Scaling eCommerce Brands Through Profitable Acquisition

Driving profitable, scalable growth for 8 and 9-figure eCommerce brands through integrated performance marketing, creative, and data systems.

AFR Fast Starter 2024 | Finalist Melbourne Young Entrepreneur | Startup of The Year | Emerging Agency of The Year

At Blue Sense Digital, we work with 8 and 9- figure eCommerce brands to align growth strategy with financial outcomes and operational leverage.

We work with businesses where marketing, finance, and operations need to be aligned for profitable scale.

Our clients typically operate in:
· Fashion & Lifestyle
· Consumer Goods
· Home & Living

We believe profitable scale depends on three critical levers:

→ Creative Velocity & Diversity
→ Technical Account Structure
→ Data Integrity

By integrating these pillars, we help brands build a scalable acquisition model, tied directly to financial outcomes.

For earlier-stage brands, we share proven frameworks and strategies through educational content and resources:

→ https://www.youtube.com/@bluesensedigital

If you’re an eCommerce leader ready to build your next chapter of growth, let’s connect: 
https://calendly.com/bluesensedigital/exploratory

I promise to honour your time.

Nathan Perdriau

We are looking for A-players who are seeking rapid skill development (outside of the ad account).

You will:
→ Be implementing across 8-9 figure retailers 
→ Step into a clear roadmap towards leadership from day one 
→ Learn from our lead strategist, myself & team leaders to accelerate your development

You will learn:
→ Financial analysis & forecasting 
→ DTC & omni-channel growth strategy
→ Data science for causal experimentation
→ Creative strategy & planning

If you have 3+ years of experience in performance marketing, then I'd love to talk. 

It DOES NOT matter where you're based globally. We hire from everywhere.

Please do NOT message me if:
→ You don't have incredibly high standards for yourself
→ You don't take accountability for everything around you
→ You aren't a perpetual student

If you think you're a fit, send me a message.

And if you know a sharp, experienced marketer who's outgrown their current role - please pass this along.

Thanks.

Producing Better Financial Outcomes for 8-9 Figure Brands

Ads in 2026 are unforgiving.

Miss one of these and you’re playing on hard mode.

Here’s what actually matters right now.

1. Your base Meta structure
   Manual campaigns are basically gone.
   Everything is Advantage+ now, with manual control layered in.

The simplest version that works:

- One testing campaign with low budgets
- Winners get moved via post ID into a scaling campaign

Not perfect for every account.
But a solid default.

2. AOV and LTV are non-negotiable
   Trying to make Meta work on a $30–$40 product?
   In the US?
   Good luck.

Reality:

- Sub $75 AOV struggles
- Sub $100 AOV is brutal unless cohorts carry it

CPMs don’t care about your margins.

3. Partnership ads still matter
   Not a “hack” anymore.
   Too saturated for that.

But still one of the few levers that moves results.
Especially founder-led and creator-led pages.

And yes, they behave differently to standard ads.
The page data matters.

4. Creative investment wins accounts
   This is the big one.

If you’re spending $300k/month on Meta and:
→ $10–20k on creative

You’re underinvesting.

If your competitor spends:
→ $300k on media
→ $60k on content

They will beat you.
Even with worse buying.

In 2025, creative is the strategy.
Media buying just distributes it.

Budget accordingly.

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Watch our guide here: https://www.youtube.com/watch?v=DnpYApVNu-Y

teagan youtube

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Emmanuel Daniel

Every time you launch an ad set, all the spend goes into just one ad.

How do we fix this?

How do we get some spend into ads 2 and 3?

We spent all this money and time making these creatives. We want to know if they worked.

Four options:

Option 1: Relaunch without ad 1

Force spend through the other 3 ads.

Is that a good idea?

It'll let you test these ads, but there's probably a reason they weren't getting spend.

They're not going to perform as well as ad 1.

If you're maximizing efficiency, not a great move.

Option 2: Do nothing

Just leave these 4 ads running.

2 won't get any spend. Oh well.

Meta is really good at distributing spend, so we trust it.

Option 3: Set up creative testing

This is a new feature Meta rolled out about 2 months ago.

You select the ads you want even spend distribution through and it will distribute spend accordingly.

