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

As an ads guy, it hurts to say...
But organic > ads.

40 days of posting on Instagram.

The reach I've generated would cost $67,000 in ad spend.

Zero dollars spent.

Here's What I've Learned:

1. Volume Negates Luck

You can't post twice and expect to go viral.
Consistency compounds.
The more you ship, the more chances you have to hit.

2. No Platform Has a Daily Posting Limit

There's no cap on how much you can post.
Most people post 3x per week and wonder why they're not growing. I now post 10 times a day.

3. Diversity Matters

Don't just talk about one thing one way.
Different formats.
Different angles (tactical, contrarian, personal).
Different hooks (questions, stats, stories).

Variety attracts different segments of your audience.

4. Contrarian Takes Matter

Everyone says the same thing.
"Test more creatives." "Improve your landing page." "Focus on retention."

Say something different.
Challenge common beliefs.

Share what others won't.
That's what stops the scroll.

5. Experience Matters Most

The uncomfortable truth:
I wouldn't have any success with organic content if I hadn't spent 6 years deep in performance marketing.

Content without expertise is noise.
Expertise without content is invisible.

The Order of Operations:

- Do the work for years (build real expertise)
- Run ads to acquire clients and prove your methods
- Document what you've learned through content
- Scale organically once you have something worth saying

You can't skip step 1.

Producing Better Financial Outcomes for 8-9 Figure Brands

I analysed I Love Ugly's ad library (250 ads running).

One thing stood out immediately:

They're leaning HEAVILY into partnership ads.
And most fashion brands are completely missing this opportunity.

Here's what I found...

What They're Doing Well:

- Heavy use of DCO to avoid page limits 
- Zero DPA's (quality over quantity) 
- Partnership content driving their best performance

The Missed Opportunity:

I scrolled through their entire library.

Everything is high-production studio shoots with clean text overlays.

Zero low-fi content. Zero organic-feeling ads. Zero auditory hooks.

Now here's the interesting part:

When I sorted by longest-running ads...

The top performers (some running 3+ years) are ALL high-production.

So their testing shows this works for them.

But there's a gap:

Their fastest-growing category is activewear.
They're pushing it hard with partnership ads.
Activewear lends itself to organic, UGC-style content naturally.

The Play:

Partnership ads are working. Activewear is growing. But they haven't tested low-fi content in this specific vertical.

That's the white space.

If you're running partnership ads but keeping everything polished and produced...

You're leaving engagement on the table.

The best partnership content feels native to the platform.

Not like an ad that happens to feature someone else.

Test organic formats in your fastest-growing categories first.

That's where audience receptiveness is highest.

We ask this in almost every interview:

"What is a good offer?"

Almost no one gets it right.

Here's the framework:
A good offer creates a value discrepancy in the market.

From your perspective as the seller:
Offer Quality = Perceived Value - Your Actual Costs

You want customers to think your product is worth WAY more than it costs you to deliver.

Example: Customer thinks: "This must cost $200 to make" Your actual cost: $40 Selling price: $100

That gap? That's your margin opportunity.

But here's where it gets interesting:
Customers are running the OPPOSITE equation in their heads.

From the buyer's perspective:
Offer Quality = What It Costs You - What I'm Paying

They want to believe you're selling close to cost.

That you're barely making margin.

That they're getting an incredible deal.

This is why 50% off Black Friday sales work so well:

Customer logic: "These normally sell for $100, now they're $50" "The cost to make these didn't just drop in half" "This is an amazing deal"

Demand goes up.

But as a seller? You just torched your margin.

The equation shrinks for you even as it grows for them.

So what's a GOOD offer that works for both sides?

A reframing of value where the consumer believes they're getting enormous value, but your COGS remain low.

An example:

Free sample packs at cost of shipping.

Customer pays $7 shipping. Gets $20+ worth of samples. Feels like they're getting it free.

But that $7 covers your COGS AND your CAC.

You're break-even on acquisition. 

All profit comes from purchase 2 and 3. 

You've maximised perceived value without sacrificing margin.

The principle:

Stop competing on price.
Start engineering perception.

The best offers make customers feel like they're stealing from you...

While you're actually widening your margins.

