Ruslan Galba AI x Google Ads

10 May 2026 16:29

Most brands run 2-3 tests per quarter. We run 20-30 per month.

70% fail. And that's okay.

Here's why testing velocity beats testing perfection - and the system behind it:

Most teams treat testing like a big event. Weeks of planning. Careful creative. One perfect launch.

Then they wait. Analyze. Debrief. Repeat next quarter.

3 tests in 90 days. Maybe 4 if they're ambitious.

Here's the problem with that approach.

The market doesn't wait for that analysis cycle.

Competitors aren't perfecting - they're shipping. And every week spent polishing, they're learning something new.

We flipped this completely.

20-30 tests per month. New ad angles. New landing pages. New audiences. New offers. Most of them are rough. Some of them fail.

70% fail, actually. And that's the point.

Because the 30% that survive? They become the new baseline. Next month's tests don't start from zero - they start from a higher floor.

We do that for 6 months and something happens that's hard to believe until you see it.

The worst-performing test in month 6 outperforms the best test from month 1. The compounding effect is brutal for anyone not running at this pace.

Here's how the system works:

Week 1: Launch 4-5 new tests across channels.
Week 2: Kill the losers (no emotion, just data). Scale what's working.
Week 3: Launch 4-5 more. Start from last week's winners.
Week 4: Review the month. Set new baseline. Repeat.

The key is the kill-or-scale decision.

A pattern we see often: brands let mediocre tests run too long because they spent time building them. Sunk cost kills velocity.

We give every test 5-7 days. If it doesn't hit baseline metrics by then, it's dead. No debates. No "let's give it another week."

This only works if we accept something uncomfortable: most of our ideas won't work. Not because they're bad ideas - because the market decides what works, not us.

We can't out-think the market. We have to out-test it.

Ruslan Galba AI x Google Ads

10 May 2026 13:58

$200K/year vs $8M/year on YouTube ads. Same platform. Different architecture.

The $200K brands launch 3-5 videos. Run them until they die. Scramble for new creative when performance tanks. Repeat this cycle forever.

The $8M brands run a 4-layer system where each layer has a different job, a different budget, and a different ROAS target.

Here's the exact architecture:

Layer 1: Prospecting (60-70% of budget)

This is your growth engine. Broad targeting - in-market, affinity, custom audiences. Video Action Campaigns optimized for conversions. You're reaching cold audiences at scale.

Target ROAS: 1.0-1.5x. That might sound low. It's not. This layer feeds everything else. You're buying awareness that converts through branded search, retargeting, and direct traffic.

Daily spend: $1,200-1,800 at maturity. 5-8 active videos rotating. 2 new videos per week. Retire the bottom 2 each week.

Layer 2: Search Capture (20-25% of budget)

This is your profit center. Keywords only - branded searches, competitor comparisons, product-specific intent. Someone searching "best supplement for focus" sees your ad. That's not interruption. That's answering their question.

Target ROAS: 2.0-2.5x. These audiences are warm and looking. Your creative here should be more direct response - acknowledge their search intent, position your product, drive the click.

Daily spend: $400-600. 3-5 direct response videos.

Layer 3: Retargeting (10-15% of budget)

Your conversion closer. Remarketing lists only - YouTube engagers who watched 25%+, website visitors, cart abandoners, product page viewers.

Different messaging here. Don't repeat your prospecting pitch. Handle objections. Show social proof. Create urgency. These people already know you. Close them.

Target ROAS: 3.0-5.0x. Daily spend: $200-400.

Layer 4: Testing Lab (5-10% of budget)

Your innovation pipeline. New creative formats, new audience segments, new landing page variants. Run each test 7-14 days with minimum $1,000 spend.

Hit 1.8x+ ROAS? Graduate it to prospecting. Under 1.5x? Kill it. No emotion. Just data.

Daily spend: $100-200. Breakeven is fine here.

Most accounts we audit have one campaign doing everything. Prospecting, retargeting, and testing all in one. No wonder they plateau at $1K/day.

Each layer compounds the others. Prospecting fills the top. Search captures high intent. Retargeting closes warm leads. Testing feeds fresh creative into the machine.

The total system runs at ~$2,000-3,000/day at maturity. But you don't start there. You start with Layer 1 and Layer 4, validate for 3 months, then build up.

Ruslan Galba AI x Google Ads

10 May 2026 11:21

Most Google Ads copy is written by people who've never studied copywriting.

And it shows. Here's the e-commerce copywriting system that actually moves CTR and conversion rate.

First principle: benefits over features. Always.

"Leak-proof insulated water bottle" = what it IS.
"Keep your coffee hot for 12 hours on any commute" = what it DOES for you.

Your customer doesn't care about the product. They care about the outcome. Every headline and description should answer: "What changes for me?"

The anatomy of a high-converting Google Ad:

Headline 1 (30 chars): Core benefit or offer. This is your hook. "50% Off Premium Skincare" or "Ships Free Tomorrow."

Headline 2 (30 chars): Social proof or differentiator. "Trusted by 10,000+ Customers" or "5-Star Rated on Google."

Headline 3 (30 chars): Urgency or CTA. "Sale Ends Sunday" or "Shop the Collection Now."

Description 1 (90 chars): Expand on the main benefit. Address a specific pain point.

Description 2 (90 chars): Overcome an objection. Free returns, guarantee, or unique value prop.