Option 4: Make new creatives

Make more creatives like the one getting all the spend.

What's the best option?

Best: Make new creatives.

Make more creatives like the one getting all the spend.

Second best: Do nothing.

There's a reason these ads aren't getting spend. They probably won't perform well.

Is that true all the time? No. 5% of the time, Meta might be wrong and you need spend through them.

But 95% of the time, you'll be wrong trying to distribute spend here.

Third best: Set up the creative testing feature.

Worst: Duplicate and turn off the high-performing ad.

I've never really seen that work as a strategy.

The lesson:

Stop trying to force spend into underperforming ads.

Either trust Meta's distribution or make more winners.

Don't fight the algorithm just to test ads that probably won't work.

Make more of what's already working.

That's how you scale.

If you’re running Advantage+ and judging ads by ROAS alone…

You’re probably scaling retargeting.

In a post-segmented world, we don’t structure accounts as:

* Top of funnel
* Middle of funnel
* Bottom of funnel

Everything sits inside one broad campaign.

So you’ve lost visibility.

Here’s the metric that replaces funnel labels:

Frequency.

How to use it properly:

Step 1
Look at the last 7 days only.
Not 30 days. Not lifetime.

Step 2
Add frequency at ad level.

Step 3
Interpret exposure depth:

* 1–2 = mostly cold
* 3–4 = warming
* 5+ in 3–4 days = heavy retargeting

Real example from a large spend account:

Ad 1
Frequency: 7 (in 3 days)
Purchases: high
ROAS: strong

The team’s instinct?
“Scale it. Make more like this.”

Wrong.

It’s winning because it’s serving warm / bottom-of-funnel users repeatedly.

Now compare:

Ad 2
Frequency: 1.8
Purchases: lower
ROAS: weaker

Most teams pause this.

But this is likely the best true cold-acquisition creative in the account.

And cold performance is what determines the ceiling.

If you ignore frequency, you’ll:

* Overproduce retargeting-style creative
* Underinvest in prospecting angles
* Hit a growth plateau you can’t explain

Here’s the system I recommend for $5m–$500m brands:

1. Categorise ads weekly by frequency band.
2. Judge performance inside its exposure bucket.
3. Scale cold winners separately from warm converters.
4. Build creativity intentionally for each depth of awareness.

Frequency isn’t just a delivery metric.

It’s your funnel diagnostic in consolidated accounts.

Most brands optimise for visible ROAS.

Operators optimise for where the conversion actually originated.

What do I actually do day-to-day at the agency?

The highest leverage opportunity within an agency is people.

An agency is fundamentally a recruitment and training business in that order.

Then sales and marketing.

Here's exactly how my time is split:

40% training

All the ancillary activities associated with developing the team.

30-40% client delivery

Strategy, quarterly planning, account oversight.

20% sales-related activities

Audits, content, ads.

(I batch everything so it's very time-efficient.)

My co-founder spends 40% of his time on recruiting alone.

The key takeaway for agency owners:

If your time is disproportionately spent on sales and marketing, you're probably not running a very good agency.

You shouldn't be demand-constrained.

You should have infinite demand because you should actually be good.

The real constraint on a good agency is hiring and talent.

That's where a lot of your time should go.

And here's the key for eComm brands vetting agencies:

Ask the founders directly:

"How much of your time is spent on recruiting, hiring, and training?"

Because that is what makes an agency good.

If time isn't being spent there by the founders, it's probably being spent there by no one.

And therefore, it's just a sales and marketing machine focused on acquiring clients.

Not on actually serving them well.

What to ask when vetting an agency:

1. How do you recruit your team?
2. What does your training process look like?
3. How much time do the founders spend on training vs sales?
4. What's your team retention rate?

The best agencies are talent obsessed.

Not lead generation obsessed.

If they can't answer those questions clearly, keep looking.

This Meta setting could be hurting your performance.

It’s called "maximise number of conversions.”
It’s the default. You’re probably using it.

The problem is simple: it optimises for the lowest CPA, not profit.

That sounds good at first. Cheaper purchases should mean more sales, right?
Not if you sell multiple products.

