A 7-video Meta test showed why creative analysis cannot stop at the hook. Different assets won different parts of the funnel: strong attention did not automatically produce the strongest downstream conversion.
02Actions and reasoning
What I contributed
Performance-testing layer, creative-performance analysis, hook / format references, feedback and support for test-and-scale decisions. Creative direction itself was collaborative.
Why I compared the full funnel
Connected hook and hold signals to landing-page behaviour, conversions and campaign economics instead of optimizing for views alone.
Judge attention alongside conversion and campaign economics.
03Evidence and context
7
Videos compared
64%
Strongest hook signal
4.78%
Top LP conversion
Top-volume format — useful attention, judged alongside downstream behaviour.View original post ↗Efficiency comparison — the funnel outcome mattered more than a single top-of-funnel metric.View original post ↗
These are separate signals from a seven-video comparison, not a single creative’s combined result.
04Outcome
The comparison showed why the strongest hook and the highest landing-page conversion needed to be evaluated separately when choosing what to test and scale.
05Learning:
Hook → behaviour → conversion → economics. Attention is an input, not the finish line.
PROJECT 02 / CAMPAIGN OPERATIONS
Campaign oversight at scale.
A repeatable review workflow made the wider campaign scope manageable.
Challenge
Repeated platform checking became an attention bottleneck as concurrent campaign work increased.
My approach
Standardized reporting and scheduled checks to surface exceptions for human review.
10Meta sales channels
4Google Search campaigns
Current operating scope.
My scope: Hands-on channel review. Two Meta portfolios independently managed; selected decisions in the third were shared with the team.
Read the monitoring workflow
01The challenge
As concurrent campaign work increased, repeated platform checking became an attention bottleneck. The challenge shifted from “can I operate the ad platforms?” to “how do I keep a larger scope observable without making manual checking the system?”
02Actions and reasoning
My responsibility
I monitor and optimize all 10 Meta sales channels individually and manage all four Google Search campaigns hands-on. Two Meta portfolios are independently managed within my assigned scope; selected decisions in the third portfolio are shared with the team. I also define reporting requirements and validate AI-assisted implementation.
How I approached it
Standardized reporting, scheduled checks, API-assisted data retrieval, campaign health rules, anomaly surfacing, structured trackers, QA and human review before meaningful actions.
Keep the wider scope observable while preserving human judgment.
03Evidence and context
10
Meta sales channels
3
Meta portfolios
4
Google Search campaigns
1-by-1
Channel-level review
Workflow
Ad PlatformsMeta + Google Search
→
Data RetrievalAPI-assisted pulls + reports
→
Rules & ChecksHealth, pacing, anomalies
→
AI SummariesSummaries + prioritization
→
Human DecisionContext, budget, exceptions
Operating scope: 10 Meta sales channels across three portfolios, plus four Google Search campaigns.
What the system produces
Four scheduled daily reports, hourly Friday updates where configured, and campaign health signals for exception review.
What I own
Campaign logic, rule thresholds, output requirements, QA and final budget decisions. AI assists with scripts, API wiring and summaries.
04Outcome:
Made simultaneous oversight across a much larger paid-media scope operationally manageable without relying solely on repeated manual platform checks.
05Learning:
Scale human attention, not manual checking.
PROJECT 03 / MEASUREMENT
Reconciling ads with payments.
A clearer view of which records to trust and which fixes to prioritize.
AI-generated concept: comparing ad reports with payment records.
Challenge
Google Ads conversion reporting and payment data did not always agree.
My approach
Cross-checked campaign records and isolated tracking, URL and conversion-value gaps.
My contribution: Conversion diagnosis, cross-checking and prioritization.
Read the tracking investigation
01The challenge
Google Ads conversion reporting and payment data did not always agree. Instead of treating every discrepancy as “bad attribution,” I investigated the failure mode campaign by campaign.
02Actions and reasoning
What I checked
Cross-checked Google Ads conversion records against payment verified source, then isolated missing purchase tracking, URL mismatch, static conversion-value problems and other conversion-flow issues where applicable.
Why I prioritized the fixes
Separated severe measurement gaps from lower-priority imperfections, then prioritized fixes by what most affected decision quality rather than changing everything at once.
Prioritize fixes that most affect decision quality.
03Evidence and context
Workflow
Ad PlatformReported conversions / value
↔
Tracking LayerGA4, URL, events, values
↔
Confirmed PaymentsConfirmed payment data
→
DecisionTrust, fix, or qualify
Each discrepancy was checked across reported conversions, the tracking layer and confirmed payments.
04Outcome:
Created a clearer map of what could be trusted, what was undercounted or mis-valued, and which measurement fixes should be prioritized.
05Learning:
Ad-platform reporting is a measurement layer. It is not automatically the business verified source.
PROJECT 04 / REPORTING & ANALYTICS
Campaign intelligence in Power BI.
One report for campaign, creative and donor questions.
Challenge
Campaign reviews needed a connected view of performance, funnel behaviour and donor activity.
My approach
Developed report views around campaign filters, comparison windows and donor eligibility rules.
My contribution: Analysis requirements, comparison logic, QA and validation. Technical implementation was AI-assisted.
Actual Power BI report. Performance values redacted. Report labels and context retained.
