How to Use Lookalike Audiences for Targeting

Building hyper-targeted prospect lists used to be a major challenge for me. I sifted through endless data, tried generic filters, and spent hours on manual searches—all with mediocre results. Then I developed a lookalike audience strategy—and it changed everything. Here's exactly how I do it: 1. Identify My Best Customers I start with my most successful and best performing clients. These are the businesses I want to replicate. 2. Grab Their Website URL I visit a couple websites and copy their URLs. This becomes the foundation for creating my Ideal Customer Profile (ICP). 3. Open PandaMatch I open PandaMatch 🐼, a tool designed to build detailed customer profiles based on existing clients. 4. Build My ICP with PandaMatch I paste the URL into PandaMatch. It analyzes the site and generates a comprehensive ICP—covering industry specifics, company size, and other key attributes. 5. Export and Find Similar Leads in Apollo.io I click "Export" and select "Find Leads in Apollo." This moves my ICP data into Apollo, where I can search for companies that match my ideal criteria. 6. Refine the Search with ChatGPT To zero in on decision-makers, I open ChatGPT and input something like: "My ICP is Law Firms with 2 to 30 employees located in the US. I'm selling a bookkeeping service. Give me 25 job titles of the best buyers within a law firm, such as owner, CEO, partner, etc. Please separate each title with a comma." ChatGPT provides a list of job titles most likely to be interested in my service. 7. Target Specific Job Titles in Apollo I copy the list of titles from ChatGPT and paste them into the "Job Title" filter in Apollo. This ensures I'm not missing anyone. And that's how I use lookalike audience to build hyper-targeted prospect lists. ✌ Hope this helps

Lookalike audiences are one of the most reliable ways to scale outbound without starting from scratch. If you’ve already closed a customer and they’re getting results, your next move should be: Who else looks just like them? At The Kiln, we’ve used Ocean.io for years to build high-signal outbound lists using this exact model. They just rolled out a major update—and it’s made a strong tool even stronger. 1. New lookalike algorithm List quality is up. Fewer false positives. Better alignment on industry, size, and intent signals. 2. Prospect-level lookalikes (here’s how we’re using it) You can now find people who resemble your best buyers—by title, function, and company type. Here’s how we’re using it: 1. Start with a high-performing customer 2. Pull the key contact (ex: “Head of Revenue Ops at a 200-person fintech”) 3. Use Ocean to find 50+ people with similar titles at similar companies 4. Run targeted outbound that speaks to their shared KPIs and pain points This turns one great persona into a repeatable outbound segment in under 10 minutes. It’s a fast way to scale without sacrificing relevance. We’ve already worked this into our outbound systems. If you build outbound lists , this is worth testing, kudos to the Ocean.io team!

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