Six psychological triggers that work in ad copy:

1. Reciprocity: Give something free to create obligation. "Free guide with every order."
2. Scarcity: Limited availability drives action. "Only 12 left in stock."
3. Authority: Expert endorsements build trust. "Recommended by 500+ dermatologists."
4. Social proof: Numbers don't lie. "Join 25,000+ happy customers."
5. Anchoring: Show the original price first. "Was $89. Now $47."
6. Framing: Emphasize the gain, not the feature. "Say goodbye to dry skin" beats "Contains hyaluronic acid."

Copy by category matters:

Fashion: Lead with aspiration and exclusivity. "New arrival" and "limited edition" drive clicks.
Electronics: Lead with compatibility and performance specs. Specific numbers win.
Health: Lead with results and ingredients. Clinical language builds trust.
Home: Lead with lifestyle imagery in the description. Help them picture it.

The test cycle: A/B test one element at a time. Run for 2-4 weeks or until you have 30-50 conversions per variation. The winning version becomes your new control. Then test the next element. Rinse and repeat.

Ruslan Galba AI x Google Ads

09 May 2026 19:07

YouTube traffic to a generic product page: 2% CVR. Same traffic to a pre-sell page: 5%.

Same offer. Same audience. Same budget.

The only thing that changed was the page they landed on.

Most brands send YouTube traffic to the same PDP they use for Meta. It's easy. It's already built. And it "should" work.

But here's what nobody talks about.

Meta traffic is an impulse click. Someone's scrolling, sees your ad, taps without thinking. They arrive cold. A product page is fine - they're in browsing mode anyway.

YouTube traffic is a considered click. They watched your ad for 15-90 seconds and made a deliberate decision to click. They arrive expecting the landing experience to continue the conversation your ad started.

Sending them to a cold product page is like giving a keynote speech and then handing the audience a brochure. The messaging disconnect destroys trust.

That's the mechanism. And the fix maps to price point.

We test 4 funnel types for every YouTube client. The right one depends on price point:

Under $50 - Direct to PDP. Product is self-explanatory, price is low friction. 6-10% CVR.

$50-150 - Pre-sell page first. Extend the education from the ad. Address objections before showing price. 5-7% CVR.

Competitive category - Advertorial. Editorial-style content that positions your product against alternatives. 5-9% CVR.

$150+ or complex - VSL. 5-15 minute video that continues the sales conversation. 4-8% CVR.

The right funnel usually surprises people. We had a client convinced they needed a VSL for a $65 product. Pre-sell page outperformed it by 2.3x.

Ruslan Galba AI x Google Ads

09 May 2026 15:57

Two founders personally executing every account is objectively a terrible way to scale an agency. Our accountant tells us regularly. Every advisor says: "Hire, delegate, stop being the bottleneck."

From a scaling perspective, they're right. From a results perspective, completely wrong.

When both people building your campaigns have nine years across 300+ brands, things move at a speed layered organizations can't match. Live in 3 days. Hundreds of creatives per week. ROAS from 1.6 to 3.8 because the person who sees the data acts on it within the hour.

No meeting to discuss the meeting about the strategy review. No Slack thread where the buyer asks the strategist what the client meant. Two people who've done this thousands of times, in your account, making decisions in real time.

Can't take 100 clients. Don't want to. Rather run a focused roster extremely well.

https://tegra.co/work-with-us

Ruslan Galba AI x Google Ads

09 May 2026 13:56

1 product page. 12 unique pre-sale pages. Zero manual design.

That's what we're getting per PDP right now.

Each page has a different editorial angle, AI-generated images that match the page context, and hyperlinks placed where they make editorial sense. Not template swaps - genuinely different pages.

Here's what most people miss when they try this.

They give AI one prompt and get 12 pages that look identical. Same structure, same tone, same flow. The output is technically correct but practically useless.

The fix was simple (in hindsight). Before any batch generation, we built a style library - different page archetypes the AI draws from. Listicle, comparison, problem-solution, editorial review, clinical study format. Each archetype has its own structure, tone, and visual approach.

But here's the part that surprised us.

The AI doesn't just write copy. It generates images that match each page's context. A cosmetics brand gets lifestyle visuals. A supplement brand gets ingredient breakdowns. And it's injecting hyperlinks where they feel natural - not dumped at the bottom like an afterthought.

We're deploying these to live Shopify stores across cosmetics and supplements brands. 10-15 pages at a time. Or on a third-party domain so it is perceived legitimately as an external resource.

Building one pre-sale page manually used to take about an hour. Now it's a batch process - and the pages are more diverse than anything we were producing by hand.

Ruslan Galba AI x Google Ads

09 May 2026 03:42

Brands are about to fight for rankings they can't buy.

ChatGPT. Perplexity. Claude. Gemini.

People are asking AI what to buy. If your brand doesn't show up in those answers, you don't exist to that audience.

This is the next SEO. Except there's no ads yet. Pure organic positioning.

Start now:
- Create content AI can cite
- Build authority on sites LLMs reference
- Get mentioned in reviews and comparisons AI scrapes

We're already seeing clients get traffic from AI recommendations. It's early. It's messy. It's real.

Early movers will own categories. Everyone else will pay to catch up later.

Ruslan Galba AI x Google Ads

08 May 2026 22:20

If you have 20+ campaigns targeting similar keywords, you're bidding against yourself in the same auctions.

Google doesn't give you a discount for being the same advertiser.

It's a self-inflicted tax most brands don't even know they're paying.

Ruslan Galba AI x Google Ads

07 May 2026 00:30

We generate $300k+/month targeting competitor brand names on Google.

3x+ ROAS. 100% legal.

Here's the 5-step system:

Step 1: Search

Not every competitor is worth targeting. Look for brands growing fast on social but ignoring Google.

