WebMetrix

Founder and Web Analyst

Chief Executive Officer

Founder 

JumpBall LLC

E-commerce Marketing Manager

PartnerStack

Sales And Marketing Specialist

10 Minute School

GTM/GA4, Web Analytics and Conversion Tracking/Optimization Specialist | Helping Agencies and Advertisers Track and Maximize Paid Ads ROAS | Generated over £17.5M in additional revenue for my Clients

Your clients rely on you to deliver results. But even if you’re excellent at what you do, inaccurate data or weak tracking can destroy your campaigns.

Here’s the reality:

   ❌Campaign performance suffers because conversions aren’t tracked properly.
   ❌Your clients lose confidence when reports don’t show the full picture.
   ❌Scaling campaigns becomes a guessing game without accurate data.

Sound familiar?

Now imagine this:

    ✅Every click and conversion is accurately tracked across platforms like Google Ads, Facebook, TikTok, GA4 and more.
    ✅Your clients trust your reports and see the ROI of their investment.
    ✅You’re positioned as a reliable expert in your industry, with campaigns that run smarter, not harder.

That’s where I come in.

Here’s how I can help:

    🤝Conversion Tracking Optimization: From server-side tracking to enhanced conversions, I ensure every lead and sale is accurately captured.
    🤝Advanced Analytics Setup: GA4, GTM, and custom Looker Studio dashboards that provide actionable insights for your clients.
    🤝Brand Authority Growth: Helping you communicate results effectively, positioning you as a trusted partner to your clients and skyrocketing revenue without extra ad spend.

The Results?
Save money, achieve higher ROAS, boost client retention, and scale campaigns with confidence. 

If you’re serious about optimizing tracking and taking your ad campaigns to the next level, let’s connect and make it happen.

👉 Shoot Me a DM or BOOK a CALL with Me

I'll see you at the top.

The biggest threat to healthcare ads isn't Meta's policy.

It's how much conversion data you can no longer see.

Meta is limiting conversion visibility
for accounts labeled Health and Wellness.

That label is based on your site and apps.
If assigned, mid- and lower-funnel events
may be hidden from your ad reports.

Events at risk include:
Add to Cart
Purchase
Subscribe
Schedule
Find Location
Contact
Lead

Losing conversion visibility is a nightmare.
But there are practical steps to reduce harm.

Start with these 5 actions:

1. Appeal your categorization
↳ You can request a review from Meta.
↳ Success is rare unless misclassified.

2. Focus on top-of-funnel events
↳ Use page view and search events.
↳ These show interest but not conversions.

3. Use custom events cautiously
↳ You take responsibility for the data sent.
↳ Never send PHI. Limit shared data.

4. Use event codenames carefully
↳ Avoid PHI in event names.
↳ "colonoscopy_scheduled" is risky.

5. Block PHI with a proxy
↳ Use HIPAA-compliant trackers.
↳ Send redacted conversions to Meta.

Health ad compliance keep changing.
Removing the Meta Pixel cripples performance.
So plan for privacy without losing scale.

Which of these will you try first?
Share your approach in the comments below.

♻️ Repost to help other healthcare marketers adapt.
And follow MD.Rakib Hasan 📈  for more practical guides.

MD Rakib Hasan 📈

Helping E-com & Lead Gen Agencies Set Up or Fix Tracking Glitches Before They Eat Your Ad Budget | GA4, GTM, Server-Side & Paid Ads Conversion Tracking | Added £27M+ in Client Revenue | White-Label Partner You Can Trust

Your ads pour money in.
Customers leak out faster.

(Welcome to the trap called "funnel blindness")

I watched it happen to an e‑commerce brand last month.

– Big ad budgets. Low-quality traffic.
– Cracked landing pages. High bounce rates.
– Confusing checkout. Cart abandonment.
– No follow-up. Lost customers.

Here's the ugly truth about broken funnels:

→ High ad spend brings unqualified visitors.
→ Poor landing pages repel interested buyers.
→ Complicated checkout turns intent into loss.
→ No follow-up wastes lifetime value and churns buyers.

