Native Advertising In Ecommerce

We deleted 80% of our client's Meta audiences. Sales went up. Not by a little. By 127% in 30 days. Here's the full story: This ecommerce brand was running: - 15 lookalike audiences - 12 interest-based audiences - 8 engagement audiences - 6 website custom audiences Total: 41 audiences Their problems: - Audience overlap - High CPMs: $76 average - Inconsistent ROAS - Ad fatigue within 5 days Our solution? Brutal audience cleanup. New Structure: 1. Prospecting Campaigns - Lookalike audience: 1% - 3% of purchasers - Broad targeting with winning interests (excluding 30-day purchasers) - Open broad targeting (excluding 30-day website visitors) Result: Continuously finding fresh audiences 2. Retargeting - Hot (7-day visitors) - Warm (45-day visitors) Total: 4-6 core audiences Technical changes we applied: - Consolidated ad sets from 22 ad sets to 6 - Increased daily budgets from $100 to $400 - Enabled campaign budget optimization 30-day results: - CPMs dropped to $38 - CTR increased to 4.8% - Purchase volume up 127% - ROAS from 2.2 to 3.8 Key learning: Meta's algorithm in 2025 performs better with fewer, stronger audience signals than multiple fragmented ones. More audiences ≠ Better targeting Better audiences = Better targeting #facebookads #digitalmarketing #ecommerce #advertising

Using Dynamic Creative Optimization (DCO) to Personalize Programmatic Ads: In programmatic advertising, standing out means delivering the right message to the right person at the right time. Dynamic Creative Optimization (DCO) makes this possible by tailoring ads in real time for different audience segments within a single campaign. Leveraging data and automation, DCO boosts relevance and engagement. Here’s how to use it effectively. Step 1: Define Your Segments Start by identifying audience segments based on first-party data—think demographics (age, gender), behavior (cart abandoners, frequent buyers), or context (location, device). For example, a retailer might target “urban millennials” versus “suburban parents.” Upload these segments to a DSP like Google DV360 or The Trade Desk, ensuring data is anonymized for privacy compliance. Step 2: Build Modular Creative Assets DCO thrives on flexibility. Create a library of interchangeable ad components—headlines, images, CTAs, and colors. For instance, pair a “20% Off” headline with a sneaker image for young shoppers or a “Free Shipping” offer with a stroller for parents. Use a DCO platform (e.g., Celtra or Ad-Lib.io) to assemble these into templates that adapt dynamically. Step 3: Set Up Decision Rules Link segments to creative variations via rules in your DCO tool. Example: If “user = cart abandoner,” show “Complete Your Purchase Now” with their abandoned item’s image. AI can refine these rules over time, learning which combos drive clicks or conversions, making the process smarter without manual tweaks. Step 4: Integrate with Programmatic Buying Sync your DCO setup with your DSP. As the platform bids on impressions, it pulls the tailored creative matched to the user’s segment in milliseconds. Test this on a small scale first—say, a single ad placement—to ensure rendering works across devices and channels like display or video. Step 5: Measure and Optimize Track performance by segment using metrics like CTR, conversion rate, or ROAS. If “urban millennials” respond better to bold visuals, double down there. Adjust underperforming variations—like swapping a weak CTA—based on real-time DSP analytics. The Payoff DCO transforms a single campaign into a personalized powerhouse, cutting creative waste and lifting engagement. A travel brand, for instance, could show beach ads to sun-seekers and ski deals to snow-lovers—all from one buy. In a crowded market, DCO’s precision is your competitive edge.