How we scaled "unscalable" outbound to 120 clients.

Four years ago, we made custom Loom videos for prospects.

Walked through their website, ads, and gave specific recommendations.

Worked great. But we could only make 10 per day.

So we hired a team.

Conversion rates tanked immediately.

Our faces weren't on the videos anymore.

The fix:

1. Scripted every recommendation
2. Recorded our voice and face for each one
3. Editors superimposed our recordings onto prospect footage

Now it looked personal. But the team did the work.

We ramped to 120 videos per day.

Here's what broke at each stage:

Hit 20/day → conversion dropped
- Fixed: Hired better prospectors

Hit 50/day → no new clients
- Fixed: Hired SDRs to call and get videos watched

Hit 80/day → ran out of editors
- Fixed: Hired full-time editors, recorded at 2x speed

Hit 100/day → too complex to manage
- Fixed: Hired outbound manager

The pattern:

Push the system until it breaks.
Find the bottleneck.
Fix it.
Repeat.

This is how you scale anything:
- Paid ads
- Content production
- Outbound systems
- Operations

People said custom videos with 20+ recommendations weren't scalable.

We proved them wrong.

Key lesson:

You can scale the unscalable.

Just identify what's restricting growth and solve that specific constraint.

Ask yourself right now:

What's the one thing stopping me from doubling output?

Then go fix it.

VSL ads in eCommerce are the opportunity right now.

I've seen a VSL recently hit $360K in total spend (one asset).

Here's their structure:
1. Hook with education (not product)
2. Problem agitate 
3. Mechanism explanation (why it works)
4. Objection handling (why past solutions failed)
5. Product proof (UGC, testimonials)
6. Risk reversal (guarantee)
7. Simple CTA

Why this works:
- Feels educational, not salesy
- Removes doubt in specific order
- Pre-qualifies buyers before landing page

I did a full video breaking down exactly how to make a VSL that can hold $100k+ in ad spend. Linked in comments below.

Birthday sales are an easy way to create a spike without feeling salesy. Time them right and they can bridge the quiet months without hurting the brand.

Three things stop creatives from fatiguing...

IM8 did $100M in under 11 months.

80% of their first-time customers are on subscription.

Sounds like the dream, right?

Wrong.

Here's why copying this strategy could destroy your business:

What IM8 Does:

Subscription is the DEFAULT option.
Save 30% messaging everywhere.
One-time purchase? Hidden on a separate page.

I bought their product to test the funnel.

I didn't even realise one-time was an option until I went looking for it.

The Subscription Trap:

Most founders think: "If we get more people on subscription, we'll have higher LTV. Then we can pay more to acquire customers. This is the key to scale."

The math seems obvious.

But here's what the data actually shows:

Your CAC on subscription customers is MUCH higher.

Yes, LTV goes up when you force subscriptions.

But your cost to acquire those customers skyrockets.

And the equation breaks.

What You Should Actually Track:

Don't just look at LTV.

Look at CAC to LTGP (Lifetime Gross Profit within 365 days).

Run this for BOTH scenarios:
→ CAC for one-time purchases
→ CAC for subscription customers

Then compare:
→ Which has better CAC to LTGP ratio?
→ Where's the actual arbitrage?

The Reality:

In 90% of cases I've analysed...

You're better off acquiring one-time customers.

Yes, they have slightly lower LTGP.
Yes, they're not "as high quality."

But you can acquire them SO much cheaper that the arbitrage is better.

Before You Go Subscription-Only:

Test both funnels side by side.

Measure actual CAC (not assumed).

Calculate the 365-day gross profit for each.

Compare the ratios.

If your subscription CAC is 2.5x higher but LTV is only 1.8x higher?

You're losing money on every "premium" customer.

The Framework:

1. Run split traffic: 50% subscription-default, 50% one-time-default
2. Track CAC for each cohort separately (not blended)
3. Measure LT GP at 90, 180, and 365 days
4. Calculate the CAC to LTGP ratio for both
5. Scale whichever has better unit economics

Subscriptions don't save struggling businesses.

Better margins do.

Know your numbers before you commit to a model that might be bleeding you dry.

Traffic campaigns feel cheap, but they are cheap for a reason...

Paid ads is for acquisition.