Read the Power BI case
01The challenge
Campaign performance, creative behaviour and repeat donations answer different questions. I needed report views that made those questions easier to investigate while keeping each metric’s source and time window clear.
02Actions and reasoning
What I owned
Defined the analysis questions, campaign/date filters, comparison windows and output requirements. Reviewed and validated outputs; technical implementation was AI-assisted.
How the views work together
Weekly monitoring compares periods and day groups. Creative intelligence follows link clicks through landing-page visits to purchases. Donor views examine repeat activity and eligible lifetime-value cohorts.
Use each view to identify what needs a closer check, then verify the relevant records before acting.
03Evidence and context
The live report contains Overview, LTV Control Center, Weekly Monitoring, Video Creative Intelligence, and Donor Journey & Campaign Audit. These screenshots preserve the actual report layout, with performance values redacted and the report context retained.
Weekly Monitoring — comparable periods, day groups and exceptions.Donor Journey & Campaign Audit — migration, repeat timing and cohort quality.
Creative performance is Meta-attributed. Weekly income uses matched OnPay records where available, otherwise Meta; purchases and cost per purchase use Meta. LTV365 includes donors observed for at least 365 days, with gross revenue before refunds and provisional email/phone identity matching.
04Outcome
A connected report for period comparisons, creative diagnosis and donor/cohort review, with the measurement definitions visible alongside the analysis.
05Learning
Before comparing results, check the source, the time window and which donors qualify.
More campaign examples
Google Search: from intent to live optimisation
Google Search · End-to-end operations
Search work can fragment across keyword research, ad copy, platform changes and search-term reviews. I use one operating flow from the campaign objective through launch and optimisation.
01 · IntentResearch keywords; group demand and exclusions.
02 · Structure & messageMap ad groups and responsive search ads to intent.
03 · Launch & QAApply changes, read back the setup and verify it.
Search strategy, keyword decisions, campaign structure, review criteria and final optimisation decisions. AI assists with technical execution where useful.
Result
A structured research-to-review workflow with campaign logic and validation kept under human control.
Capturing Search Demand During Arafah
Google Search · Peak-intent campaign
During a coordinated SMS and Google Search campaign, Search ads captured 128 platform-reported conversions in a two-day window. This is a campaign outcome, supported by advertising export and reporting checks.
My contribution
Managed the Search capture work and applied the learning to later campaign structure, keyword intent, negative keywords, messaging and performance review.
Measurement context
The two-day campaign result covers May 25–26, 2026. Platform-reported conversions are not a count of independently confirmed donors, and the result reflects the wider campaign activation.
Testing Creative Approaches for Winter Relief
Creative Testing · Winter Relief
A seasonal fundraising campaign provided a setting to compare content formats and use performance feedback to guide further tests.
My contribution
Contributed content references, format ideas, performance feedback and testing recommendations alongside marketing leadership and the creative team. I also appeared on camera in a campaign video.
What Carried Forward
Used campaign and creative comparisons to inform later tests. Creative development and campaign delivery were collaborative team work.
Campaign creative — my on-camera contribution within a collaborative team effort.View original post ↗
Learning: When a campaign is flat, test the system before blaming the audience. Creative, message, funnel and measurement can all be constraints.
About Naqiuddin
Marketing experience, applied to better systems.
Based in Shah Alam, Malaysia. Performance marketing is my anchor; analytics, automation and web work help turn that experience into useful client deliverables.
I work hands-on with Meta Ads and Google Search, review measurement gaps and build reporting workflows. My approach is to define the question, check the source and make the next action clear.
AI helps with implementation and repetitive work. I own the requirements, review the outputs and keep meaningful decisions with people.
Each project has its own purpose. AI education lives on Celik AI; products and client sites keep their own identities.
Client site / case study
Setia Alam Dental
A local dental website project covering research, site architecture, build and SEO foundations, with a clearer route from treatment information to enquiries.
Scope evidence: the client website. No traffic or revenue uplift is claimed here.
Supported event documentation and designed a human-in-the-loop synthesis workflow for live multi-speaker discussion. Live notes stayed authoritative; AI expanded coverage and helped structure the material.
The Brainy Marketers · TBM 3.0.Read the documentation workflow
WorkflowAI-generated workflow diagram. Live notes remain authoritative; transcription and synthesis include human checks.
Live Notes: Source-of-truth baseline
9 Audio Chunks: Manageable recordings
ASR + QA: Transcribe, then verify
Cross-check: Speakers, topics, confidence
Synthesis: Publication-ready knowledge
Transcript QA before downstream use
Timestamped argument extraction and speaker attribution
Cross-checks against live notes / other outputs
QR-based Google Sheets attendance workflow for participant check-in
Lakefront Basketball Veteran Team
Team Manager
I coordinate player communication, schedules and matchday logistics for a community basketball team.
After the final game of the NCBL Veteran 35 league at Sekolah Han Ming. I’m on the far left.Team coordination responsibilities
Player communication
Scheduling and matchday preparation
Basic logistics
Cross-generational rapport
Informal community coordination
Basketball PIC · Karnival Sukan Lakefront 2026
I was the sole basketball PIC for the Lakefront sports carnival in September 2026, coordinating 21 players, four teams and a 12-match schedule.
I set the team-draw rules and used AI to help build and check an Apps Script app. Google Sheets kept grouping results, match records and standings together.