Example:

Product 1: Hoodie ($90)
Product 2: T-shirt ($50)

Costs:
Hoodie delivery: $21
T-shirt delivery: $13

CPA:
Hoodie: $45
T-shirt: $25

Both products are at 2x ROAS.
T-shirt: spend $25 to make $50
Hoodie: spend $45 to make $90

Same ROAS.

But Meta will push spend to the t-shirt because it has the lower CPA.
Meta doesn’t care about ROAS or profit. It cares about CPA.

Now look at profit contribution:

T-shirt: $50 - $13 - $25 = $12
Hoodie: $90 - $21 - $45 = $24

The hoodie is twice as profitable.

But if you’re using “maximise number of conversions,” Meta will still send most of the budget to the t-shirt.

This shows up a lot in fashion accounts.
A low-priced item gets added to the campaign and suddenly eats all the spend.

Why?
Lower price = lower CPA = algorithm favourite.

The fix:

If you want higher AOV, better profit contribution, and smarter budget allocation across products, switch to “maximise conversion value.”

That tells Meta to prioritise value, not just cheap purchases.

Stop optimising for the cheapest conversion.
Start optimising for the most profitable one.

If you still think Meta targeting = interests & look-a-likes…

You’re 5 years behind.

Here’s how targeting actually works in 2026.

Back in 2019:

* Someone engaged with pet content
* Meta slapped a “pets” label on them
* You targeted the “pets” interest
* Ads were shown to everyone with that tag

Simple.

But, people being “interested in pets” tells you almost nothing about:

* Income
* Location
* Buying behaviour
* Psychographics
* Brand affinity

So Meta changed the algorithm.

They moved from label-based targeting
→ to vector-based targeting.

Here’s what that means in plain English:

Instead of tagging someone as “pets”…

Meta positions every user inside a multi-dimensional space.

Every:

* Post
* Reel
* Story
* Click
* Purchase

Pushes that user in a certain direction.

Over time, people naturally cluster.

Example:

* High-income mums who like pets
* Urban males into outdoors + premium brands
* Fitness-focused students in major cities

They don’t share one label.

They share a pattern.

Now here’s the important part for brands:

When you run broad…

Meta doesn’t “ignore targeting”.

It tests your ad across the space.

Then it identifies which cluster responds.

Then it doubles down on users who sit near that cluster.

Your creative becomes the targeting filter.

That’s why:

* Weak creative fails in broad
* Strong creative scales aggressively

And why layering 20 interests does almost nothing now.

Practical takeaway:

If you’re running ASC or broad campaigns:

1. Stop obsessing over interest stacks.
2. Focus on clear ICP-aligned messaging.
3. Analyse who is actually buying (geo, AOV, device, age bands).
4. Feed that insight back into creative angles.

Your job isn’t to find the audience.

It’s to create ads that attract the right cluster.

Back with ABOs and CBOs, here's how they worked:

They'd serve to 1,000 random people.

Once you accumulated 50 conversions, Meta knew who to target.

Then it would exit the learning phase.

But Meta realized this is a huge waste of spend.

So instead, with Advantage+ campaigns, it actually skips the learning phase by using historical conversion data within the account to go after the highest-intent people possible.

That's why when you launch Advantage+ campaigns, performance starts really good and then dips off.

It capitalizes on all the high-intent audiences from the historical conversion data and targets those people first.

Whereas with ABOs, you'd typically see better performance over time because they'd start collecting conversion data and then learning.

The Advantage+ lifecycle:

Day 1-3: Great performance (hitting historical high-intent audiences)
Day 4-7: Performance dips (exhausted high-intent audiences)
Day 8+: Performance stabilizes (finding new audiences)

This is normal.

Don't panic and turn off campaigns when performance dips after the first few days.

It's just moving from high-intent to cold audiences.

The fix:

1. Expect the dip
2. Give campaigns 7-14 days to stabilize
3. Feed them with more creative to expand reach
4. Scale once stabilized, not during the high-intent phase

The lesson:

Advantage+ skips learning phase by using your account's historical data.

This is good (faster to profitability).

But it also means you'll see a performance dip as it exhausts high-intent audiences.

This is expected behavior.

Don't kill campaigns too early.

Let them stabilize.

The Meta “learning phase” won’t save your bad ads.