I see it everywhere:

– Marketers splurge on clicks, not outcomes
– Founders optimize ads, ignore post-click
– Teams blame creatives, not funnels
– Sales targets miss because retention fails

But here's what an optimized funnel does:

✓ Targeted ads drive qualified traffic
✓ Clear landing pages lower bounce rates
✓ Simple checkout completes purchases
✓ Automated follow-up creates repeat buyers

Because at the end of the day:

Result: Low CPA, high ROAS, scalable growth.

Fix the funnel before you scale the spend.

PS. Which stage in your funnel leaks the most?

MD Rakib Hasan

Helping E-com & Lead Gen Agencies Set Up or Fix Tracking Glitches Before They Eat Your Ad Budget | GA4, GTM, Server-Side & Paid Ads Conversion Tracking | Tracked £50M+ Client Revenue | White-Label Partner You Can Trust

Conversion tracking is used by millions of advertisers.

But most people don't understand its true power.

So I built a conversion tracking checklist showing you how:

Tracking conversions isn't optional.
It's the backbone of scalable growth.

Here's EXACTLY what's inside.

1. How to map conversion events across platforms
2. The exact tags and pixels you must install
3. My validation steps to ensure data accuracy
4. How to attribute conversions to the right channels
5. A 4-step audit you can run in 10 minutes
6. Reporting templates that show true ROI

And so much more, every section is action-first.
I built it from scratch based on live campaigns.

If your numbers feel wrong or unpredictable...

This checklist is exactly what you need today.

Comment CHECKLIST and I'll DM you the link.
Connect with me so I can send it.

PS: What is 1 conversion metric you struggle with?

At scale, your Meta results often look wrong.

But it's usually a tracking mistake.

1. Pixel & Event setup

Then: Pixel firing on every page.
Then: Multiple purchase events sent.
Then: Missing or wrong event parameters.

Result: Inflated or missing conversions.

Today: Fix single pixel per domain.
Today: Validate events in Test Events.
Today: Use proper event names & params.

2. Attribution & reporting mismatch

Then: Using last-touch metrics by default.
Then: Ignoring attribution windows.
Then: Comparing Meta to GA without context.

Result: Numbers that contradict each other.

Today: Match attribution windows.
Today: Align conversion definitions.
Today: Compare apples to apples only.

3. Server-side & deduplication

Then: Browser pixel + server events both sent.
Then: No event_id to dedupe.
Then: Ad blockers blocking browser hits.

Result: Double counts or missing data.

Today: Implement Conversion API.
Today: Send event_id for deduplication.
Today: Monitor matched rate in Events Manager.

One thing that's never changed, though:

Then: I blamed Meta dashboards.
Today: I fix tracking first.

When 1-2-3 above are accurate, results make sense.
Because your data stops lying to you.

Please don't optimize ads until your tracking is clean.

P.S. Ask me about one tracking issue in the comments. Anyone.

When I first posted this concept, it blew up.

This is my no. 1 CAC vs LTV rule:

Customer Acquisition Cost (CAC)
The cost to acquire one customer.

The image breaks down what CAC includes:
1. Marketing spend and campaign costs
2. Sales team salaries and commissions
3. Software and other tools

Goal: Keep CAC low.

Lifetime Value (LTV)
Total revenue a customer brings over time.

LTV depends on three things:
1. Average purchase value per order
2. How often customers buy from you
3. The customer lifespan or retention

Goal: Make LTV high.

Key concept: LTV must exceed CAC
for a profitable business model.

Ideal ratio: LTV: CAC = 3:1

Key takeaway: Acquire customers profitably
and maximize their long-term value.

Question:

Are you tracking LTV vs CAC for your business?

P.S. Repost this so others see it.

Stop wasting money on app-based tracking.
It’s silently failing your campaigns.

Here’s exactly why Custom Pixel + GTM wins on Shopify:

The fix isn't another tracking app.
It's a clear tracking architecture.

Think like an analyst, not an installer.
You need control over data flow and ownership.

High Reasoning Mode
↳ Server-side tracking & end-to-end accuracy

Add these to your setup checklist:

☑︎ "Route events via GTM server"
☑︎ "De-duplicate browser + server hits"  
☑︎ "Validate purchase attribution server-side"

Minimal Reasoning Mode
↳ Quick fixes & simple tasks

Tell your dev or agency straight:

☑︎ "Install client pixel via GTM"
☑︎ "Map purchase and product events"
☑︎ "Send only needed fields"

Verbosity Control → Dial data volume up or down

Control what you send:

☑︎ "Send basic event only"
☑︎ "Send enhanced ecommerce details"
☑︎ "Send raw server events for analysis"

Examples that actually work:

- Short: "Fix missing purchase events in 24 hours."
- Medium: "Implement GTM + server container for better ROAS tracking."
- Long: "Full migration plan: migrate apps, set server GTM, validate, and monitor."

