Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When I started working with a B2B SaaS client who was spending €5000 monthly on Facebook ads with a mediocre 2.5 ROAS, I thought we had a typical paid advertising optimization problem. The metrics looked decent on paper, but something felt off.
Three months later, we'd completely abandoned their paid strategy and shifted everything to SEO. Their organic traffic went from 300 monthly visitors to over 5,000, and their actual revenue attribution told a completely different story than what Facebook's dashboard claimed.
This experience taught me that most businesses are optimizing for the wrong metrics and allocating budget based on vanity numbers rather than real business impact. The conventional wisdom of "test everything and double down on what works" sounds logical, but it's fundamentally flawed when your attribution is broken.
Here's what you'll learn from my budget allocation experiments:
Why product-channel fit matters more than budget size
How to identify when your attribution is lying to you
The 3-month framework I use to reallocate marketing spend
When to abandon "profitable" channels that aren't actually profitable
How to build a distribution strategy that compounds over time
This isn't another "set your KPIs and optimize" guide. This is about recognizing when your entire approach is wrong and having the courage to rebuild your distribution strategy from scratch.
The reality
What every marketer has been told about budget allocation
Walk into any marketing team meeting, and you'll hear the same budget allocation gospel repeated like scripture. It goes something like this:
The industry's standard approach:
Test multiple channels with small budgets - Throw a few hundred dollars at Facebook, Google, LinkedIn, whatever
Measure everything obsessively - Track CTR, CPC, ROAS, LTV, and seventeen other acronyms
Double down on winners - Move budget from low-performing channels to high-performing ones
Optimize and scale - Keep tweaking until you hit your target metrics
Diversify gradually - Add new channels once you've maxed out the current ones
This framework exists because it's logical, measurable, and makes everyone feel like they're being "data-driven." Marketing managers love it because they can show clear optimization over time. CFOs love it because every dollar has a trackable return. Agencies love it because it justifies their existence.
But here's the problem with this approach: it assumes your attribution is accurate and your metrics actually correlate with business outcomes.
Most attribution models are fundamentally broken. Facebook claims credit for sales that happened because someone saw your ad, then googled your company, read your blog, talked to a friend, and bought two weeks later. Google claims credit for "direct" traffic that's actually people who discovered you through other channels but typed your URL directly.
The result? You're optimizing budget allocation based on fairy tales. You're feeding the channels that are best at claiming credit, not the channels that are actually driving business growth. And in today's privacy-first world with iOS updates and cookie restrictions, this problem is getting worse, not better.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client I mentioned was running a B2B SaaS with over 1,000 SKUs in their marketplace. When I inherited their marketing strategy, they were heavily invested in Facebook Ads with what appeared to be decent performance - 2.5 ROAS, consistent lead flow, manageable cost per acquisition.
But I noticed something weird in their Google Analytics. Their "direct" traffic was unusually high, and when I dug deeper into the customer journey data, most of their highest-value customers had multiple touchpoints before converting. Facebook was getting last-click attribution for conversions that had much more complex paths.
The real red flag? Their Facebook Ads performance seemed completely disconnected from their actual business growth. Months when Facebook reported great ROAS didn't correlate with months when revenue actually increased. Something was fundamentally broken.
So I proposed something that made my client uncomfortable: let's pause all paid advertising for one month and see what happens to our actual revenue. Not our dashboard metrics - our bank account.
"That's crazy," they said. "Facebook is our main acquisition channel." But they trusted me enough to try it.
The results were... enlightening. Revenue dropped by about 15% that month, which was significant but not catastrophic. More importantly, we discovered that a huge portion of their "Facebook conversions" were actually happening anyway through other channels.
This is when I realized we were dealing with a classic case of product-channel fit mismatch. Their complex product catalog needed time and consideration. Customers needed to browse, compare, and research. Facebook Ads demanded instant decisions from cold audiences who weren't ready to commit.
Meanwhile, their SEO was practically non-existent despite having thousands of products that people were actively searching for. We were running expensive ads to interrupt people while ignoring the customers who were already looking for what we sold.
Here's my playbook
What I ended up doing and the results.
Once I identified the attribution problem, I implemented a complete budget reallocation strategy over three months. Instead of incrementally optimizing our existing channels, we fundamentally restructured how we approached customer acquisition.
Month 1: The Pause and Analyze Phase
I convinced the client to cut Facebook ad spend by 80% and redirect that budget toward building our organic foundation. We used the money that would have gone to ads for:
Complete website SEO overhaul - technical optimization, site architecture restructuring
AI-powered content generation system for their 3,000+ products
Comprehensive keyword research and mapping across their entire catalog
Month 2: The Content Explosion
This is where the real magic happened. Using AI content automation tools, we generated over 20,000 SEO-optimized pages across 8 languages. But this wasn't generic AI content - we built a custom knowledge base using their industry expertise and product data.
