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.

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

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.

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.

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.

I wrote this to help you fix cookie consent V2, tracking, and privacy gaps — fast.  
Consent Mode V2 can restore data accuracy without breaking privacy rules.

If you delay enabling it, you risk regulatory fines and missing conversions.  
Consent Mode V2 lets your tags adapt to the user’s real choices — not guesswork.

It adds ad_user_data and ad_personalization on top of earlier controls.  
That means analytics and ads stay useful while respecting consent.

Implement via GTM, a certified CMP, or a careful manual setup.  
Test every consent state so tags behave correctly in every scenario.

The biggest wins come from clear UX, persistent consent sharing, and tag hygiene.  
Audit your tags, update triggers, and verify with a consent checker.

After 6 months of testing, I just finished — The Cookie Consent V2 Playbook

Full GDPR-compliant, and you can have it for FREE.

Comment "GUIDE" and I'll send it to you

It's the exact system I used to;

→ Recover lost conversions and improve ROAS  
→ Make ad reporting compliant without sacrificing accuracy  
→ Train teams to implement consent-first tracking  
→ Ship safer analytics for enterprise and growth teams  
→ Reduce wasted ad spend from misattributed data

I share key insights I've NEVER published before like

→ The step-by-step GTM + CMP wiring that prevents common leaks  
→ How to test every consent combination (and what most people miss)  
→ The exact data mappings for ad_user_data & ad_personalization

It teaches you EXACTLY

How to implement Consent Mode V2 — without breaking your reporting.

This is the only guide you need to fix consent, keep conversions, and stay compliant.

And I promise: it'll be worth your time.

Comment "GUIDE" and I'll send it to you

♻️ Repost this for priority access.

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?

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.

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?

CAC vs LTV matters more than you think.  
Most founders ignore it until it's too late.

Knowing your CAC tells a cost story.
Knowing your LTV tells a value story.
Together they tell profit or loss.

I built a simple CAC vs LTV calculator.
It shows if your ads are profitable or not.
It also plots a clear graph for quick insight.

Use it to stop wasting ad spend.
Use it to scale with confidence.
Use it to make better growth bets.

Its FREE
♡ For my clients.  
♡ For my community.  
♡ For you.

So my questions are:

→ Do you track CAC vs LTV today?  
→ What would you change if you knew the true numbers?

PS. Want the CAC vs LTV calculator in your inbox?
Comment "LTV" and I’ll send it to you.

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?

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.

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.

I've audited 150+ Google Ads Conversion Tracking.  
(Here's the exact checklist I use every time.)

After years of trial and error, I built the ultimate Google Ads Conversion Tracking Checklist — and I'm giving it away for FREE 🎁

Here's what you'll get:
✅ Comprehensive conversion tracking checklist (all critical checks I run)  
✅ GTM + server-side (GTM SS) setup guide & common pitfalls  
✅ Attribution & conversion window matrix (what to choose, when)  
✅ Offline & CRM import templates + mapping tips  
✅ Phone call, form, email click, and micro-conversion tracking setups  
✅ Conversion value & currency validation steps (dedupe & accuracy)  
✅ Cross-domain, URL parameter, and redirect fixes  
✅ QA playbook with testing scripts (Tag Assistant, GA4 DebugView, real-event tests)  
✅ Quick fixes that stop wasted spend immediately  
✅ Tools list and troubleshooting flow for fast audits

Why it matters:
– Accurate conversions = smarter bidding and better automated bids  
– Clear ROAS visibility (stop cutting winners)  
– Faster, confident decisions from trustworthy data

Want it?
1) Connect with me  
2) Like this post and comment "TRACK" below

I’ll send the checklist straight over (no email signup required). 

♻️If this helps, please repost to help someone wasting ad spend.

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