🔍💻Google ads & Meta ads specialist | Performance Marketing | Founder of KLIQBLE

With over 8 years in the ever-evolving world of PPC advertising, I've honed my skills to become a results-driven PPC specialist. My passion lies in crafting data-backed strategies that maximize ROI for my clients. Whether it's Google Ads, Facebook Ads, or Bing ads, I thrive on optimizing campaigns and exceeding expectations. Let's connect and discuss how I can help you achieve your digital marketing goals.

What if you could test your entire Google Ads strategy — not just one campaign?

Google just rolled out a new multi-campaign experiment format (Beta) — and it’s a big step forward for account-level decision making.

For the first time, you can test different campaign mixes inside one experiment, including:
•⁠ ⁠Search
•⁠ ⁠Performance Max
•⁠ ⁠Shopping
•⁠ ⁠Demand Gen
•⁠ ⁠Video & App campaigns

Instead of asking “Which campaign performs better?”
You can finally ask:
“Which structure and budget split actually drives better results?”

Why this matters 👇
Most accounts are built on assumptions:
– how much budget Search vs P-Max deserves
– whether Demand Gen supports or cannibalizes Search
– how different campaign types influence each other

This experiment format lets you:
•⁠ ⁠compare up to 5 different strategic setups
•⁠ ⁠test budget distribution across campaign types
•⁠ ⁠see incremental impact, not just isolated performance
•⁠ ⁠make structural decisions based on data — not opinions

It’s still in Beta, but this clearly shows where Google Ads is heading:
fewer guesses, more system-level testing.

If you manage complex accounts, this is one of the most meaningful updates in a while.

If you’re running Google Ads and want to test strategy — not just tactics — this is worth paying attention to.

Violeta Balbae

🔍💻B2B & B2C Leadgen specialist | Google ads & Meta ads | Performance Marketing | Founder of KLIQBLE

Mtawali Fredrick

Performance Max has always shown what works.
Now it finally lets us test why.

Google just launched A/B testing for assets in Performance Max (Beta) — and this is a meaningful shift.

For the first time, you can:
•⁠ ⁠test two different creative sets (A vs B) inside the same asset group
•⁠ ⁠keep shared assets running in parallel
•⁠ ⁠compare performance in a more controlled way — not pure algorithmic guesswork

Until now, P-Max creatives were a black box.
You uploaded assets — and hoped the algorithm figured it out.

This update brings intentional creative testing into a system that desperately needed it.

Here’s when this new P-Max asset experiment actually makes sense 👇

✅ Use it when:
•⁠ ⁠your campaign has stable volume
•⁠ ⁠the learning phase is complete
•⁠ ⁠tracking is clean (leads ≠ revenue, but at least qualified)
•⁠ ⁠you’re testing messaging angles, not random creatives

❌ Don’t use it when:
•⁠ ⁠the campaign is still learning
•⁠ ⁠volume is too low to reach significance
•⁠ ⁠lead quality isn’t measured at all
•⁠ ⁠you expect answers in a week

This isn’t a shortcut.
It’s a controlled experiment — and it demands patience.

But for the right accounts, it finally brings creative accountability into Performance Max.

And that’s a big deal.

If you’re running Performance Max and want to make sure your creative tests actually improve lead quality and business outcomes — feel free to reach out or DM me.
Happy to help you decide whether this experiment is a growth lever or a waste of budget.

B2B ads don’t just fail because of bad creatives alone.
They also fail because platforms optimise for the wrong thing.

Running B2B campaigns in Google Ads and Meta is a completely different game from B2C.

•⁠ ⁠longer decision cycles
•⁠ ⁠higher CPL
•⁠ ⁠smaller audiences
•⁠ ⁠and leads that look good in dashboards — but never close

If you work with B2B, you’ve probably seen this:
👉 “Leads are coming in, but sales say they’re trash.”
👉 “We can’t see which ads actually bring revenue.”
👉 “The audience is too small, CPC keeps growing.”

The core problem isn’t traffic.
It’s that platforms are trained on form fills, not real buyers.

What actually changes the game in B2B:
•⁠ ⁠optimising toward qualified leads, not volume
•⁠ ⁠feeding CRM data back into ad platforms
•⁠ ⁠filtering leads before they hit sales
•⁠ ⁠building trust first, not pushing demos instantly

When ads are connected to real business data, algorithms finally learn who is worth paying for.

That’s when B2B advertising becomes predictable — not stressful.

If you’re a B2B founder or business owner struggling with lead quality, attribution, or scaling paid ads — let’s talk.

I help businesses turn ad platforms into revenue systems, not lead factories.

Most ads fail for one simple reason.
They announce instead of influencing.

Too many experts still use generic headlines and creatives that bluntly say:
“Who we are. What we do.”

But they ignore what actually drives decisions:
pain, instinct, and human psychology.

We don’t.

We apply user psychology across all advertising platforms and here’s one B2B case:

• Facebook CPL before: 600–800 SEK
• Facebook CPL after: 150–250 SEK

Result?
👉 The channel became scalable.

We also ran RSA A/B tests in Google Ads:
✔ Lower cost per lead
✔ Higher conversion rate
✔ Better lead quality

No hacks.
No magic buttons.
Just understanding why people act and building ads around it.

That’s where performance actually comes from.

If you want better results from your ads message me.
We’ll help you fix what’s actually holding performance back.

taplio

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