If you’re at 1x ROAS today…

You’re not magically hitting 3x because the status changes to “Active”.

This belief is burning cash in $5M–$500M Ecom accounts.

Here’s what founders get wrong:

They launch a new ad set.

Days 1–5:

* ROAS = 1.0
* Break-even = 3.0

Response?

“Let’s just wait until it exits learning.”

That’s not how it works.

What the learning phase actually does:

→ Reduces volatility
→ Improves delivery stability
→ Tightens audience matching

It does NOT:

→ Double your ROAS
→ Fix weak creative
→ Save bad economics

Before learning completes, results look like this:

Up.
Down.
Spike.
Drop.

After learning completes:

More consistency.

Less variance.

That’s it.

In most accounts, performance lift post-learning is:

* 0–10% typical
* 10–20% good
* 30% rare

If you need a 200% improvement to be profitable…

You don’t have a learning phase problem.

You have:

1. A creative problem
2. An offer problem
3. A margin problem
4. Or a targeting strategy issue

Here’s how to approach it properly:

1. Judge direction, not perfection, in first 72 hours
2. Compare CPA to your allowable, not just ROAS
3. If you’re 60–70% of target, it may stabilise in the range
4. If you’re 30% of target, kill it

Stop waiting for the algorithm to rescue you.

Meta optimises.

It doesn’t perform miracles.

If the foundations aren’t there…

The learning phase just stabilises losing money faster.

Most brands guess how many ads to launch each month.

That’s why creative output never matches revenue goals.

Here’s the equation you actually need:

Winning Ad Value × Hit Rate = Expected Value per Ad Launched

Break it down.

1. Winning Ad Value
   How much revenue does one true “winner” generate per month?

Not a decent ad.

A real scaler.

2. Hit Rate
   Out of every 10 ads you launch…
   How many become winners?

If 1 in 10 wins, your hit rate is 10%.

Example:

* A winning ad drives $100k/month.
* Your hit rate is 10%.

That means:

Each ad you launch is worth $10k in expected monthly revenue.

Now reverse it.

If you want to add $300k/month in new customer revenue…

You need to launch 30 ads.

Not 5.
Not “as many as we can manage.”

30.

This is how you remove emotion from creative strategy.

Here’s how to apply it this week:

1. Pull 6–12 months of Meta data.
2. Define what qualifies as a “winner”.
   (Spend threshold + CPA target.)
3. Calculate your real hit rate.
4. Work out average monthly revenue per winner.
5. Set a revenue target.
6. Reverse engineer required ad volume.

Then do one more thing most brands skip:

Back-propagate into diversity.

If you need 30 ads per month and your hit rate improves when you test:

* 3 hooks
* 3 angles
* 2 formats
* 2 audiences

You can plan that in advance.

Creative stops being random.

It becomes maths.

$5M brands ask:
“What should we test next?”

$50M brands ask:
“How many tests do we need to hit the number?”

Volume isn’t chaos.

It’s controlled probability.

Know your numbers.

Then scale your output to match your ambition.

If one ad is getting all the spend…

Stop trying to “fix” it.

Every eComm team has done this:

You launch 4 ads in one ad set.

Ad 1 gets 90% of the spend.

Ad 2, 3 and 4 get scraps.

And someone says:

“How do we force spend into the others?”

Here are your 4 options — ranked.

1. Make more creatives like the winner.
   Best option.

If Meta is pushing spend into one ad, it’s because it’s predicting higher return.

Instead of fighting it:

* Break down the winning ad.
* Identify the hook.
* Identify the angle.
* Identify the format.
* Recreate variations.

Scale what’s working.

2. Do nothing.
   Second best.

Meta’s distribution isn’t random.

95% of the time, if an ad isn’t getting spend, it’s because it won’t perform.

Trust the signal.

Optimizing for ego (“we spent time on that creative”) kills efficiency.

3. Use Creative Testing.
   Third.

Meta rolled this out recently.

You can select specific ads and enforce more even spend distribution.

Useful if:

* You’re validating messaging.
* You’re early in account learning.
* You need directional data.

But understand:

You are overriding the algorithm.

4. Duplicate the ad set and remove the winner.
   Worst option.

Yes, you’ll force spend through the others.

No, it won’t magically make them good.