Your threshold: 2,000+ monthly branded searches. Pull 90 days from Keyword Planner or SERP API.

Fast-growing brands heavy on Meta/TikTok but light on Google Ads = best targets.

Step 2: Target

Launch two campaigns simultaneously:

- Competitor Search: exact + phrase match on their brand keywords
- Competitor PMax: their brand names, URLs, and product terms as audience signals

Don't use their brand name in your ad copy. Google will reject it.

Step 3: Execute

Start conservative. Validate for 30 days.

Track impression share, CPA vs target, and conversion rate. Review search terms weekly - you'll find brand variations you missed.

Day 30 decision: positive ROAS = scale. Poor results = deprioritize that competitor.

Step 4: Amplify

Build a comparison landing page. "Us vs Them" format.

Feature table. Switcher testimonials. Clear CTA.

These pages convert 20-40% better than standard product pages for competitor traffic. One client picked up 300+ monthly conversions from a single comparison page.

Step 5: Layer

Scale winners 20-30% budget increase every 2 weeks. Add new competitors hitting the search threshold.

Build dedicated comparison pages for your top 3-5 targets. Each page = higher relevance = higher conversion rate.

Five steps: Search, Target, Execute, Amplify, Layer.

Some accounts have cleared six figures from this strategy alone.

Ruslan Galba AI x Google Ads

05 May 2026 00:30

We almost killed YouTube Ads. They showed 0.8x ROAS. The real number: 2.4x.

Here's what happened.

Platform reporting said YouTube was our worst channel. Not close to profitable. Every spreadsheet said turn it off.

We almost did.

Instead, we ran a blended attribution model across YouTube, Search, Shopping, and Meta. Turns out YouTube was driving conversions everywhere except its own column.

This isn't just us. Haus.io ran 190 incrementality tests across brands. YouTube drove 3.4x more incremental revenue than what Google Ads reported. And YouTube's iROAS improved by 70% in the post-test window - meaning the real impact takes weeks to show up.

(Most brands don't wait that long.)

Here's what actually happens. Someone sees your YouTube ad. They don't click. They Google your brand 3 days later. Buy through Search or direct. Last-click attribution credits Search. YouTube gets nothing.

Google's own data: YouTube ads increase branded searches by up to 420%. That traffic shows up as "organic" or "branded search" in your reports. You're crediting the wrong channels.

We've seen clients kill YouTube and watch their branded search drop 40% within 2 weeks. They turned off the channel that was warming up everything else.

The halo effect is real:
- Branded search volume: up 40-60%
- Meta retargeting performance: 2-3x better
- Email open rates: 15-25% higher
- Overall CPA: down 20-30%

YouTube ROAS in Ads Manager looks terrible. But you're not buying YouTube conversions. You're buying the awareness that makes every other channel convert better.

If you're evaluating YouTube in isolation, you're reading the wrong scorecard.

Ruslan Galba AI x Google Ads

03 May 2026 00:30

Google reps swear audience signals are "just hints." My last campaign spent 78% on complete strangers.

Here's what's actually happening inside PMax.

We pulled the audience breakdown on a campaign running $40k/month. Custom intent audiences. In-market segments. First-party data signals. The works.

78% of spend went to people matching zero signals. Not loosely matched. Not adjacent audiences. Zero overlap.

Google's official explanation: "Audience signals are suggestions that help Smart Bidding find the right users."

What the data shows: PMax uses your signals for roughly the first 2-3 weeks. Then the algorithm "learns" and decides it knows better. Your carefully crafted audiences become wallpaper.

Why this happens:

Google has inventory to sell. Display Network placements, YouTube pre-rolls, Gmail slots, Discover feeds. PMax is the vehicle that fills all of it. Your audience signals would limit where Google can spend. Ignoring them opens up the full inventory pool.

This isn't a bug. It's the business model.

What to actually do:

1. Pull your PMax audience reports monthly. Check signal match rate. If it's below 40%, your signals are decoration.

2. Use placement exclusions aggressively. Block the junk inventory PMax loves to dump budget on.

3. Layer in negative audiences. Exclude existing customers, recent purchasers, and anyone who already converted.

4. Test brand exclusions. PMax loves to take credit for brand traffic. Strip it out and see what's left.

Your audience signals aren't guiding the algorithm. You need to build the guardrails yourself.

Ruslan Galba AI x Google Ads

01 May 2026 00:30

Everything I know about running Google Ads for ecom in 10 sentences:

1. Feed quality determines Shopping ROAS more than bidding strategy. Fix the feed first.

2. 80% of budget waste happens in search terms nobody reviews weekly.

3. The account with 7 campaigns and full exclusion lists outperforms the one with 40 campaigns every time.

4. Conversion tracking that fires once on the thank-you page is not conversion tracking. It's a coin toss.

5. PMax works when you treat it as a model to train, not a campaign to manage.

6. Your agency's ROAS looks high because they're bidding on your brand. Your incremental ROAS is a different number.

7. Most ecom brands should put 70%+ of spend into Shopping before touching Search or YouTube.

8. We've built Claude Code systems that do in 8 minutes what agencies charge $3k for. The gap will only grow.

9. A/B testing landing pages with less than 500 sessions per variant is noise, not data.

10. The best Google Ads account I've ever audited had the fewest campaigns. Simplicity scales. Complexity hides waste.

Ruslan Galba AI x Google Ads

29 April 2026 13:50

We just shipped the 2026-04 refresh across every Google Ads agentic-system product.

Six products. One operating-system update. Here's what changed and why.

