Article-level visibility
Case study · 01LinkedIn ↗
SEO / AEO · Shopify SaaS
Building visibility from scratch—and recovering what search had eroded.
I created an attribution-safe measurement system for seven content assets I personally owned, separating new-demand acquisition from organic recovery and tracing performance from search visibility to early product intent.
01 / Evidence dashboard
Measured only where I could defend the attribution.
Choose a metric to see its measurement boundary.
Measurement note
Across the seven URLs, GSC recorded 14,915 search impressions and 56 clicks from June 23 to August 22. In a separate GA4 window, six tracked landing pages generated 178 sessions and reached 130 active users from July 23 to August 23. The two sources are presented side by side—not as a direct conversion rate—because their dates and page cohorts differ.
Trajectory, not a static total
Daily search visibility strengthened 3.0×.
107First two weeks
impressions / day
impressions / day
320Final two weeks
impressions / day
impressions / day
1,496 impressions in the first fortnight → 4,477 in the final fortnight.
Best new-content position#8.32Discount Stacking
Highest CTR1.44%Checkout Examples
Top AI-feature visibility526Hydrogen & Oxygen
Technical-query recovery#4.36“What is Hydrogen & Oxygen”
02 / Source evidence
The story is traceable to the original GSC and GA4 views.
Search visibility is isolated in GSC; downstream landing-page activity is documented separately in GA4.
Interpretation boundary: GSC reports impressions in Generative AI features, not proof of an explicit citation. GA4 covers six landing pages from July 23–August 23, while GSC covers seven URLs from June 23–August 22; the portfolio therefore does not treat the two figures as a direct funnel conversion rate.
03 / Decision anatomy
The work, reconstructed as judgment—not a task list.
Eight questions every project in this portfolio must answer.
Business problem
Skai Lama needed to create visibility around high-intent Shopify merchant problems while recovering articles whose search performance had declined after algorithm changes.
Insight I found
New content and depleted content were doing different jobs. Evaluating both through publishing volume—or claiming company-wide growth—would hide whether my work was actually building or recovering demand.
Recommendation
Isolate the URLs I personally owned, segment them into new and recovery cohorts, then measure search visibility, landing-page behaviour and early product intent separately.
Scope I owned
Four new articles, three strategic refreshes, keyword and query research, content execution, Webflow handoff, reindexing, and the article-level GSC/GA4 evidence model.
Functions coordinated
Marketing leadership, product marketing, product specialists, analytics and web publishing—using product feedback and performance data to shape what was written and how it was measured.
What shipped
Seven measurable assets spanning Shopify discounts, bundles, checkout, Hydrogen/Oxygen and BFCM—each mapped to a specific acquisition or recovery job.
Result
14,915 standard-search impressions, 56 organic clicks and 1,609 impressions in Google's Generative AI features. Standard-search daily visibility rose 3.0× from the first fortnight to the final one.
What my judgment changed
The evaluation shifted from ‘how much did we publish?’ to ‘did each asset build or recover discoverability, attract visitors and create evidence of intent?’—without overstating attribution.
04 / Attribution boundary
Credibility lives in what I refused to overclaim.
During my first 30 days, site-wide organic traffic rose 34.7% and ranking keywords rose 19.4%. Those figures provide company-level context—not proof that my seven articles caused the entire increase.
The defensible portfolio result is the isolated cohort above: the pages I personally wrote or refreshed, measured through their own GSC and GA4 evidence.