If they were better, Meta would already be spending on them.

Here’s the mindset shift:

You’re not in the “fair distribution” business.

You’re in the profit business.

For brands doing $5M–$500M per year:

Creative volume > creative control.

When one ad wins:

* Don’t redistribute spend.
* Don’t overanalyze the losers.
* Double down on the signal.

Most scale comes from pressing advantages.

Not rescuing underperformers.

Every time you launch an ad set, all the spend goes into one ad.

But you spent all this time making these other ads.

How do we get spend into the others?

Four options:

1. Relaunch the ad set

Duplicate it and hope the next time spend goes into the other ads.

2. Do nothing

Just let it distribute how it's distributing.

Meta is probably better than you at knowing where to put spend.

3. Use the creative testing feature in Meta

Select your ads and set up a creative test that will force spend through the other ads.

4. Make new creative

What should you actually do?

Best option: Make new creative.

There's a reason Meta is distributing spend like this.

It's probably because this is the best-performing ad at this level of spend.

Second best: Do nothing.

Let Meta optimize.

Third option: Use the creative testing feature.

Last option (don't do this): Relaunch and duplicate.

It's going to do absolutely nothing.

It's a waste of time.

Why this matters:

Meta's algorithm is very good at finding the best-performing ads.

If all the spend is going to one ad, it's because that ad is winning.

Trying to force spend into underperforming ads won't help.

What to do instead:

1. Accept that one ad is winning
2. Analyze why (hook? messaging? format? avatar?)
3. Make new creative inspired by what's working
4. Launch new ads and let Meta test them

If the new ads are better, Meta will shift spend to them.

If they're not, Meta will keep spending on the winner.

Stop fighting the algorithm.

Work with it.

Make better creative.

That's the only lever that actually moves the needle.

Should you run interest or broad targeting in 2026?

It comes down to how machine learning inside Meta Ads actually works.

How Meta used to work:

They’d collect data and label you.

Engage with pet content?
You get tagged with the “pets” interest.

Simple. Keyword-based buckets.

That’s largely outdated.

What it moved to: vector-based targeting.

Sounds complex. It’s not.

Instead of assigning a word like “pets,” Meta assigns you a position in multi-dimensional space.

Think of it like coordinates.

If you engage with pet content, you get pushed slightly in that direction.
Engage again? You move further.

But this isn’t 3D space.

It’s hundreds or thousands of dimensions.

Every interaction pushes you slightly in different directions based on:

* Content type
* Frequency
* Recency
* Purchase behavior
* Demographics
* Device usage
* And countless other signals

Over time, people naturally cluster.

Groups form:

* Parents cluster together
* Young males cluster together
* High-income hobbyists cluster together

Everyone sits in a unique position, but similar people sit near each other.

So what’s the difference?

Interest targeting relies on old-school labels.
“Target people interested in pets.”

Broad targeting leverages this entire vector space.

When you launch broad, Meta shows ads across many clusters.
Then it quickly narrows into the pockets where purchases actually happen.

It’s not just asking:

“Do they like pets?”

It’s asking:

* How much do they like pets?
* What else do they like?
* What behaviors correlate with buying?
* What demographic + economic signals compound that?

Maybe your buyers aren’t just “pet lovers.”

Maybe they’re:
Pet lovers

* Aged 50–55
* 4 kids
* Top 10% household income
* Recently browsing eComm sites

Interest targeting can’t capture that nuance.

Broad can.

Why broad wins in 2026:

1. It finds buyers you’d never manually select
2. It models nuance, not just surface-level labels
3. It adapts in real time based on conversion data
4. It compounds signal density over time

Interest targeting = static labels.
Broad targeting = dynamic, multi-dimensional modeling.

In 2026, machine learning is better at finding your customer than you are.

Run broad.

Let the algorithm do its job.

How many creatives do you actually need per week to scale?

There's a good way to solve this:

1. Figure out your hit rate (your average rate of getting a high performer)
2. Figure out how much spend a high performer can hold on average
3. Then you've got your maths

Example:

You want to spend $10K/day.

You know your hit rate is X%.

You know each high performer can hold Y amount in spend.

You just back-propagate to the amount of ads you need.