The headline numbers:

End-to-End Codebase: 98 → 114 commands.
Agent Codebase: 27 → 34 commands.
Search Conversion: 28 → 31.
Landing Page: 36 → 42.
Shopping & PMax: 23 → 25.
Demand Gen + YouTube: 20 → 24.
Attribution: 21 → 24.

But the count is not the story. The story is what those commands do.

1. Page Review Panel on every page deploy

`presell-review` and `offer-review` run a scanner + an expert reviewer ensemble on every deployed page. P0 findings block ship. P1 flags rework. P2 goes to backlog.

`presell-review-backfill` scores every historical page against the current rubric. So you can see your bottom 20% in a single pass.

`brand-calibrate` re-weights expert reviewers per brand based on which reviewer's scores best predict real conversion outcomes. The panel learns which reviewer is right for your audience.

2. Canvas-design is the new default

Before this release, the default on a new page build was "auto-match the closest saved theme." It felt efficient. In practice it produced look-alike pages.

The 2026-04 default is Canvas-design from-scratch. The Theme Registry (91 presell + 12 offer + 25 image themes) is still there - it's just opt-in only via `theme=<name>`.

Reused layouts only happen when you ask for them.

3. Five autogrowth loops running in parallel

Presell. Offer. Search. PMax. Demand Gen.

Every loop runs weekly. Every loop is dry-run by default. Every promotion is ledger-tracked. Every week's mutations roll back with one command.

The system finds its own winners. You decide which to keep.

4. Phase 0 objective brief on every artifact

Before generation begins on any presell, offer, Search, PMax, Demand Gen, or review artifact, a structured Phase 0 brief is required. Who is this for. What is the primary action. What is the brand voice constraint. What is the proof constraint. What is the review gate target.

Sounds bureaucratic. In practice it kills 80% of regenerate-and-rewrite loops. The brief is the contract between operator and agent.

5. Search framework lockdown

Exact-match only. Phrase and broad never default.

CPC floor and ceiling per ad group. IF-functions banned. One DKI per RSA. Headline composition standards (13-Point Creative v10.5).

`search-mine` replaces phrase/broad entirely - coverage comes from mining your own converting queries, not from match-type laxity.

6. PMax safety defaults

URL expansion always OFF on every new campaign. Live mutations dry-run by default. Explicit `--apply` required to ship.

Asset content immutable. Associations mutable. Same posture across every command that touches a live PMax campaign.

7. Attribution reads page-health

The Attribution & Measurement System now consumes page-health signals. Low-health pages get deprioritized in budget allocation. High-health pages get scaled. Page-health surfaces in `brand-pdf-report` and `brand-digest`.

Attribution stopped being just analytics infrastructure. It is also a page-quality intelligence layer.

Why we shipped this

Most "AI marketing" products age fast. The model changes. The defaults drift. The doctrine the codebase was built on stops matching how the channel actually behaves.

This refresh updates the doctrine. Every product moves to the same 2026-04 baseline. Changelog in every product directory captures what changed and why.

If you own any tier of any of these products, your update is already live. If not - check it out below.

Ruslan Galba AI x Google Ads

28 April 2026 13:11

We had a client convinced their "lifestyle" creative was the winner.

Premium production. High budget. Professional models. Perfect lighting.

A simple product flat lay beat it.

3.2x vs 1.4x ROAS.

Here's what it taught us:

You don't know what works until you test. Nobody does.

The creative director thought lifestyle was the play. The brand team agreed. Months of work went into it.

The flat lay took 5 minutes to create. White background. Product shot. Basic.

Data picked the winner. It wasn't close.

This pattern repeats constantly.

We've seen minimalist designs beat complex ones. UGC beat polished. Long copy beat short. Short beat long.

There's no universal rule except this: Test and find out.

So we built systems that launch hundreds of variations. Sometimes thousands.

Not because we're being thorough. Because that's how you find what actually works.

The mechanism is simple: More variations = more chances to find unexpected winners.

When you're testing 3-5 options, you're guessing. When you're testing 500, you're discovering.

The mindset shift matters:

From "I think this will work" to "Data shows this works."

Your opinion about what works is probably wrong.

Ours usually is too.

The difference is we test broad enough to find out.

Ruslan Galba AI x Google Ads

25 January 2026 22:14

5 Google Ads Scaling Killers

After analyzing 100+ accounts, the same 5 mistakes kill scaling every time.

Number 5 is the one most brands refuse to accept.

Mistake #1: Scaling too fast

The symptom: ROAS craters within 48 hours of a budget increase.

The cause: Algorithm can't adapt quickly enough to spend efficiently at new volume.

The fix: Never increase budget more than 20% at a time. Wait 7-14 days. Evaluate. Repeat.

Patience compounds. Impatience bankrupts.

Mistake #2: Ignoring product-level performance

Aggregate ROAS hides sins.

An account can show 4x ROAS overall while:
- 20% of products drive 5x+
- 40% drive 2-3x
- 40% lose money

The symptom: Spend increases but profit doesn't follow.

The fix: Product segmentation. Heroes get volume. Zombies get strict limits or exclusion.

Mistake #3: PMax-only strategy

Performance Max is powerful. It's also a black box.

Go all-in on PMax and you lose:
- Visibility into what's working
- Control over brand vs non-brand mix
- Ability to quickly pause specific tactics

The symptom: Can't explain why performance changed.

The fix: Hybrid architecture. Standard Shopping as control layer. PMax for scale.

Mistake #4: Neglecting feed quality

Your competitors are optimizing their feeds. Are you?

The symptom: Declining impressions even with stable bids and budgets.

