Sales & Conversion
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
When I started managing Google Ads for e-commerce clients, I religiously followed every "best practice" guide about budget allocation. 20% for search campaigns, 30% for shopping, 15% for display, and so on. The formulas were clean, the spreadsheets looked professional, and the results? Absolutely terrible.
Here's what nobody tells you about Google Ads budget allocation: those "industry standard" percentages are killing your ROI. They're based on averages across thousands of businesses that have nothing to do with your specific situation, product catalog, or customer behavior.
After working with over a dozen e-commerce stores and burning through way too much ad spend following conventional wisdom, I developed a completely different approach to budget allocation. One that's based on actual performance data rather than theoretical frameworks.
In this playbook, you'll learn:
Why traditional budget allocation formulas fail for most stores
My data-driven method for finding your optimal channel mix
How I helped one client reduce their ad spend by 40% while maintaining revenue
The specific metrics I track to reallocate budget in real-time
Why product-channel fit matters more than your total budget size
Ready to stop throwing money at "best practices" and start building a budget strategy that actually works? Let's dive into what the industry won't tell you.
Industry Reality
What every e-commerce guide recommends for Google Ads budgets
Walk into any digital marketing agency or open any Google Ads course, and you'll hear the same budget allocation gospel. The industry has basically standardized around these "proven" formulas:
The Classic 80/20 Rule: Spend 80% on search campaigns (branded + non-branded) and 20% on everything else. The logic seems sound - people searching have high intent, so that's where your money should go.
The Diversified Portfolio Approach: Split your budget across multiple campaign types: 40% search, 30% shopping, 20% display/video, 10% testing. This supposedly reduces risk and maximizes reach across the customer journey.
The Funnel-Based Method: Allocate budget based on funnel stages - 50% bottom-funnel (search), 30% middle-funnel (shopping/video), 20% top-funnel (display/discovery). Makes logical sense on paper.
The Platform Recommendation: Google's own "Smart Bidding" suggests letting their algorithm distribute budget automatically across campaigns. They promise better results with less manual work.
These approaches exist because they're simple to implement and easy to sell to clients. Agencies love them because they can create standardized processes and onboard new team members quickly. The problem? They treat every business like it's the same.
But here's what I learned the hard way: your optimal budget allocation depends entirely on your specific product catalog, customer behavior, and market position. A store selling luxury watches needs a completely different approach than one selling pet supplies. Yet the industry keeps pushing these one-size-fits-all solutions.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about the project that completely shattered my faith in "best practice" budget allocation. I was working with an e-commerce client - let's call them a fashion accessories store - who had been burning through about €3K per month on Google Ads with mediocre results.
When I first audited their account, it looked like a textbook case of poor budget allocation. They were following the classic diversified approach: spreading their limited budget across search campaigns, shopping campaigns, display, and even some YouTube experiments. Every campaign was getting just enough budget to maybe generate a few clicks per day.
The problem was obvious to me - they needed to consolidate their spend and focus on their best-performing channels. So I did what any "expert" would do: I reallocated their budget according to performance data. More money to search (their best ROAS), less to display (their worst ROAS), and optimized their shopping campaigns.
The results after two months? Slightly better, but nothing revolutionary. We improved their ROAS from 2.5 to about 3.2, which was progress but not the breakthrough they needed.
That's when I discovered the real issue. The problem wasn't their budget allocation - it was a fundamental mismatch between their product catalog and the channels we were using. They had over 1,000 SKUs across dozens of categories. Customers needed time to browse, compare, and discover the right products for them. But we were forcing them through high-intent search campaigns that demanded immediate decisions.
Facebook Ads' quick-decision environment was fundamentally incompatible with their shopping behavior. No amount of budget optimization could fix a product-channel fit problem. This realization completely changed how I approach budget allocation - it's not about finding the perfect percentage split, it's about finding the channels where your specific products can actually succeed.
Here's my playbook
What I ended up doing and the results.
After that wake-up call, I completely rebuilt my approach to Google Ads budget allocation. Instead of starting with channel percentages, I start with understanding how customers actually interact with different product types. Here's my step-by-step framework:
Phase 1: Product-Channel Audit (Week 1-2)
Before allocating a single euro, I analyze how each major product category performs across different campaign types. I'm looking for patterns like: Do complex products perform better in Shopping campaigns where customers can see detailed images? Do impulse purchases convert better through Search campaigns? Do seasonal items need more Display budget for awareness?
For that fashion client, this audit revealed that their jewelry performed completely differently than their bags. Jewelry needed visual Shopping campaigns, while bags converted better through branded Search campaigns from customers who already knew what they wanted.