If you can't be bothered doing that, here's an easy back-of-napkin number:

For every $1,000/day in ad spend, one new ad is generally going to get you in a really good position.

The formula:

Daily ad spend ÷ $1,000 = Number of new ads per day

Examples:

$10K/day → 10 new ads/day → 70 ads/week
$50K/day → 50 new ads/day → 350 ads/week
$100K/day → 100 new ads/day → 700 ads/week

This is a rough guideline.

Your actual number will depend on:
- Your creative hit rate (how many ads become winners)
- Your average spend per ad (how much each ad can hold)
- Your niche (some niches need more diversity)

But as a starting point, 1 ad per $1K/day is solid.

The lesson:

Most brands don't launch enough creative.

They're trying to scale to $10K/day with 5 new ads per week.

That's not enough.

You need 70 ads/week to sustain $10K/day.

If you're not launching enough creative, you'll hit a ceiling.

Scale your creative production with your ad spend.

Otherwise, you'll fatigue your ads faster than you can replace them.

The issue with CBOs when you don't have a lot of conversion data:

They over-prioritise soft metrics.

In fact, this is how the entire Meta algorithm works.

When you first launch a campaign or ad set, it's not actually optimising for purchases.

Because there are none. Or at least aren't a lot.

So instead, where there's a lot more signal in clicks and impressions.

Meta is going to prioritise all of these soft metrics - to decide how to distribute spend.

That's why if you're running an account with a very high average order value (so you don't have a lot of purchase volume turning through), and you run an ABO structure...

What ends up happening:

You might have 3 ad sets and all the spend just ends up going into the top ad set.

Not because it's driving the best financial outcome for you.

But because it simply has the highest CTR and the lowest CPC.

You have to be really careful about this, particularly when structuring the account.

The lesson:

Soft metrics get prioritised, at least within the first 1-7 days of something new getting launched.

This occurs under ad sets and under campaigns.

Be careful when:
- Running high AOV products (low conversion volume)
- Mixing images and videos in the same campaign
- Using CBOs with new ad sets

The fix:

1. Separate images and videos into different ad sets
2. Use ABOs for high AOV products (forces spend distribution)
3. Wait 14 days before judging based on soft metrics
4. Look at actual ROAS, not just CTR

Your best ad might be your worst problem.

It trains you to stop testing.

Then performance drops and you have no pipeline.

Here's the rule:

Winners get cloned into new angles immediately.

Same proof, new message, new buyer moment.

If one creative drives most of your revenue, you don't have a strategy.

You have a single point of failure.

Why this happens:

You find a winning ad.

It's crushing it.

3x ROAS, scaling well, everyone's happy.

So you stop testing.

Why would you? Things are working.

Then 6 weeks later, the ad fatigues.

Performance drops 40% overnight.

You scramble to find new creative.

But it takes 2-4 weeks to brief, produce, and test new ads.

You have a gap in performance.

Revenue drops.

Panic sets in.

The fix:

Treat every winning ad as a starting point, not a destination.

When you find a winner, immediately ask:

1. What angle is working? (Problem-solution? Transformation? Social proof?)
2. What audience is this speaking to?
3. What buyer moment is this hitting? (Awareness? Consideration? Intent?)

Then clone it:
- Same proof, different message
- Same message, different avatar
- Same avatar, different format
- Same format, different problem

This creates a pipeline of new creative before you need it.

The goal:

Never have one ad driving more than 30-40% of your revenue.

If you do, you're one fatigue event away from a crisis.

Diversify your creative portfolio like you'd diversify an investment portfolio.

Winners get cloned.

Immediately.

Always.

That's how you stay ahead of fatigue.

If you want to reposition a product…

Study what this collagen brand is doing.

1,200+ ads live.
58 videos.
87 images.
UGC.
High production.
Partnership ads.
Dynamic creative.

That’s not chaos.

That’s controlled angle testing.

They’re pushing multiple hooks:

* Weight loss.
* GLP-1 support.
* Skin + hair.
* Bloating.
* Energy.

Here’s the smart bit most brands miss:

Every major angle has its own landing page.

Click a beauty ad?

You land on a beauty page.

Click a GLP-1 ad?

You land on a GLP-1 specific page explaining support, FAQs, context.