Not retention.

Here's why:

At least 70% of returning customer revenue comes from:
- Email
- SMS
- Direct traffic
- Social (organic)

Your most loyal customers return to your website naturally to see new product drops.

They don't need ads to come back.

This is where most brands get confused.

They measure paid ads against total revenue.

That's fundamentally wrong.

Paid ads don't drive all your revenue.

They drive new customer revenue.

The metrics shift:

Instead of MER (total revenue / total ad spend)

Use Acquisition MER (new customer revenue / ad spend).

This applies to every metric:
- ROAS → new customer ROAS
- CPA → new customer CPA
- Profit contribution → new customer profit

Everything should be measured against acquisition only.

Example:

If you're spending £100K on ads and generating £400K in total revenue, your MER is 4x.

But if only £250K came from new customers, your actual acquisition MER is 2.5x.

That's a massive difference in how you should be evaluating performance.

Caveats:

Fashion brands are different.

High product drop rates mean you need some retention spend.

But for most businesses?

You're spending way too much on retention through paid ads.

Focus retention efforts on:
1. Email flows and campaigns
2. SMS campaigns
3. Loyalty programs
4. Organic social

Focus paid ads on:
1. Acquiring new customers
2. Testing new audiences
3. Scaling what works

Stop conflating the two.

Measure them separately.

Optimize them separately.

Creative is everything in 2026, but most training doesn’t teach you how to actually make a good ad...

Recessions consolidate to the top.

Great businesses thrive.

Weak ones collapse.

We're seeing this play out right now.

Ad accounts that were crushing 2 years ago with zero fundamentals are struggling.

No creative strategy.
No retention systems.
Just cranking Meta budgets and hoping for the best.

It worked when CPMs were cheap and demand was high.

Now those brands are hitting £10M/year and falling apart.

Here's what's breaking:

Their MER is dropping.

They audited their business and realized:
- No retention system in place
- Terrible cohort lift after acquisition
- Creative thrown in randomly with no strategy
- Heavy discounting to spike revenue

Now their entire customer base only buys on discount.

Margins squeezed from 70% to 40%.

All of this compounds.

The brands that survive recessions are the ones that built on fundamentals:

1. Strong unit economics
Contribution margins above 60%.
Can afford to acquire customers profitably at scale.

2. Retention systems
Email flows, SMS, subscriptions, loyalty programs.
Not just hoping people come back.

3. Strategic creative production
Angle testing, offer testing, message-market fit.
Not random UGC hoping something sticks.

4. Pricing discipline
No training customers into discount dependency.
Promotions are strategic, not desperate.

When times are easy, bad strategy gets rewarded.

When the market tightens, fundamentals win.

If your growth is built on "just spend more on Meta"...

You're in trouble.

Fix your fundamentals before the market forces you to.

Third-party attribution tools aren't that helpful.

Here's why:

Attribution is unknowable.

These tools try to identify the first click that leads to a purchase.

But they don't pull you back far enough in the journey to reliably show where spend should go.

So why do people think they're amazing?

Because their platform setup is wrong in the first place.

Third-party tools just expose the flaws in your account structure.

Example 1: Meta attribution

You log into Triple Whale.

Meta says 5x ROAS.
Triple Whale says 3x ROAS.

Suddenly you have clarity: "That's why we can't scale past 3x on incremental spend."

But you didn't need Triple Whale to tell you this.

The issue?

Your Meta attribution is set to 7-day click + 1-day view.

You're getting massive overattribution from view-through conversions right before purchase.

The fix:

Change campaigns to 7-day click only.

Or just check the attribution breakdown in Meta for 7-day click ROAS.

Problem solved. No third-party tool needed.

Example 2: Google attribution

Google shows 10x ROAS in-platform.

Your third-party tool shows 4x ROAS.

People think: "This tool revealed Google isn't performing well."

The real issue?

Brand campaigns and existing customers are sitting in your PMAX and shopping campaigns.

They're inflating your ROAS.

The fix:

1. Segment brand campaigns properly
2. Exclude existing customers from PMAX
3. Structure campaigns correctly

Numbers will match up. No third-party tool needed.

What third-party tools actually do:

They highlight problems in your account structure.