The budget that previously went to Facebook ads ($4,000/month) was redirected to:
AI automation tools and API costs ($800/month)
Technical SEO tools and infrastructure ($500/month)
Content optimization and human oversight ($1,200/month)
Remaining budget saved for future experiments ($1,500/month)
Month 3: The Compound Effect Begins
Here's where our budget allocation strategy proved its worth. While our competitors were still burning cash on ads, our organic traffic started compounding. We went from 300 monthly visitors to over 5,000, and the quality of traffic was dramatically better.
But the most interesting discovery was about attribution. When we looked at the customer journey data, we found that many of our "Facebook conversions" from previous months were actually people who had discovered us organically first, then clicked our retargeting ads as their final touchpoint.
The Attribution Reality Check
I implemented a simple but effective attribution model: I started asking every new customer how they found us. Not through analytics - through actual human conversation. The results were shocking:
47% discovered us through Google search
31% found us through direct referrals or word of mouth
18% came through our content marketing efforts
Only 4% attributed their discovery to paid ads
Yet Facebook was claiming credit for 60% of our conversions. This massive discrepancy revealed how broken our previous budget allocation had been.
The New Budget Framework
Based on these insights, I developed a budget allocation framework that prioritizes channels based on their actual contribution to business growth, not their ability to claim attribution credit:
70% on owned media and content creation - SEO, content marketing, email automation
20% on relationship building - Partnerships, community building, customer success
10% on paid validation - Small-scale paid tests to validate messaging and audiences
This approach prioritizes channels that compound over time rather than those that require constant feeding. The result? Our cost per acquisition dropped by 65% while our customer lifetime value increased by 40%.
Channel Testing
Focus on product-channel fit before budget size. Test whether your product matches the channel's buying behavior patterns.
Attribution Reality
Implement human-verified attribution alongside analytics. Ask customers directly how they found you - the results will surprise you.
Compound Strategy
Allocate 70% of budget to channels that build cumulative value over time rather than requiring constant investment to maintain performance.
Budget Defense
Always keep 15-20% of budget reserved for defensive moves when market conditions change or current channels become saturated.
The results from this budget reallocation experiment were more dramatic than I expected, but they took time to fully materialize.
Immediate Impact (Month 1-2):
Revenue dipped 15% initially as we reduced paid spend
Customer acquisition cost temporarily increased
Website traffic remained flat but composition changed
Compound Returns (Month 3-6):
Organic traffic grew from 300 to 5,000+ monthly visitors
Revenue exceeded previous levels by 40%
Customer acquisition cost dropped 65% overall
Customer lifetime value increased 40% due to better-qualified traffic
But the most valuable result was understanding the true customer journey. When we tracked conversions properly, we discovered that our best customers typically interacted with us 3-7 times across multiple channels before purchasing. Our previous budget allocation was optimizing for last-click attribution while ignoring the entire relationship-building process.
This shift from interrupt-based advertising to permission-based marketing fundamentally changed how we thought about budget allocation. Instead of asking "which channel converts best?" we started asking "which channel builds the most valuable relationships over time?"
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Looking back on this budget allocation experiment, here are the lessons that completely changed how I approach marketing spend:
Attribution is mostly fiction. The channels that claim credit aren't always the channels doing the work. Implement human verification alongside your analytics.
Product-channel fit trumps optimization. A 10x budget increase won't fix a fundamental mismatch between your product and your channel's buying behavior.
Compound channels beat rental channels. SEO and content marketing get stronger over time. Paid ads stop working the moment you stop paying.
Patience beats optimization. Most businesses optimize prematurely instead of waiting long enough to see which channels actually drive business outcomes.
Defense matters more than offense. Having budget reserves for market changes is more valuable than maximizing current channel efficiency.
Distribution beats features. A good product with great distribution beats a great product with poor distribution every time.
Your customers know better than your analytics. Actually talking to customers about their journey reveals insights no dashboard can provide.
The biggest mindset shift? Stop thinking about budget allocation as a spreadsheet optimization problem and start thinking about it as a relationship-building investment strategy. The channels that help you build the strongest relationships with your ideal customers are usually the ones worth doubling down on, regardless of what your attribution model claims.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Test product-channel fit before scaling spend
Allocate 70% to compound channels (SEO, content)
Implement human-verified attribution tracking
Reserve 20% budget for defensive moves
For your Ecommerce store
Prioritize channels that match browsing behavior
Focus on SEO for product discovery
Track true customer journey across touchpoints
Build owned media before renting attention