Sales & Conversion
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
Last year, I was managing Facebook ads for a B2C Shopify store, and I was doing everything "right" according to the playbooks. Detailed audience segments, lookalike audiences, interest-based targeting – the whole nine yards. I spent weeks crafting the perfect audience combinations, testing different demographics, and analyzing every possible targeting parameter.
The results? Mediocre at best. We were burning through budget testing different audience segments while our competitors seemed to be crushing it with what looked like... simpler setups?
That's when I discovered something that completely changed how I approach ecommerce advertising: targeting is dead, and creative testing is the new targeting. This isn't another "broad targeting" article – this is about fundamentally shifting how you think about reaching your customers in 2025.
Here's what you'll learn from my experience:
Why detailed targeting actually hurts your campaign performance
The simple framework that increased our ROAS from 2.5 to 8-9
How to systematically test creatives instead of audiences
The weekly creative testing rhythm that scales
When broad targeting fails (and what to do instead)
If you're still splitting audiences and wondering why your cost per acquisition keeps climbing, this playbook will completely change your approach to ecommerce marketing.
Strategy Shift
What every marketer thinks they know about targeting
Walk into any marketing conference or scroll through any advertising course, and you'll hear the same targeting gospel repeated over and over:
"Know your customer avatar" – Create detailed buyer personas with demographics, interests, and behaviors
"Layer your targeting" – Combine multiple interest and demographic filters for precision
"Test audience segments" – Run different ads to different customer groups
"Use lookalike audiences" – Let Facebook find people similar to your best customers
"Exclude competitors' audiences" – Avoid wasting spend on the wrong people
This advice made perfect sense in 2018. Facebook's targeting was incredibly granular, privacy regulations were looser, and the algorithm needed our help to find the right people. Marketers could target people who "traveled to Europe in the last 6 months and liked yoga brands."
But here's the uncomfortable truth: this approach is not only outdated – it's actively hurting your campaigns.
Why does this conventional wisdom persist? Because it gives marketers a sense of control. It feels strategic to say "we're targeting women aged 25-45 who are interested in sustainable fashion." It looks professional in client reports. It's easier to sell detailed targeting strategies than to admit the platform has evolved beyond our need to micromanage audiences.
The reality is that privacy changes (iOS 14.5, GDPR, etc.) have fundamentally broken the detailed targeting game, but most marketers haven't adapted their strategies to match the new reality.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started working with this B2C Shopify store, they were running the "sophisticated" targeting approach. Multiple campaigns for different customer segments: new customers vs. returning customers, age-based campaigns, interest-based audiences, and carefully crafted lookalikes.
The store sold fashion accessories with over 1,000 SKUs – think jewelry, handbags, and lifestyle products. They had a decent catalog but were struggling with a 2.5 ROAS on Facebook ads, which barely covered their margins. The team was convinced they had an audience problem.
I inherited campaigns that looked like this:
Campaign 1: Women 25-35, Fashion Interests
Campaign 2: Women 35-45, Luxury Brand Interests
Campaign 3: Lookalike 1% (Top 10% Purchasers)
Campaign 4: Retargeting Website Visitors
Each campaign was using the same creative assets – basically the same product photos and generic "shop now" messaging across all audiences. We were spending 70% of our time managing audience segments and 30% thinking about the actual ads people would see.
The fundamental problem? We were trying to change the rules of the platform instead of playing by them. Facebook's algorithm in 2025 is incredibly sophisticated at finding people who will convert – if you give it the right signals through your creative content.
But here's what really opened my eyes: when I looked at the attribution data, most conversions were being labeled as "direct traffic" or had murky attribution paths. The detailed targeting we were so proud of wasn't actually reaching the people who ended up buying. The real customer journey was much messier than our neat audience segments suggested.
Here's my playbook
What I ended up doing and the results.