The cause: Competitors with better feed quality are winning auction advantages.

The fix: Quarterly feed audits. Continuous title testing. Attribute completeness checks.

Mistake #5: Chasing efficiency over growth

This is the one brands refuse to hear.

The brand at 10x ROAS and $10k spend isn't winning.

The math:
- $10k at 10x = $100k revenue
- $80k at 4x = $320k revenue

The second scenario generates 3x more revenue and significantly more profit.

But brands obsess over the 10x number. They're "efficient." They're also leaving 80% of their market untouched.

The symptom: High efficiency metrics. Flat revenue.

The fix: Reframe success as total profit, not ROAS. Efficiency at low spend is just a smaller version of winning.

Ruslan Galba AI x Google Ads

10 May 2026 15:44

Quality Score 7+ accounts pay 30-50% less per click than accounts scoring 5 or below.

Same keywords. Same auction. Dramatically different costs.

We tracked this across 25 e-commerce accounts. Here's what actually matters - and the order to fix things.

Quality Score has three components:
1. Landing page experience
2. Expected click-through rate
3. Ad relevance

Most agencies spend months tweaking ad copy to improve scores. That's the wrong starting point.

Fix the landing page first.

Landing page optimization (fastest impact):

- Add 500+ words of relevant, useful content. Thin pages score "below average" almost every time.
- Page speed under 2 seconds. We took one client from 4.2s to 1.8s load time. Quality Score went from 4 to 7 in 6 weeks. CPC dropped from $3.80 to $2.10.
- Mobile-first design. Not "responsive." Mobile-first.
- Reviews and trust signals above the fold. Not at the bottom where nobody scrolls.
- Transparent pricing. No hidden fees.

We've seen accounts go from Quality Score 4 to 7 just by fixing the landing page. No ad changes at all.

Ad relevance (second priority):

- Build tight ad groups with 10-15 keywords maximum. Not 50 keywords in one group.
- Ad copy must match search intent, not just include the keyword.
- Use every available extension - sitelinks, callouts, structured snippets, promotions.
- Google emphasizes extensions heavily in their scoring. Most accounts barely use them.

Expected CTR (third priority):

- Negative keyword hygiene every 72 hours. This is the single most neglected optimization.
- Run manual split tests alongside your RSAs. Low-budget accounts can't get enough impressions for RSA optimization to work. Monthly manual tests solve this.
- Bid adequately for competitive positioning. Being too conservative tanks CTR because you're showing in position 4 instead of position 1.

The funnel-based approach:

- Match your ad messaging and landing page to the customer journey stage.

Top of funnel: educate about the problem.
Middle of funnel: compare your solution.
Bottom of funnel: urgency + guarantee + clear CTA.

One generic ad and one generic landing page for all stages guarantees mediocre Quality Scores across the board.

Target: Quality Score 7+ on every keyword. Below that, you're overpaying for every single click.

Ruslan Galba AI x Google Ads

10 May 2026 11:21

Recent numbers, because theory is cheap.

$800K to $2.1M revenue in 90 days. CPA down 40%. ROAS from 1.6 to 3.8. Shopping CTR up 60%. MER improved 30%.

Not cherry-picked from a 2021 case study. Current results from accounts we manage right now.

They happened because both founders sit inside every account daily, deploying through the API at a volume manual work can't touch. Hundreds of creatives per week. Dozens of campaign variants. Weekly test-and-promote cycles that keep the algorithm fed instead of running the same four ads until they die.

Most agencies show you their best result from their best client from their best quarter. We'd rather show you the baseline of what happens when both founders execute personally and creative velocity runs at 12x what typical agencies produce.

If you want to see what we'd do on your accounts: free audit, no slide deck, just your account on screen and an honest conversation: https://tegra.co/work-with-us

Ruslan Galba AI x Google Ads

10 May 2026 00:12

We audited 200 Google Ads accounts. Average headline uniqueness across RSAs: 35%.

That means 65% of your headlines are basically duplicates.

Here's what we see over and over: brands writing "Free Shipping on All Orders" and "Get Free Shipping Today" and "Shop Now - Free Shipping" and counting that as 3 unique headlines.

It's not. Google's RSA system sees through it.

RSAs work by testing combinations. Google mixes and matches your headlines to find winning combos. But if every headline says the same thing in slightly different words, there's nothing to test. You're giving the algorithm 15 headlines that are really 5 ideas wearing different outfits.

The result: your ads underperform and you don't know why. Click-through rates plateau. Quality Scores stagnate. You blame the auction or the competition when the problem is your inputs.

Here's what we do instead.

We generate 45 unique headlines per ad group cluster. Each one built around a different psychological hook - urgency, social proof, specificity, curiosity, loss aversion, authority, contrast, and benefit stacking. Not just different words. Different angles.

"Free Shipping" becomes one urgency headline. But you also get:
- A specificity headline: "4.8 Stars Across 2,400+ Reviews"
- A curiosity headline: "The Moisturizer Dermatologists Keep Recommending"
- A loss aversion headline: "Your Current Routine Is Missing This"
- A contrast headline: "Half the Price of Department Store Brands"

Each headline gives Google something genuinely new to test.

Uniqueness jumps from 35% to 100%.

The CTR impact: +40% on seasonal campaigns. Same budget. Same landing pages. Same products. Just better inputs.

Most brands don't realize how much performance they're leaving on the table because their headlines all say the same thing.

Ruslan Galba AI x Google Ads

09 May 2026 17:34

AI isn't magic for Google Ads. But it's one of the best research assistants we've ever used.