I just audited a $3M e-commerce company’s ad account. Found 26% of “conversions” in their NEW customer campaign came from existing customers - nearly $30,000 worth of budget competing against themselves. Also found their P-Max campaign spending almost $20,000 on products already running in shopping campaigns. Pure audience overlap making their algorithm stupid. This happens because most advertisers obsess over keywords and bidding strategies but completely miss the biggest lever for ad efficiency: ↳ Controlling WHO sees your ads. Here's the audience targeting framework that destroys keyword competition: Step 1: PREVENT OVERLAP Most campaigns let new customers, retargeting audiences, and existing buyers all trigger the same ad groups. Our fix: Apply your customer list to each campaign, then set bid adjustments down 90%. This prevents existing customers from spending in new customer campaigns. Step 2: SEPARATE ROAS TARGETS ▪ Existing Customers: 600% ROAS target Why: They've already converted. Extremely high likelihood to purchase again. ▪ New Visitors: 400% ROAS target Why: Unknown conversion potential requires tighter efficiency targets. ▪ Retargeting Audiences: 250-280% ROAS target Why: Retargeting generates partial conversions - worth accepting lower ROAS. Step 3: ENABLE ALL AUDIENCES This account has 700+ audience segments available. They're only using 26 of them - all set to observation mode - collecting data but making zero bid adjustments. Even at 95% impression share and 3.87x ROAS, they're leaving efficiency on the table. Our fix: Enable ALL audiences in observation mode and use the data to target valuable audience segments that have previously been overlooked. Step 4: PRECISE RETARGETING Add another precision layer with time-based retargeting: 7-day, 14-day, 30-day, and 90-day segments each deserve different bidding strategies based on recency patterns. Step 5: SCALE AD SPEND Properly segmented campaigns get MORE efficient as you scale budget, not less. When properly implemented, you should be able to add a zero to your daily budget without changing efficiency metrics. That's how you know the system operates at maximum market capacity. THE TAKEAWAY This account was already operating at a 7.5/10—better than 95% of what I audit. But precision targeting makes the difference between hitting a ceiling and breaking through it. Stop fighting the keyword auction. Control the audience auction instead.

Different customers need different messages, even under the same brand. A recent audit revealed that two major customer groups were receiving nearly identical messaging. Both groups purchased from the same company and shopped similar categories, but their motivations differed. One prioritized performance, pace, distance, and technical features, while the other valued comfort, support, stability, and everyday movement. Same brand. Different buying psychology. This shows up across almost every eCommerce business. Beginners need straightforward information and clear guidance, while experienced customers seek detailed specifications, comparisons, and greater control. Cold prospects need to understand the problem, the product, and the brand’s credibility. Product viewers may require proof, cart abandoners may need a specific objection addressed, and past customers may be ready for replenishment or an upsell. Even “retargeting” is too broad on its own. A website visitor who has never purchased should not receive the same message as a loyal customer placing their fifth order. This principle applies across products and channels. Consumers may focus on taste, results, and product integration into their routines, while retailers prioritize margin, category demand, stock availability, and shelf placement. Strong segmentation is less about slicing audiences into endless groups and more about understanding three things: Why are they buying? How much do they already know? What do they need next? The brand voice should stay recognizable. The message should meet the customer where they are.

STOP Using Broad Targeting on Meta Ads (If you want a higher ROAS) After auditing $100M+ in ad spend across premium 8-9 figure e-commerce brands, here's the framework that's actually driving profitable scale: The Cascade Targeting System Most brands get targeting backwards. They deploy mass market messaging that no one listens to. Instead, they need to speak to many niche groups—personally. Optimize for resonance before reach. Here's the 3-layer framework that's generating a 3X-10X MER: → Level 1: Market Segments (Highest Impact) → Level 2: Personas (Secondary Impact) → Level 3: Angles (Tactical Impact) The AG1 Example: Instead of "Get healthier today" mass-market messaging, here's how they stack segments: Level 1 | Market Segments: → Travelers → Athletes → Busy Parents Let's use the busy parents segment as an example. This may breakdown into multiple personas as follows. Level 2 | Busy Parents Personas: → The Nutrition Research-Oriented Busy Parent → The Convenience-Focused Busy Parent → The Wellness-Minded Busy Parent Now we'll create different angles for each persona, let's just use the first one here. Level 3 | Nutrition Research-Oriented Busy Parent Angles: → "Nutrition you can trust for your family" → "Simple ingredients, 3rd-party tested, pediatrician-approved" → "Efficacious absorption for all gut biomes" To scale this, we can just add more angles, personas or market segments. The Data That Changes Everything: Mass appeal creative: Hits 15% of audience at 25% message resonance Hyper-targeted creative: Hits 100% of audience at 85% message resonance The Scaling Hierarchy (By Account Impact) 1. New Market Segments → Unlock entirely new customer pools (highest ROI) 2. New Personas → Deeper penetration within existing segments 3. New Angles → Tactical optimization within proven personas (lowest ROI) Here's what most brands miss: The biggest impact on account performance doesn't come from better creative execution. It comes from strategic market expansion. Most premium e-commerce brands are sitting on 5-10 untapped market segments. They're optimizing the wrong variable. The breakthrough happens when you stop asking "How do we appeal to more people?" and start asking "Which specific group haven't we spoken to yet?" Each new market segment you unlock is a step-function increase in addressable audience. Each new persona is incremental growth within that segment. Each new angle is tactical optimization towards converting that persona. Stop trying to be everything to everyone. Start being everything to someone very specific. Then stack another segment. And another. That's how you build a $100M brand from targeted creative strategy.

