lifecycle Marketing: Full Klaviyo eCommerce integration, customer-event synchronization, product and behavioural data integration, reviews integration, and automated segmentation.

Omnichannel Growth Strategy

Each channel had a clear role in the customer journey.

Google Ads captured high-intent demand through Search, Performance Max, Shopping visibility, remarketing, controlled budgets, product-level testing, and high-ticket conversion-value optimization.

Meta Ads supported retargeting, product rediscovery, funnel closure, brand awareness, visitor re-engagement, and demand generation.

SEO improved product discovery, category visibility, indexation, site architecture, search accessibility, and long-term organic opportunities.

Klaviyo converted, recovered, and retained customers who were not ready to buy in their first session. During the early stage, email influenced up to 90% of purchases.

Lifecycle Revenue Engine

The Klaviyo automation system included welcome discount, abandoned cart, checkout abandonment, browse abandonment, product-view follow-up, post-purchase, re-engagement, high-ticket buyer reinforcement, and review/trust-building flows.

Initial Paid Media Validation

The paid strategy began with around $1,000 validation spend across Google Search and Performance Max to validate demand, identify high-intent traffic, measure product-level conversion behaviour, test acquisition costs, establish baseline data, and guide channel allocation.

Initial 90-Day Performance

Metric	Result
Total Revenue	$65,000+
Total Ad Spend	$10,000
Blended ROAS	6.5x
AOV	$1,500–$2,000
Email Influence	Up to 90%
Catalogue	30,000+ SKUs

Performance Disruption and Recovery

March: Payment Failure and Pause

In March, a payment-processing issue affected checkout reliability. Sending paid traffic to a broken payment system would have wasted budget and corrupted performance data, so I paused campaigns.

After the issue was fixed, I relaunched tests, but performance dropped sharply: conversion rates declined, efficiency deteriorated, acquisition became unprofitable, and ad spend stopped producing sustainable returns.

Instead of increasing budget, I paused again.

CRO Diagnosis and Recovery

The payment issue exposed deeper customer-journey problems. With my colleague Abdul Kaium, who led an in-depth CRO audit, we evaluated checkout usability, friction, product pages, trust signals, mobile UX, funnel continuity, payment completion, and high-ticket purchase reassurance.

The goal was not just to restart ads. It was to improve the system receiving the traffic.

April–May: Controlled Relaunch

In April, I relaunched with a reduced budget. But peak boating season had started, and larger competitors were bidding aggressively.

Meta Ads began spending inefficiently, so I paused Meta completely to protect the budget.

Google Ads continued producing sales, mostly lower-ticket purchases, so I kept it active with controlled spend through May to maintain signals, preserve data, capture high-intent demand, observe product-level behaviour, and wait for stronger high-ticket signals.

June: PMax Remarketing Recovery

In June, I launched a low-budget Google Performance Max remarketing campaign targeting users who had already interacted with the website, products, or brand.

Across June, the account generated:

• $58,000+ gross sales
• $51,000+ net sales
• Around $4,000 total ad spend
• Approximately 14.5x ROAS on gross sales
• Approximately 12.75x ROAS on net sales

The PMax remarketing campaign itself reached approximately 22x ROAS by mid-June, driven mainly by high-ticket product sales.

Performance improved across the key signals: high-ticket sales increased, cost per result decreased, revenue quality improved, conversion value increased, and Google Ads became the primary recovery channel.

Campaign Management Approach

I avoided aggressive asset changes, constant bid-strategy switches, frequent restructuring, panic optimization, premature scaling, and decisions based on insufficient data.

Instead, I gradually increased budget and allowed the system to collect meaningful conversion signals.

Only after June 20, once enough value-based conversion data had developed, I shifted the campaign from Maximize Conversion Value to Target ROAS. This avoided restricting delivery before the campaign had reliable conversion-value data.

Key Outcomes

• $65,000+ revenue during the initial 90-day phase
• 6.5x blended ROAS during the initial growth phase
• $1,500–$2,000 AOV
• Up to 90% email-influenced early purchases
• Recovered from unprofitable post-payment-issue performance
• Eliminated Meta budget waste by pausing inefficient spend
• Maintained Google Ads revenue during the rebuilding period
• Generated $58,000+ gross sales and $51,000+ net sales in June
• Achieved June performance from around $4,000 total ad spend
• Reached approximately 14.5x gross ROAS and 12.75x net ROAS in June
• PMax remarketing reached approximately 22x ROAS by mid-June
• Transitioned from Maximize Conversion Value to Target ROAS after enough data

My Contributions

Omnichannel growth strategy, Google Search and PMax management, Meta Ads strategy, budget allocation, campaign pause/relaunch decisions, technical SEO, Merchant Center integration, Meta Pixel and server-side CAPI, GA/GTM tracking, Klaviyo automation, funnel analysis, CRO support, high-ticket customer-journey strategy, reporting, and decision-making.

Strategic Outcome

This was not just an advertising success story. It showed the importance of managing the full growth system.

When performance declined, the right response was not to spend more. It was to pause inefficient acquisition, diagnose technical and conversion issues, fix the journey, protect budget, preserve useful data, wait for reliable signals, and scale only after validation.

Core lesson: Growth marketing is not only about scaling campaigns. It is also about knowing when to pause, what to diagnose, which channel to remove, when to protect budget, and when the data is strong enough to scale.

Build the growth system → Recover the conversion system → Scale the validated signal