After weeks of mediocre results, I completely flipped the script. Instead of fighting Facebook's algorithm with detailed targeting, I decided to work with it. Here's the exact framework I implemented:
The One Campaign Structure:
1 primary campaign
1 broad audience (basic demographics only: location, age range, gender if relevant)
Multiple ad sets with different creative angles
Weekly creative testing schedule
Step 1: Strip Down to Basics
I killed all the detailed targeting and started with just:
Location: France (their market)
Age: 25-55 (broad enough to let the algorithm learn)
Gender: All (let data decide, not assumptions)
Step 2: Creative-Based Audience Discovery
Instead of targeting different audiences, I created different creative approaches that would naturally attract different customer types:
Lifestyle-focused creatives (aspirational content)
Problem-solving creatives (practical benefits)
Social proof creatives (customer testimonials)
Product-focused creatives (features and quality)
Step 3: The Weekly Creative Rhythm
Every Monday, we launched 3 new creative variations. This wasn't random – I developed a systematic approach:
Week 1: Test different value propositions
Week 2: Test different visual styles
Week 3: Test different formats (video vs. carousel vs. single image)
Week 4: Scale winning concepts with variations
Step 4: Let Data Drive Audience Discovery
Here's the key insight: your creative IS your targeting. Different creatives naturally attract different customer segments without you having to manually define those segments. The algorithm learns who responds to what type of content and optimizes accordingly.
For example, our lifestyle-focused creatives attracted younger, trend-conscious buyers, while our quality-focused creatives attracted older, value-conscious customers. But we didn't have to manually target these groups – the creative content did the targeting for us.
Step 5: Attribution Reality Check
I stopped obsessing over Facebook's attribution reporting and started tracking actual business metrics: total revenue, customer acquisition cost across all channels, and lifetime value. This broader view revealed that our SEO efforts were driving significant traffic and conversions, but Facebook was claiming credit through murky attribution models.
Algorithm Partnership
Instead of fighting Facebook's machine learning, we partnered with it by feeding high-quality creative signals
Weekly Testing
Every Monday: 3 new creative angles, systematic testing of messaging, visuals, and formats
Creative Targeting
Different creative styles naturally attract different customer segments without manual audience definition
Business Metrics
Track total revenue and LTV across channels, not just Facebook's attribution claims
The results spoke for themselves. Within the first month of implementing this creative-first approach:
ROAS jumped from 2.5 to 8-9 – but here's the crucial context: this wasn't just Facebook performing better. Our SEO strategy was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. The real insight? We finally had a sustainable, multi-channel approach instead of relying on detailed targeting tricks.
Cost per acquisition dropped by 40% because we were no longer competing in overly-specific audience segments. Broad targeting meant lower competition and better ad delivery.
Creative fatigue became predictable. With our weekly testing rhythm, we could see performance patterns and refresh content before ads burned out. No more sudden campaign death spirals.
Time allocation shifted dramatically. Instead of spending hours tweaking audience parameters, we invested in creating compelling creative content. This had lasting value beyond just Facebook ads – great creative assets worked across other channels too.
The most surprising outcome? Customer quality improved. When we let the algorithm find people who genuinely resonated with our creative content, we attracted customers with higher lifetime value and lower return rates. The creative-first approach filtered for engaged, interested prospects naturally.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from moving away from detailed targeting:
Privacy killed detailed targeting effectiveness – iOS 14.5 and GDPR fundamentally broke the targeting game. Adapting to this reality is essential, not optional.
Creative quality beats audience precision – A compelling ad shown to a broad audience outperforms a mediocre ad shown to a "perfect" audience every time.
Systematic creative testing is scalable – Having a weekly testing rhythm prevents campaign stagnation and keeps you ahead of creative fatigue.
Attribution is messier than reports suggest – Focus on business metrics across all channels rather than getting lost in platform-specific attribution claims.
Algorithm partnership beats algorithm fighting – Work with Facebook's machine learning by providing quality signals (good creative) rather than trying to outsmart it with manual targeting.
Broad targeting reduces competition – Detailed audiences are crowded. Broad targeting with great creative finds untapped pockets of potential customers.
Creative-first thinking improves all marketing – The discipline of creating compelling, varied content benefits your entire marketing strategy, not just paid ads.
This approach works best for ecommerce stores with strong visual products and sufficient budget for consistent creative production. It may not work for very niche B2B products or markets with extremely specific regulatory requirements.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, apply this framework by:
Testing different value proposition angles in creative
Using broad professional targeting instead of specific role-based segments
Creating demo videos that naturally filter for engaged prospects
For your Ecommerce store
For ecommerce stores, focus on:
Weekly creative testing with 3 new angles
Broad demographic targeting only
Different creative styles for different customer psychographics
Business-wide attribution tracking beyond Facebook