But they don't solve them.

You still need to fix:
- Attribution windows
- Campaign segmentation
- Audience exclusions
- Account structure

If your setup is clean, platform data is reliable enough.

Stop relying on tools to tell you what your account structure should already show.

Fix the fundamentals first.

High repeat rate ≠ profitable product.

Here's what to track instead:

The Problem:

Most brands look at repeat purchase rates by product and think they've found their winners.

Product A: 17% repeat rate → "This is our hero product!"
Product B: 12% repeat rate → "Deprioritise this one"

But repeat rate alone is meaningless.

What You Should Actually Measure:

Contribution margin lift.

Not just "do they come back?"

But "how much more profit do we make when they do?"

The Framework:

Step 1: Calculate first-time customer contribution margin by product

Step 2: Calculate lifetime contribution margin by product (track 365 days)

Step 3: Find the dollar lift between the two

Step 4: Rank products by margin lift, not repeat rate

Here's Why This Matters:

Let's say you have two products:

Product A:
- 17% repeat rate (looks amazing)
- First purchase GP: $20
- Lifetime contribution margin: $35
- Lift: $15

Product B:
- 12% repeat rate (looks mediocre)
- First purchase GP: $35
- Lifetime contribution margin: $80
- Lift: $45

You'd think Product A is the winner.

But Product B is generating 3x more profit per customer over their lifetime.

That's where you should be aggressive on acquisition.

How To Apply This:

Once you know your contribution margin lift by product:

Adjust your acceptable CAC (Cost to Acquire A Customer) accordingly.

Product with $15 lifetime lift → Cap CPP at $40
Product with $45 lifetime lift → Push CPP to $50+

You can afford to pay more upfront for customers who generate more profit long-term.

The Key Metric:

Stop optimising for repeat rate.

Start optimising for contribution margin lift.

Repeat rate tells you if customers come back.

Margin lift tells you if it's worth acquiring them in the first place.

Why Most Brands Miss This:

Shopify doesn't make this analysis easy.

You need to build your own model that tracks:
- Product-level contribution margins
- First purchase vs lifetime margins
- Repeat rates tied to entry products
- Dollar lift, not just percentage lift

But once you have it?

You'll know exactly which products to scale and which to pause.

The metric that matters isn't how often they buy.

It's how much profit they generate when they do.

A marketing win that does not show up in the P&L is not a win.

Account simplification has gone too far.

Some people are advising brands to run just one Meta campaign.

Terrible advice.

Here's why you need structure:

1. New vs returning customer isolation

Without segmentation, Meta juggles budgets however it wants between these groups.

The result?

Way too much spend on returning customers.

You're paying to remarket to people who would've bought anyway.

You need separate campaigns to control this split.

2. Geo segmentation

If you don't segment by region, you can't:
- Balance sell-through rates across markets
- Manage different 3PLs effectively
- Allocate spend to hit regional revenue targets

One campaign means Meta decides where your budget goes.

Not you.

3. Structured testing

You need structure to run conclusive tests.

Without it, you can't:
- Isolate variables
- Draw clear conclusions
- Inform future decisions

One campaign with everything thrown in = zero learnings.

4. Product margin profiles

Meta doesn't know your gross margins.

It will push whatever converts easiest.

Often that's your lowest AOV product with the worst margins.

If you optimize for "maximize conversions" (the default), Meta will drive you towards the worst profit contribution.

You need campaign structure to prioritize high-margin products.

The fix:

Build structure around:
- New vs returning customers
- Geographic regions
- Product margin tiers
- Testing frameworks

Account simplification is good.

Oversimplification kills profitability.

Don't confuse the two.

Your Facebook ads aren't performing as well as you think...

How we forecast eComm revenue (quantitative + qualitative).

Most forecasts fail because they're purely backward-looking.

Here's our approach:

Layer 1: Quantitative forecast

Two sides:

1. Returning customer revenue (highly predictable)
2. New customer revenue (larger error bars)

For new customer revenue:

Look at every day for the last 2-3 years.

Plot daily efficiency (return on spend) on a graph.

It looks like an exponential line going down.

The more you spend, the lower the return.

Run a logarithmic regression through that data.

This finds the optimal spend level for each month historically.