Chain-of-Thought (CoT)
↳ Step-by-step debugging process

"Trace an event: client → GTM → server → vendor. Verify payloads and timestamps."

Perfect for:
- Improving ROAS and cutting wasted ad spend
- Identifying which ads drive real revenue

Tree-of-Thought (ToT)
↳ Compare multiple setups

"Test at least 3 configurations. Compare data loss, latency, and maintenance. Pick the best and document why."

Excellent for:
- Google, Meta, TikTok attribution
- Scaling across Shopify updates

The framework that changes tracking:

1. State the role ("You are my analytics owner")
2. Give context (shopify, apps, ad platforms)
3. Set tracking level (client, server, hybrid)
4. Control verbosity (events to send)
5. Specify output format (raw, aggregated, BI)
6. Ask for improvements ("Audit and harden the config")

Most stores trust app tracking and lose conversions.
Your tracking should be precise, observable, and owned.

Quick tasks = client pixel + basic GTM.
Big migrations = server GTM + full validation.

Stop wasting ad spend on blind data.
Start tracking with Custom Pixel + GTM.

It's accurate, scalable, and future-proof.

Most sites report less data since consent banners rose.

Behavioral modeling in GA4 fills those gaps.

What it does and how to enable it:

1. What modeling estimates
↳ Fills data for users who decline consent
↳ ML trains on consenting users, applies patterns to non-consent

2. Why it matters
↳ Restores more accurate metrics while protecting privacy
↳ Better user counts, sessions, journeys, and geo data

3. Modeled vs observed — quick note
Observed (consent)
↳ Tracked from consenting users
↳ Events tied to sessions

Modeled (estimates)
↳ Estimated for non-consent users
↳ Events are anonymous and aggregated

4. How GA4 builds modeled data
↳ Holdback validation tests — GA4 withholds data to validate predictions
↳ Adaptive models per property — learns from your users, activates only with quality data
↳ Privacy-first approach — anonymisation and Google quality checks

5. Prerequisites to enable modeling
↳ Consent mode must be sitewide (Advanced consent mode)
↳ Tags must load before consent if flags are set before Google tags
↳ Data thresholds:
  ↳ 1,000 denied events daily × 7 days
  ↳ 1,000 granted users daily × 7/28 days

6. How to show modeled data in reports
↳ Admin → Data Settings → Data Display → Select “Blended” → Save
↳ Blended = observed + modeled

7. Where modeled data appears (and where it doesn’t)
↳ Appears: standard reports (higher users & sessions), some geo and journey data
↳ May differ: Explorations
↳ Look for data-quality icons
↳ Not applied to: Audiences, Explorer/Cohort/Lifetime reports, sequence segments, retention, predictive metrics, BigQuery exports

8. Seven-step checklist to start
1) Role: define the property owner and assign consent/modeling owners
2) Task: implement Advanced consent mode; ensure tags load with consent flags
3) Format: choose Blended reporting ID
4) Warnings: monitor modeling limits — it stops if thresholds fail
5) Reasoning: modeling restores visibility while balancing privacy
6) Stop conditions: watch for drops below thresholds or repeated validation failures
7) Context: supply product, region, traffic details to improve relevance

GA4 modelling analysed:

☑︎ Fills gaps from consent declines
☑︎ Keeps user anonymity
☑︎ Restores visibility in core reports
☑︎ Requires sitewide consent mode and volume thresholds


Will you enable modeling in GA4? Share how it goes in the comments.

This week, I'm diving into why server-side tracking matters  
for accurate conversion tracking.

Marketers who rely only on client-side lose data.
Browsers and ad-blockers are silently dropping events.

Server-side tracking captures events reliably.
It moves critical data off the browser.

That reduces ad-blocker loss.
It prevents attribution gaps from breaking metrics.

It also improves data privacy controls.
You decide what to forward to ad platforms.