That’s how repositioning actually works.

Because here’s the problem:

Collagen does 5,000 things.

If I click a “weight loss” ad…

And land on a generic collagen page talking about joints, hair, gut health and nails…

Continuity is dead.

Conversion drops.

Angle matching is the unlock.

Ad angle → Dedicated page → Reinforced belief.

That’s how you scale multi-angle creative.

Now the opportunity:

They’re running a 28-day challenge.

Great idea.

Challenges are strong because they:

* Add structure.
* Add community.
* Add urgency.
* Increase AOV via bundles.

But the execution matters.

If I land on the page and can’t clearly answer:

“What is this?”
“What do I get?”
“How do I win?”
“Why should I care?”

You’ll leak conversions.

Here’s the fix for any challenge funnel:

1. One clear headline.
   “Join the 28-Day X Challenge to achieve Y.”

2. Bullet the tangible benefits.

* Weekly trackers.
* Expert Q&A.
* Prize breakdown (clearly explained).
* Exact entry rules.

3. Visual roadmap.
   Week 1 → Week 2 → Week 3 → Outcome.

4. Tight FAQ that answers real objections.

Most $5M–$50M brands test more ads.

$50M–$500M brands test:

* More angles.
* More pages.
* More positioning narratives.

Creative volume gets attention.

Angle-to-page continuity gets scale.

If you’re repositioning a hero product this year…

Don’t just change the ad.

Change the destination.

In 2026, creative is the targeting.

But most brands are trying to learn creative the wrong way.

You can find:

* 100s of videos on account structure
* Deep dives on Shopify analytics
* Endless media buying breakdowns

But try finding a clear system for “how to make a good ad”.

You can’t.

Because most creative training is intuition-based.

You’ll hear things like:

“The hook just isn’t strong.”
“The body needs to flow better.”
“This angle feels off.”

If you’re analytical, that’s useless.

You don’t need vibes.

You need a framework.

If creative still feels random in your business, it’s not a talent issue.

It’s a lack of structure.

Here’s a simple 3-step framework we use to pressure test every ad:

1. Attention
2. Communication
3. Direction

That’s it.

Step 1: Does it capture attention?
If they don’t stop, nothing else matters.

Check:

* Is the first 2 seconds visually disruptive?
* Is the first line outcome-driven or pattern-breaking?
* Would you stop for this in your own feed?

Step 2: Does it clearly communicate the product?
Not the vibe. Not the lifestyle.

The product.

Can someone answer in 5 seconds:

* What is this?
* Who is it for?
* Why should I care?

If clarity is weak, conversion will be weak.

Step 3: Does it drive action?
Is there a clear next step?

Not vague.
Not implied.

Clear direction.

* Shop now
* Start your trial
* Get 20% off today

Most underperforming ads fail at one of these three.

Not because the team isn’t creative.

But because they never audited the ad against a framework.

If you’re running $5m–$500m per year, creative can’t be “gut feel”.

It needs to be systemized.

Next time an ad underperforms, don’t ask:

“Is this a bad concept?”

Ask:

Where did it break?
Attention?
Communication?
Direction?

Creative becomes scalable the moment it becomes measurable.

If your ad is “crushing it”…

But you have a big organic audience…

Be careful.

Here’s the influencer ad trap killing scale for $5M–$500M brands 👇

An ad gets:

* 5,000 likes
* Dozens of comments
* Strong blended ROAS

Looks like a winner.

But it’s not the ad.

It’s the audience.

What’s actually happening:

1. You target 100,000 people
2. 50% already know you
3. They convert at 2–3%
4. Cold traffic converts at 0.5% (or worse)

Example:

* $10k to warm → 10x ROAS
* $20k to cold → 1.5x ROAS

Blended result?

$30k spend
$130k revenue
4.3x ROAS

Everyone celebrates.

But you’re scaling the 1.5x.

Not the 4.3x.

The 4.3x is propped up by owned attention.

This is why influencer-led and organic-first brands struggle when moving to paid:

* The hook is the founder
* The creative lacks diversity
* The algorithm isolates into familiar audiences
* Cold never truly performs

Blended numbers lie.

Incrementality doesn’t.