Phase 2: Channel Testing with Micro-Budgets (Week 3-4)
Instead of committing large budgets to "proven" channels, I run small tests across all available campaign types. I'm talking €10-20 per day per channel, just enough to get statistically relevant data. The goal isn't immediate profitability - it's understanding where each product category finds its natural fit.
This is where I discovered something counterintuitive: the fashion client's lowest-priced items (under €50) actually performed best in Display campaigns, not Search. People weren't actively searching for these products, but they'd impulse-buy them when they saw appealing creative.
Phase 3: Budget Reallocation Based on Performance Density
Here's where my approach differs from traditional methods. Instead of allocating budget based on channel type, I allocate based on "performance density" - how much profitable volume each channel can handle before hitting diminishing returns.
For example, their branded Search campaigns were generating a 6x ROAS, but they could only spend about €200/month before running out of search volume. Their Shopping campaigns hit 3.5x ROAS but could absorb €1,500/month profitably. Traditional advice would say "spend more on branded search because the ROAS is higher." My approach says "max out branded search at its natural limit, then scale Shopping campaigns."
Phase 4: Dynamic Reallocation System
The final piece is building a system for ongoing budget adjustments. I track three key metrics weekly: performance density (how much profitable spend each channel can absorb), conversion delay (how long between click and purchase), and seasonal patterns.
This client's data showed clear weekly patterns - Shopping campaigns performed 40% better on weekends, while Search campaigns stayed consistent. So I built automated rules to shift budget from Search to Shopping every Friday, then back again on Monday.
Performance Density
Focus budget where channels can absorb profitable spend, not just where ROAS is highest
Channel Saturation
Each campaign type has a natural spending limit before performance drops - find and respect these limits
Conversion Patterns
Track when different product types convert across channels to optimize budget timing, not just allocation
Product Mapping
Match product categories to their best-performing campaign types before setting any budget percentages
The results from this approach were dramatic. Within three months, the fashion client saw their overall ROAS improve from 2.5 to 4.2, but more importantly, their total revenue increased by 60% with the same monthly ad spend.
The key insight was that we weren't trying to make every channel work equally well. Instead, we found where each product category naturally performed best and doubled down on those combinations. Their jewelry line got 70% of its budget allocated to Shopping campaigns. Their accessories got 60% allocated to Display. Their branded search campaigns got small but focused budgets that maxed out their available impression share.
But the real breakthrough came when we started treating budget allocation as a dynamic system rather than a static split. By shifting budget based on weekly performance patterns, we captured seasonal opportunities that fixed allocation would have missed.
One unexpected result: their cost per acquisition actually increased slightly, but their customer lifetime value increased dramatically. We were attracting customers who bought multiple items instead of single-purchase bargain hunters. This taught me that sometimes the "best" budget allocation optimizes for business growth, not just immediate ROAS.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this framework across multiple e-commerce clients, here are the key lessons that transformed how I think about Google Ads budgets:
Lesson 1: Product-channel fit beats budget optimization every time. No amount of smart budget allocation can fix a fundamental mismatch between your products and your chosen advertising channels. Before optimizing budgets, ensure your products can actually succeed in each channel.
Lesson 2: Performance density matters more than ROAS. A campaign with 3x ROAS that can absorb €2,000/month profitably is more valuable than one with 5x ROAS that maxes out at €300/month. Scale campaigns based on their profitable volume capacity, not just their efficiency metrics.
Lesson 3: Seasonality requires dynamic budgets, not static ones. Customer behavior changes weekly, monthly, and seasonally. Fixed budget allocations miss these opportunities. Build systems that can shift spend based on performance patterns.
Lesson 4: Channel saturation is real and measurable. Every campaign type has a natural spending limit where performance starts degrading. Find these limits through testing, then respect them. Pushing beyond saturation points destroys profitability.
Lesson 5: Conversion delay affects budget allocation timing. Some channels generate immediate conversions, others plant seeds for future purchases. Factor conversion delays into your budget allocation decisions, especially for seasonal campaigns.
Lesson 6: Small budgets spread across many channels usually fail. It's better to fully fund 2-3 channels than to under-fund 5-6 channels. Most campaigns need minimum viable budgets to generate meaningful data and achieve stable performance.
Lesson 7: Your optimal allocation is unique to your business. Industry benchmarks and competitor insights can provide direction, but your final budget allocation should be based on your specific performance data, not external "best practices."
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups, focus budget allocation on:
Search campaigns for high-intent keywords around your solution category
Display campaigns for remarketing to trial users and demo requests
YouTube campaigns for explainer content and product demos
Test budget limits with micro-campaigns before scaling successful channels
For your Ecommerce store
For e-commerce stores, optimize budget allocation by:
Mapping product categories to their best-performing campaign types first
Allocating based on performance density rather than just ROAS
Building dynamic budget rules for seasonal and weekly patterns
Testing channels with small budgets before committing large allocations