The key is knowing what to ask for - and what to ignore.

Here's how we actually use it:

First, we prime it. Feed it the product data and tell it to wait. Most people skip this step and get generic nonsense back. Context-first execution engeneering changes everything.

Then we run a 7-phase sequence. Not for ad copy. For strategic research.

Phase 1: Competitor landscape analysis. Who else is in this space, what are their strengths and weaknesses. This surfaces positioning gaps you'd normally need a $200/hr strategist to find. We use SERP APIs and Meta API here.

Phase 2: Market gap identification. What USPs does this product have? What's underserved in the market? This gives you ad angles you wouldn't think of manually.

Phase 3: Search intent keyword list. 10 keywords for each intent stage - awareness, consideration, decision. This maps your funnel in minutes.

Phase 4: Long-tail keyword variations. These are high-intent, low-competition phrases that often convert better and cost less than head terms.

Phase 5: Transactional keyword grouping. Tight keyword groups ready for campaign structuring.

Phase 6: Seed keywords. A starter list of 20 core terms to feed into SERP API. Or leave it open for the agent to uncover.

Phase 7: Targeting recommendations. Demographics, interests, search behavior, platform recommendations.

Total time: about 20 minutes. Output: a complete campaign blueprint with keyword groups, targeting layers, and competitive positioning.

But here's what separates this from "just using ChatGPT":

You validate everything. Run every keyword through SERP API for actual volume and CPC data. AI suggests - you verify. AI generates 50 ideas in 30 seconds. You spend 60 minutes filtering them down to the 10 that actually matter.

The result? Campaign launches that used to take 2 days now take 2 hours. And the quality is better because you're starting from a broader research base + actual data points from SERP data.

AI does the groundwork. Humans do judgment.

Ruslan Galba AI x Google Ads

09 May 2026 15:57

The agency model has a scaling problem that nobody talks about.

More clients = more people = more management = less of the expertise that got you the clients in the first place.

We decided not to play that game.

Instead of hiring our way to scale, we built AI systems that compound.

System 1 was feed optimization. Saved 12 hours a week. Used that time to build System 2: ad copy generation. That saved another 8 hours. System 3: automated reporting. System 4: quality validation. Each one freed the time to build the next. By System 6, we had more capacity than a team three times our size - and every system was getting better without us touching it.

Every system we build makes the next one faster. That's the part most agencies miss. It's not about one automation. It's about compounding automation.

One system saves 10 hours/week.
Five systems save 50.
The sixth system gets built with those 50 freed hours.

The math compounds. Every system we build makes the next one faster to create. That's the real advantage - not the output of any single system, but the velocity of building the next one.

The agencies still hiring to scale are competing against this math. The window where you could scale the old way is closing. Not because people aren't valuable. Because the gap in output per person is becoming impossible to ignore.

Build the systems. Or compete against the teams who did.

Ruslan Galba AI x Google Ads

09 May 2026 11:48

YouTube CPC: $0.49.
Meta CPC: $1.72.
YouTube NVP: 75%+.
Meta NVP: ~55%.

Cost per genuinely new visitor:
YouTube: $0.65. Meta: $3.13.

Math is simple - YouTube is 5x cheaper for new customer acquisition.

Ruslan Galba AI x Google Ads

09 May 2026 00:30

We manage multiple markets for the brands we work with. Most brands fail at market #2. Here's the system.

The first thing that breaks: complexity math.

Two countries isn't twice the work. It's roughly 3x. Five countries isn't 5x. It's closer to 10x when you factor in feeds, languages, currencies, campaign structures, bidding calibration, and attribution.

At 10 countries with proper segmentation, you're managing 70-80+ campaigns. We've seen this number first-hand. And every one of those campaigns needs enough daily spend for the algorithm to learn.

Step 1: Score every market before spending a dollar.

We rate each target market across 8 factors - growth opportunity, existing brand presence, organic signals, local support, competitive landscape, budget feasibility, fulfillment readiness, and PMax readiness. Each factor weighted, scored 1-5.

4.0+ = Phase 1 launch. 3.0-3.9 = Phase 2 candidate. Below 3.0 = defer.

Step 2: Start adjacent, not ambitious.

US to Canada. UK to Australia. Germany to Austria. Same language, similar culture, minor currency adjustments.

Why this works: your existing feed, ad copy, and landing pages need minimal adaptation. You can validate demand without the full localization investment. We've never seen a 6-country simultaneous launch work well.

Step 3: Per-market campaign architecture.

Don't run one PMax campaign and let it "figure out" the markets. That's wrong for international.

Each market gets its own PMax with localized asset groups, native-language search themes, and market-specific audience signals. A US-heavy customer list used as a signal in Germany tells the algorithm to find German users similar to US buyers. The targeting it produces is irrelevant.

Step 4: Feed localization is not translation.

"Sneakers" in the US is "trainers" in the UK. Both English. Both correct. If you copy your US keyword list to a UK campaign without this adjustment, you're missing how they actually search.

Across languages it gets worse. "Running shoes" in German could be "Laufschuhe," "Jogginschuhe," or "Sportschuhe" - each with different search volumes and intent. Google Translate gives you one. Keyword Planner gives you all three.

Step 5: Budget allocation that prevents starvation.

The most common mistake: $100/day across 10 countries. That's $10/day per country. Split across branded and non-branded, it's $5/day per campaign.

At $5/day, nothing learns. Smart Bidding needs 30-50 conversions in 30 days to calibrate. If your CPA is $30, you need $900-1,500 per campaign per month to clear the learning threshold. Or at 30-50 conversions per account at the bare minimum.