Now you have a baseline: "If August happened again like the last 3 years, what should we spend?"

The problem:

The future is never the past.

Every model will be wrong.

Layer 2: Qualitative adjustments (human input)

Example adjustments:

"In August last year, we were out of stock in core products. We need to add $100K in revenue to account for that."

"We're launching a new activation. We expect it to hold an extra $10K in spend at X efficiency."

"Black Friday creative tested well. We'll scale 20% harder than historical baseline."

Layer marketing events on top of the baseline forecast.

Why this matters:

Without the qualitative layer, you end up with vague targets like "$2M revenue, $100K spend."

If it doesn't work, you can't diagnose why.

With layered forecasting:

You can say: "We expected $300K from that product launch and it only generated $150K."

Now you have learnings.

You know where to adjust spend allocation going forward.

How to implement this:

1. Pull 2-3 years of daily spend and efficiency data
2. Plot it (spend vs ROAS or spend vs revenue)
3. Run a logarithmic regression to find optimal spend per month
4. Overlay qualitative adjustments (stock levels, launches, activations, seasonal factors)
5. Hold accountability to both revenue expectation and spend allocation
6. Review post-period and refine the model

This gives you:
- A data-driven baseline
- Context for anomalies
- Clear accountability
- Learnings that compound over time

Stop guessing at forecasts.

Build a model that learns.

Alo yoga is absolutely crushing it on Meta...

The goal of ads isn't the highest ROAS possible.

That's how you kill growth.

Instead, focus on maximizing contribution profit.

You're thinking: "Aren't those the same thing?"

No. And here's why it matters:

ROAS relies on attribution.

Contribution profit is tied directly to cash.

The best operators see reality clearly.

When your metrics sit closer to reality (cash, profit), you get:
- Better feedback cycles
- Sharper decisions
- Campaigns that actually move the needle

If you're optimizing for the highest ROAS in Meta or Google...

You're operating further from reality.

Further from your P&L.

Here's the shift:

Bridge your metrics closer to what actually matters.

When you launch a new test or optimize a campaign, you need to know it's impacting the thing you care about.

Simple question:

Would you rather have:
- 50x ROAS but make no money
- 2x ROAS but make £5M in profit

You'd choose the second every time.

So why are you still optimizing for the first?

How to fix it:

1. Calculate contribution profit per order (revenue - COGS - ad spend - shipping - fees)
2. Set this as your north star metric (not ROAS)
3. Build your dashboards around profit, not attributed revenue
4. Optimize campaigns for maximum profit dollars, not maximum ROAS
5. Test new channels and creatives against profit impact, not efficiency ratios

Stop chasing vanity metrics.

Start chasing cash.

Zack Miller

Senior Digital Marketing Manager (Head Of Google Ads & Reporting)

I’ve been a DTC Ecommerce Growth Marketer since 2019. I’ve done everything from media buying and client strategy, to agency internal operations.

I’ve contributed to the growth of over 200 ecommerce brands in my agency experience. Now, as an independent growth marketing partner, I work closely with a small number of brands on their new customer acquisition strategy.

I don’t offer coaching or “hands off keyboard” consulting because I share what I know for free on my website and social media.

If you’re a brand owner and need help acquiring new customers, let's talk!

Head of Google Ads

Let’s be honest. 
Media buying skills don’t matter on Meta anymore.
 
Seriously, the old “audience segmentation” playbook is dead.
 
I see accounts with 30-40 ad sets with different audiences, different exclusions.
And guess what? They’re all hitting the same people.
 
You’re not optimizing anything. 
You’re just introducing human bias.
 
The truth is, Meta’s smarter than you now.
 
It already knows who’s converting and who’s seen what.
It doesn’t need your help – just your data.
 
Right now, the best-performing accounts I see run almost everything in one ad set.
 
That used to be a terrible idea. 
Now it’s the only thing that works. 
 
So stop trying to outsmart the algorithm.
 
Forget about manual targeting or funnel segmentation.
Focus on your inputs: Stronger copy, better creative. 
 
The job isn’t media buying anymore. 
It’s machine training: teaching the algorithm how to win.
 
Bold claim? Sure, but I have the data to prove it.

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