Because conversion accuracy isn't luck, it's architecture.
You need a reliable pipeline, not fragile scripts.

If you want conversion data you can trust,
Start moving key events server-side today.

The old conversion tag underreports sales.
(Welcome to the fix called Enhanced Conversions)

I watched this change roll out last quarter.

– Your basic pixel? Misses many conversions.
– Enhanced conversion? Matches more users.

Here’s the simple flow the infographic shows:

– User action: customer converts on site.
– Data collection: site captures email or phone.
– Secure upload: hashed data sent via HTTPS.
– Match & attribute: Google links hashes to accounts.

I see it everywhere:

– Marketers blaming poor ROAS.
– Clients asking for more accurate data.
– Agencies switching tag setups overnight.
– Teams scrambling to implement server uploads.

Numbers tell the rest:

– Old tagging → undercounted conversions.
– Enhanced conversions → better match rates.
– Better matches → smarter bidding.
– Smarter bidding → more efficient spend.

What to do now:

✓ Capture first-party contact data securely
✓ Hash data client-side or via GTM
✓ Send via server-to-server HTTPS or API
✓ Use matched conversions for bidding signals

When your conversions improve,
you stop guessing and start scaling.

PS. Have you enabled enhanced conversions yet? What stopped you?

Oh, we are so tracked. Cookies run web analytics.

They quietly follow visits across pages.

1. User visits a website.
2. Server sets a cookie in response.
3. Subsequent visits send that cookie back.

☑︎ Recognizes returning users
☑︎ Tracks sessions and page paths
☑︎ Enables personalization per user
☑︎ Measures conversions and funnels
☑︎ Distinguishes first vs third party

→ Sent in HTTP responses and requests
→ Often includes an ID and expiry date
→ Read by analytics platforms like GA
→ Stored by the browser with each request

There’s no single technical mystery here.
Just a tiny file linking behavior to IDs.

Save this. In 10 years, you'll remember
when small bits of data felt intrusive.

Repost for others who need to see how tracking works.

This is my biggest landing page win.

I fixed conversions without a redesign.

Problem: "My client's landing page isn’t converting."

But I stopped guessing and started measuring.

Solution: How to analyze heatmaps for conversion bottlenecks.
Solution: How to improve CTA placement using scroll depth data.
Solution: How to A/B test headlines and images for impact.

I applied all three in one sprint.

The thing that surprised me, though?

1. No full redesign needed.
2. Conversion lift in just weeks.
3. Changes were cheap and repeatable.

Proof that small, data-led fixes win.

On a scale of 1-10, how urgent is this for you?

P.S. Comment "CHECKLIST" and
I'll send a conversion audit checklist.

Your clients don't want the process.

They want the result.

If you're serious about delivering outcomes,
You need to treat your service like a business.

It's one thing to promise great work.
It's another to prove impact with real data.

Most ad accounts track conversions.
But they still miss the real wins.

I audit ad accounts weekly.
And 90% make the same tracking mistakes.

1. Events fired but not deduped.
2. Conversions counted on multiple tags.
3. View-throughs treated as first-touch.

The result? Inflated ROAS.
Misleading bids and wasted budget.

Yet most managers blame creatives.
When the tracking math is broken.

Fix the tracking first.
Then optimize bids and audiences.

Do the audits. Save ad spend.
Scale with real data.

P.S. One clear tracking fix
can double your optimization signal.

First-party vs third-party data: quick guide

Know the real differences before you buy or build.

1. First-party = data you collect directly.
2. Third-party = data bought from others.
3. Sources (first): website, apps, CRM, emails.
4. Sources (third): data brokers, other sites.
5. Ownership (first): full control, owned by you.
6. Ownership (third): limited control, licensed use.
7. Use cases (first): personalization, retargeting, loyalty.
Bonus: Don’t replace consented data
→ with cheap, broad lists for scale.

Key benefit (first): higher accuracy, consent-based.
Key challenge (third): privacy concerns and costlier reach.

Use data smarter, sell smarter = This is the whole game.

P.S. Which data source are you prioritizing this year?

After auditing 100+ ecommerce store data, one thing's clear.

(It’s all about clean tracking and better insights)

Reducing CAC and boosting LTV
starts with accurate conversion data.

Small tracking errors inflate ad costs.
Bad data leads to wasted acquisition spend.