Two well-funded markets outperform ten starved ones. Every time.

Step 6: Market-maturity bidding progression.

Don't import your home market tROAS into new markets. That's how you get the death spiral - too-aggressive tROAS restricts impression share, which reduces conversion volume, which starves learning data, which worsens performance.

Months 1-2: Maximize Conversions or Maximize Conversion Value. No target.
Months 3-4: Loose tROAS at 150% of break-even.
Month 5+: Market-specific tROAS tuned to local data.

The brands that win in multi-market Google Ads aren't the ones with the biggest budgets. They're the ones with the best systems.

Score markets before spending. Start adjacent. Localize feeds with native keyword research. Fund fewer markets properly instead of spreading thin. Progress bidding strategies based on actual conversion data, not imported targets.

That's the playbook we run across markets. It works at 2. It scales to 20.

Ruslan Galba AI x Google Ads

08 May 2026 00:30

"Google Ads can't do cold traffic." Brands spending $100K+/mo on Demand Gen say otherwise.

Here's what most brands miss about Google in 2026.

Your Search and Shopping campaigns have a ceiling. It's called search volume. Once you've captured everyone actively searching for your product, growth flatlines.

That's where most brands stop scaling. They assume Google maxed out and throw more budget at Meta.

But Google isn't just search anymore.

Demand Gen campaigns put your ads on YouTube Shorts, Gmail inboxes, and Google Discover feeds. Millions of users. Scrolling, watching, browsing - not searching.

(Sound familiar? That's Meta's playbook. Except CPMs are still 30-50% lower on Demand Gen because most advertisers haven't caught on yet.)

Here's why this matters more than people realize.

When someone sees your Demand Gen ad on YouTube Shorts, they don't convert immediately. But now they're in Google's ecosystem. Google knows they engaged with your brand.

Next step: they search. Your Shopping ad is waiting. Your Display ad follows them to blogs. Your Brand campaign closes it.

One Demand Gen impression feeds the entire Google funnel. You didn't just find a new customer - you started the cascade.

We've been layering Demand Gen on top of Search and Shopping for months now. The brands running this stack aren't hitting search volume walls anymore. They're building full prospecting-to-conversion engines on a single platform.

The stack looks like this:

TOF: YouTube Shorts via Demand Gen - reach millions, no search ceiling.
MOF: Search + Display retargeting on engaged users.
BOF: Brand campaign captures the sale.

Look, Meta isn't going anywhere. But if your Google strategy still starts and ends with "someone searches, you show up" - you're leaving the biggest growth channel on the table.

Google is a cold traffic platform now. Most brands just don't know it yet.

Ruslan Galba AI x Google Ads

06 May 2026 00:30

Most brands split budget evenly across campaigns. It feels fair. It's the most expensive mistake in your account.

We stopped doing this years ago. Here's why - and what we do instead.

When you give every campaign the same budget, you're making a hidden bet. You're betting that your 1.2x ROAS campaign deserves the same investment as your 3.4x campaign.

Think about that for a second.

You're taking money from a campaign that triples every dollar and handing it to one that barely breaks even. And hoping it turns around.

It won't.

We've watched this play out across dozens of accounts. Losing campaigns almost never recover into winners. They just drain budget that could be compounding somewhere else.

Here's what we use instead - the 60/30/10 framework:

60% goes to proven winners.
Campaigns doing 2.5x+ ROAS over a 30-day window. These are your compounders. The more you feed them, the more they return.

Stop being cautious with what's already working.

30% goes to promising tests.
Campaigns sitting at 1.5-2.5x ROAS. They're not winners yet, but they're showing signal.

This budget buys you the room to refine targeting, bids, and creatives until they graduate to the 60% tier.

10% goes to experiments.
New angles, new audiences, new formats. Most will fail (that's the point).

But the ones that hit feed your 30% tier. Without this, your pipeline dries up.

The math is simple. Every dollar you move from a 1x campaign to a 3.0x+ campaign generates 3x more return immediately.

Not next quarter. Today.

Look, I get why brands default to even splits. It feels responsible.

But "fair" allocation isn't strategic allocation. Your best campaigns are begging for more budget and you're rationing them.

Stop feeding losers. Start compounding winners.

Ruslan Galba AI x Google Ads

04 May 2026 00:30

Scaling Google Ads isn't about budget.

Here's the 5-module system that takes you from "this works" to "this scales."

Module 1: Competitive intelligence.

Most brands check competitors once and never look again. We run a 17-phase protocol quarterly.

The output: a messaging matrix that maps every competitor's positioning. Where they're crowded (red ocean). Where nobody is playing (blue ocean). Then we build counter-ads for the gaps.

One home goods brand found every competitor running price messaging. We went authority-only. CPCs dropped 20%. Impression share recovered from 40% to 60%.


Module 2: Offer page strategy.

Your product page is an order form. Your offer page is a sales argument.

Value stacks build perceived value before revealing the price. Core offer, implementation support, speed bonus, risk reversal, surprise bonus. Each layer addresses a different objection.

A $200 product feels expensive alone. The same $200 after showing $600 in value feels like a deal. That's not manipulation. It's clarity about what they're getting.


Module 3: Reporting system.

Five channels. Five different numbers. One source of truth.

GA4 says one thing. Google Ads says another. Shopify says a third. Nobody can tell which is right. This module builds a unified dashboard with three views: revenue reconciliation, channel performance, and conversion quality metrics.

Report generation time drops from 2-3 hours per account to 20-30 minutes. Over 50 accounts, that's 105 hours per month returned to strategy work.