While the "go broad" playbook works for many brands advertising on Meta, your competitors are quietly banking on these 4 targeting strategies that drive outsized returns. I was just chatting with Tanner Duncan, one of the sharpest minds (and best golfers 🏌️♂️) in the media buying space, to get his take on how brands can find an edge with audience targeting. Let's break it down 👇 1️⃣ Interest-Based Audiences - these connect with users whose interests align with your brand. These fall into 2 categories:  → Direct interests - target users who engage with content specifically related to your products. Example: As a beauty brand, you can target users interested in skincare, haircare, makeup tutorials, and beauty influencers. → Tangential interests - target users based on their lifestyle and broader preferences.  Example: If your brand’s target demo is affluent women in their early 30s, you might build an audience around premium fitness brands (Lulu, Alo, SoulCycle), high-end grocers (Erewhon), or luxury lifestyle content. We usually look for a mix of 5-15 relevant interests. 2️⃣ Lookalike Audiences (LALs) - target new customers who share similar characteristics with your existing customers - including purchasing behavior, demographics, and interests. Here's how they work: 1. Seed: Your original audience that serves as the foundation (e.g. your best customers) 2. LAL %: The size of your lookalike audience (smaller % = more similar to seed) Pro tip: Providing Meta with large, high-quality seed audiences gives the algorithm stronger signals to build higher-converting LALs. Some effective seed types to test: • Pixel-Based: People similar to ALL of your customers • Value-Based: People most similar to your highest-value customers • Proxima AI Audiences: Enriched by cross-store data and predictive models • Shopify List Exports: Segmented customer lists (e.g. AMEX cardholders) 3️⃣ Proxima AI Audiences If you’re looking for scale, Proxima unlocks a whole host of new LALs by leveraging billions of cross-store data points. So you can be more aggressive in customer acquisition without sacrificing profitability. If traditional seeds are like gas for your car, Proxima gives you rocket fuel. 4️⃣ Retargeting Audiences Retargeting remains a viable method for reconnecting with users who’ve already shown interest—whether they browsed your site, engaged with your emails, or previously purchased. Highly recommend grouping audiences by time windows, rather than over-segmenting (think: L365 Klaviyo, L30 Klaviyo engaged subscribers). Hope this was helpful! We did deeper dives into each audience type on our blog post. Check out our common campaign setup with budget examples, how we structure our LAL tests, and more here: https://lnkd.in/eubFeQ7x

Microsegmentation is where personalization meets performance. Microsegmentation on Amazon is not a nice-to-have thing anymore. It’s the whole game. If you’re still running one-size-fits-all ads hoping to catch everyone in one net… you’re missing the point. Because the way customers shop now is different. It’s personal. It’s specific. And it’s powered by intent. 🎯 One shopper might want "clean skincare for sensitive skin" 🎯 Another wants "trendy K-beauty finds under $25" 🎯 A third? “Natural sunscreen that doesn’t feel sticky on dark skin” Same category. Completely different searches, interests, and expectations. Enter: Microsegmentation In the video I just shared, I break down why microsegmentation matters more than ever on Amazon — especially when using DSP. When you break your audience into real intent-based segments and run tailored campaigns for each, your message hits harder. Because it actually matches how they think. No more guessing what will work for “women aged 25 to 45” You’re going after “women looking for travel-size clean SPF before their Mexico trip” And that changes everything. And it’s not just ads anymore Amazon is quietly building the infrastructure to connect these dots on their own. Remember what we talked about yesterday? That new AI feature called Interests is literally doing this in the background for customers — scanning for new products based on their hobbies, tastes, and prompts. So whether you use DSP or not, Amazon is starting to surface products by customer intent, not just by keywords. That means the way you segment your campaigns and craft your listings needs to level up too. Because microsegmentation is how you: ✔️ Show up to the right people ✔️ Speak their language ✔️ Maximize conversions without wasting ad spend ✔️ Future-proof your brand in a world of AI-curated discovery If Amazon is doing this for customers with tools like Interests, imagine how much more powerful it is when you do it with data, strategy, and creative that actually matches the person on the other side. And when you combine that with the right tools — from DSP to Amazon Interests — you don’t just sell more, you connect deeper. Because at the end of the day, relevance always wins. #AmazonSellers #Ecommerce #AmazonDSP #MarketingStrategy #Microsegmentation #PersonalizedAds #AmazonInterests #AIinCommerce #ConsumerIntent #MarketplaceMarketing #FBAgrowth