You need:

♡ Server-side tracking to capture lost events  
♡ Cookie Consent V2 for compliant data collection  
♡ First-party domain tracking to fix cross-domain gaps  
♡ Heatmaps to spot UX drop-off points  
♡ Custom GA4 setup for precise event modeling  
♡ Looker Studio dashboard for unified reporting

With these in place, we could trust our metrics.
That lets us optimize bids and creatives confidently.

The result: lower CAC per conversion.
And higher LTV through better retention moves.

If your analytics feel fuzzy,
Start with tracking, then build reports.

PS. Want a checklist to apply this to your store?

Enhanced conversions are often the fastest accuracy boost
you can add to Google Ads.

But it's not the only change that matters.

Step 1: A user converts on your site.
Step 2: The site collects email or phone.
Step 3: Data is hashed on the device.
Step 4: Hashed data uploads securely to Google.
Google then matches hashes to logged-in users.
That improves attribution and bidding.

Most advertisers stumble because they:

• Send unhashed data or skip hashing
• Use insecure upload methods
• Expect perfect matches without testing

I used to rely only on cookies for tracking.
Conversions felt incomplete and noisy.
Switching to enhanced conversions improved match rates.

Key benefits are clear:
Increased accuracy, better bid optimization,
and more powerful audience targeting.

CAC vs LTV decides if your business survives.

But most founders ignore one side.

CAC is what you spend to win a customer.
LTV is what the customer pays over time.

If CAC > LTV, you're losing money.
If LTV >> CAC, you scale with confidence.

You trade short-term growth for long-term profit.
A low CAC fuels fast growth.
A high LTV buys you resilience.

Healthy benchmark: LTV should be ≥ 3x CAC.
Also measure CAC payback period.
Faster payback = safer scaling.

How to lower CAC:
Keep messaging tight.
Target high-intent channels.
Improve conversion rates.

How to raise LTV:
Increase retention with value.
Introduce upsells and bundles.
Price strategically per value delivered.

Track cohorts, not averages.
Cohort analysis reveals real trends.
Optimize where value leaks out.

I run experiments monthly on these metrics.
Some lower CAC, some boost LTV.
Most improve unit economics over time.

If you want my LTV/CAC Calculator, Comment ''LTV".
It's not for everyone.

But maybe for you.

Most businesses ignore CAC vs LTV.
That mistake costs growth and margin.

CAC tells you what you pay to win a customer.
LTV shows what that customer is worth over time.

Together they answer one simple question:
Are your ads actually profitable or burning cash?

Comment "LTV" to get my CAC vs LTV calculator in your inbox.
It plots your numbers and shows profitability with a graph.

→ Break down how you actually do things
→ Call out the common mistakes you’ve seen in your field
→ Share the hard-won lessons only experience can teach

Start using real metrics to guide your ad spend.
Stop guessing and start scaling with confidence.

PS. What’s one customer metric your buyers can’t just Google?

If you run paid ads and want to scale,
This will save you thousands monthly.

It recovered £18,700 in lost tracked revenue
for us last month alone.

I fix broken tracking for agencies and brands.
The biggest issue I see is simple:

Most teams scale ads on data they can’t trust.

❌Events missing.
❌Revenue misattributed.
❌Dashboards lying.
❌CAC inflated.
❌LTV invisible.

That creates a huge waste of spending.
Teams make decisions in the dark.

So I built a complete step-by-step system.
It turns chaotic data into reliable tracking.

And you can get it 100% free.

Inside, you’ll receive:

- The full DataFlow Mapping Guide (events → revenue)
- Conversion Verification Checklist across Meta, Google, TikTok & GA4
- CAC vs LTV Diagnostic Framework
- GTM + Pixel QA Workflow
- Attribution Conflict Fixing Guide
- Cookie Consent V2 Guide
- Shopify Data Layer
- CRO Checklist for Ecom ( with example)
- And more documented processes we use daily

We use this system across millions in ad spend.
It helps teams scale confidently without guessing.

A client said I could charge £800+ for this.
Today, you can have it free.

To get access:

1. Connect with me
2. Like this post
3. Comment “$$$” below

I’ll send the checklist straight over. No email required.

PS. This will only be active for 48 hours.

Feel free to repost if this helps someone running ads.

taplio

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