Module 4: The weekly optimization protocol.

90 minutes per week. Four steps, same order, every time.

Analyze: pull dashboard, flag anything that moved 15%+.
Plan: identify cause and response for each flag.
Execute: make changes one at a time, document each one.
Opportunities: log growth ideas for the monthly deep dive.

Before this, optimization was "open the dashboard and adjust whatever catches your eye."

That costs 20-40% of potential performance.

The monthly deep dive covers what the weekly cycle doesn't.

Trends: compare month-over-month and year-over-year at account, channel, and product levels. Opportunities: review the weekly log, form testable hypotheses. Tests: run 2-3 per month with specific success criteria. Scale: fund the winners, document the learnings from the losers.

Every bid adjustment has a trigger and a wait period. No guessing. No emotional reactions.


Module 5: The scaling framework.

Three phases - foundation, structure, scale. Each has phase gates. You don't move forward until you pass.

Phase 1 gate: tracking under 3% discrepancy, feed 90%+ active, brand separation complete. Phase 2 gate: 30+ conversions per campaign, positive prospecting ROAS, 3+ campaign types live. Phase 3 gate: data density at threshold, competitive positioning mapped, optimization protocol running.

Skip a gate, pay the tax later.

A fashion brand followed this exact sequence. $20K to $300K monthly revenue in 14 months. A supplements brand went from 0.6x to 1.4x ROAS in 30 days from feed work alone. A home goods brand added $400K in annual revenue from competitive intelligence.

Different brands. Different categories. Same system. Same sequence. Same result: controlled, sustainable growth instead of budget-and-hope.

Built from $184M+ in managed spend across 160+ accounts.

Ruslan Galba AI x Google Ads

02 May 2026 00:30

We analyzed 500+ product feeds.

Most had near-identical titles across products.

Google notices. Here's what it costs you.

The problem is templates. Brands create one title structure and apply it to everything.

"[Brand] [Product Type] [Color] [Size]"

Sounds efficient. Looks terrible to algorithms.

When 80% of your product titles share the same structure, Google treats them like duplicate content. Lower quality scores. Worse ad placements. Wasted spend.

We measured it.

Average uniqueness score on feeds we audit: 35%.

That means 65% of title content is basically copy-paste.

The fix isn't manual rewriting. You can't write 5,000 unique titles by hand. Nobody has the bandwidth.

So we built systems that do it.

Each product gets individually written content. Not variations of a template - actual unique descriptions based on the specific product.

Result: 35% → 98.7% uniqueness score. That's +182% improvement.

The mechanism is simple: Google rewards unique content with better quality scores. Better quality scores mean better placements at lower costs.

When we rewrite feeds, we see CPCs drop 15-30% in the first 60 days.

Not because the products changed. Because the content Google sees changed.

Most brands don't know their feed is hurting them.

Check your title patterns. If they look the same, they probably are.

Ruslan Galba AI x Google Ads

29 April 2026 17:19

We've audited 100+ accounts running PMax-only.

Same pattern every time: they can't explain why performance changed.

What you lose going all-in on PMax:
- Visibility into what's working (keywords)
- Control over brand vs non-brand (changed recently, but still limited)
- Ability to pause specific channels (at least not directly)

Run Standard Shopping alongside it. That's your control layer.

Ruslan Galba AI x Google Ads

28 April 2026 22:53

Not all products deserve the same bid.

Higher bids on luxury items (bigger margins). Lower on budget pieces. Then layer modifiers:

- Geo-bids for high-converting locations
- Weekend boosts for high-ticket items
- Weekday boosts for budget items

Match bids to purchase behavior.

Ruslan Galba AI x Google Ads

27 April 2026 20:18

Furniture brand doing 6 figures/mo purely organic. Google Ads was an afterthought.

Now: $80K spend generating $300K+ revenue monthly.

Brands with strong organic foundations have a paid advantage they rarely use.

The brand equity is there. Paid just captures it.

Ruslan Galba AI x Google Ads

23 January 2026 04:37

Your Google Shopping products are fighting each other.

Every time you group 100+ variants in one segment, you're funding both sides of an internal war. The fix isn't complicated. But most advertisers miss it.

Here's what happens:

Google's auction runs at the product level. Not the segment level.

So when 3 variants of the same shoe enter the same auction, they're all bidding against each other. Your budget pays all three.

Same shopper. Same search. Triple the cost.

We audited 40+ Shopping accounts last quarter. The pattern was consistent: accounts with 100+ variants per segment had 30-45% higher CPCs than accounts using tighter groupings.

Not because of competition. Because of self-competition.

Most advertisers think: "More products = more coverage."

What actually happens: Smart Bidding sees 8 similar products, can't pick a winner, and spreads budget across all of them. You're not getting 8x visibility. You're getting 1/8th efficiency.

The fix isn't adding more structure. It's removing ambiguity.

When Google knows exactly which product should win a given search, it stops hedging. CPCs drop. ROAS climbs.

Here's how to do it. 3-step segmentation fix:

1. Group by price band (not just category)
2. Separate top performers into their own segments
3. Exclude duplicates at the variant level

Takes 20 mins. Saves thousands in wasted spend monthly.

Just did this for one of the clients last month. Same products. Same budget. CPC dropped 28%. ROAS up from 3.2x to 4.1x.

The algorithm didn't change. The ambiguity did.

If you're running Shopping with 100+ variants per segment - this is your lowest-hanging optimization.

If this was useful, I put together a deeper breakdown on Shopping segmentation with more examples.

Enjoy https://tools.tegra.co/40-variant-rule