What to do instead:

1. Segment warm vs cold properly
2. Make creative built for cold audiences
3. Expect cold ROAS to be materially lower
4. Run ongoing lift tests
5. Make scale decisions on incremental return, not blended

If you don’t know your incremental ROAS…

You don’t know your real ceiling.

And if you’re pushing spend based on blended MER…

You’re flying blind.

The influencer led brands that scale are the ones who understand where performance actually comes from.

Helping Scale D2C brands | Google and Facebook ads | Performance Marketing Manager at Bunaai | Digital Marketing

Are you a D2C brand struggling with performance marketing?
I help D2C brands profitably run paid ads and scale them.🚀 I have worked with brands ranging from spends of Rs 30,000 a day to over Rs 3 lakh a day and have gotten a blended ROAS between 4 to 7 for these brands.

Feel free to reach out of if you are :
🎯A D2C brand that is running your campaigns on a break - even or losing money on you ads campaigns
🎯Struggling to run ads on a platform that you think has potential 
🎯Finding it hard to maintain your ROAS as you scale your campaigns

I can help you :
✅ Build a profitable sales funnel that will help maximize your ROAS and make it easier to scale
✅ Maximize your brand's potential on platforms by improving your content , communication and audience targeting
✅ Retain more of your existing customers and serve them better

I am open to connecting with you to help you grow your brand and sky rocket your sales. Head to my website at the top and book a free appointment now

Aakash Baglane

Why Rahul Dravid is the cricketer who scares me most.

Not because of raw talent. He had none the world noticed.

No signature shot. No helicopter. No flashy six. Just a man who looked painfully ordinary for 16 years.

Then you see the numbers:
10,000+ Test runs • 10,000+ ODI runs • T20 World Cup winner (Coach) • U19 World Cup winner (Coach) • Mentor to Pant, Gill, Iyer

All in one lifetime.

How does "ordinary" achieve the extraordinary?

He had things that don't trend:
🧱 Patience that looked boring
🧱 Discipline that looked invisible
🧱 Grit that looked forgettable
🧱 Humility that looked unremarkable

Compounded over 16 years, "ordinary" became one of India's most decorated careers.

The lesson?
We obsess over raw talent and instant results.

Dravid's life proves consistency beats hype. Process beats spotlight. Character beats charisma.

In a world chasing viral moments, the real flex is just showing up. Every day. Boringly. Relentlessly.

That's not just cricket. That's how you build anything that lasts.

Who's your Rahul Dravid?

#Leadership #Cricket #RahulDravid #Consistency

Helping Scale ads for D2C brands | Google and Facebook ads | Performance Marketer

This month, we helped a brand scale revenue by 49% month-on-month.

Here's the exact framework we used 👇

Step 1: Split the products by price

Running ₹3,500 products and ₹7,000 products under the same cost cap is insanity. One bleeds margin, the other never scales.

We created three buckets:
• Below ₹4,000
• ₹4,000 – ₹6,000
• Above ₹6,000

Each with its own cost cap. Scale without slaughtering efficiency. This meant there was enough to spend on new buyers looking to try something without a hole in their pocket and the high value buyers who increase the AOV.

Step 2: Split by category (another experiment)

Each category got:
• Its own campaign
• Its own cost cap
• Its own benchmarks

We allocated ~40% budget to both these campaigns. 

Step 3: Flooded the top of funnel (40% budget)

Cost caps need quality data to work. We gave it:
• Influencer collabs
• Best-seller videos
• Sharp geo/age/gender splits

Found winning cohorts fast. Doubled down faster.

Step 4: Saved 20% for remarketing + creative tests

Bottom funnel matters. So does testing what's next.

The result?
- 49% MoM revenue growth.
- Zero discount increases.
- Zero budget hikes.

The lesson?

You don't need cheaper prices. You need cleaner structure.

Still running campaigns the old way? You're leaving revenue on the table.

What's one structural fix you're making this month?

#D2C #PerformanceMarketing #FacebookAds #GrowthStrategy

taplio

The Battle

Nathan Perdriau

Aakash Baglane

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taplio

The Battle

Nathan Perdriau

Aakash Baglane

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Nathan Perdriau's LinkedIn Performance

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Performance ScoreNathan Perdriau's LinkedIn Performance