Audience Segmentation Techniques

NEW targeting setup for 'Meta Sales Campaigns' ⚠️ The original audience types (prospective vs. retargeting) are now being removed and replaced by 'Custom Audiences'. 🧠 Here's what this means: Before this update, when creating a Sales campaign using 'Advantage+ catalog ads', you could navigate to the ad set to choose the audience type to target. It allowed you to select 'finding prospective customers' or to 'retarget ads to people who interacted with your products'. In the updated interface, Meta recommends creating and using 'Custom Audiences' for that same purpose. 💡 Here's how to create these 'Custom Audiences': Navigate to 'Audiences' in Meta Business Suite. Click the blue button to 'Create' a new audience and select 'Custom audience'. Now select 'Catalog' as the source for your custom audience. From here you can create custom audiences from people who: - Viewed products from your product set - Added products from your product set to cart - Purchased products from your product set Select different 'Product sets' to upsell and cross-sell products and use the 'Any' or 'All' criteria to define your final audience. Now you can select all custom audiences you've created to include or exclude at the ad set level of your Sales campaign. #MetaAds update spotted by Bram Van der Hallen 💙 Follow for more Meta Ads content.

746M → 737M → 749M. Linear TV reach is wobbling. And this is not a blip. 👉An 11% decline in TV ad volumes. 👉TV’s share of total ad spend is projected to fall from 21% to 15% by 2027. 👉Subscription growth is slowing. 👉Broadcasters under pressure. This isn’t a cyclical dip. It’s structural. Here’s what most people will say: “Digital is eating TV.” That’s lazy analysis. What’s actually happening is far more interesting. 1️⃣ The audience hasn’t vanished. It has reorganised. The same household that once consumed prime-time soap now consumes: • CTV • OTT subscriptions • Short video • Retail media placements • Influencer commerce Attention did not disappear. It fragmented, became portable, and became measurable at a user level. And that changes power. 2️⃣ Ad budgets follow measurability, not nostalgia. As a Data & Programmatic lead, I can tell you this clearly: CFOs don’t cut TV because they dislike it. They cut it because they cannot defend it in a performance review. When advertisers can see: 👉 deterministic signals 👉audience overlap 👉incrementality 👉outcome attribution They will allocate accordingly. The boardroom today asks:“Show me incremental sales lift.” Not: “Show me GRPs.” 3️⃣ The real story is not linear vs digital. 👉It is identity vs panels. 👉Panel-based measurement was sufficient in a broadcast monopoly era. 👉In a multi-device, logged-in, subscription economy? 👉Sample-based inference is colliding with deterministic data environments. And here’s my controversial take: 👉 Attention as a standalone metric will also struggle in this new order. Because visibility without outcome is theatre. 👉Impressions without identity are a probability. 👉And attention without commerce linkage will soon be questioned. 4️⃣ The next shift: Commerce-led media planning 👉Retail media is rising. 👉CTV is becoming addressable. 👉Telecom bundling is accelerating IPTV. 👉Distribution players are moving closer to identity graphs. 👉Broadcasters are moving closer to subscription stacks. The future TV ecosystem will be: • Logged-in • Addressable • Outcome-measured • Commerce-integrated The winners will not be those who defend legacy measurement. They will be those who integrate data across screens. 5️⃣ My POV This is not the death of TV. This is the death of anonymous TV. And that distinction matters. If broadcasters embrace clean rooms, privacy-first identity, and cross-screen measurement, they regain pricing power. If they cling to reach as the primary currency, they slowly lose relevance. 👉The market is not punishing TV. 👉It is punishing opacity. And as someone who has worked across programmatic, privacy, retail media and cross-platform measurement for over a decade, I can confidently say: 👉The next five years will not be about screen size. 👉They will be about data fidelity. 👉TV companies that behave as data companies will thrive. 👉The rest will become content suppliers inside someone else’s platform.

Different audiences need different openings. Here’s what works for each. There’s no one-size-fits-all opener. Your audience, executives, clients, and students, shape how you should begin. Here are 9 customized ways to hook attention based on who you’re talking to: 1. For Execs: Lead with a Promise    ➝ “In 7 minutes, you’ll see why this solution drives $3M in ROI.”     2. For Clients: Use a Vivid Visual    ➝ “Picture your Q3 roadmap—cut in half. That’s what this does.”     3. For Internal Teams: Tell a Story    ➝ “Two years ago, we faced the same challenge you’re in now…”     4. For Students: Ask a Provocative Question    ➝ “What if failing this test made you better at your job?”     5. For Pitches: Make a Bold Claim    ➝ “We’re not just solving X—we’re reshaping the category.”     6. For Workshops: Issue a Challenge    ➝ “Stand up if you’ve ever wanted to walk out of a training session.”     7. For Keynotes: Start with Silence    ➝ A pause before speaking builds gravity and presence.     8. For Tech Audiences: Hit with a Data Stat    ➝ “42% of teams still deploy weekly with manual QA…”     9. For Any Audience: Speak with Empathy    ➝ “I know how intimidating this can feel. I’ve been there too.”     The opener sets the emotional tone. Make it intentional. Who are you speaking to next? Pick one and practice. 📌 Save this cheat sheet 👤 Follow Jay Mount for communication systems that flex with context

Love this campaign by Stella. "Worth it" ✨ Playing off a familiar scene we all know. That claustrophobic bar. Enter "Claustrobar" You're crammed shoulder to shoulder... Getting bumped left and right. Then you get your first sip. Makes it all worth it. 👀 Or does it...? We're seeing the OPPOSITE trend for B2B events. Marketers want smaller more niche events. Think dinners with 15 to 25 people. ONLY the exact ICP they want. We just did our Q1 retro at The Alliance 🧵 NEW Q1 EVENT DATA FOR YOU: Dinners under 25 people drove 3.4 times higher average pipeline per attendee than 200+ person field events Sponsor satisfaction scores were 27 points higher for private dinners vs traditional happy hours Events with personalized pre invite cadences had a 35 percent average acceptance rate among ICP targets Renewal rates on sponsor programs anchored around curated dinners hit 82 percent, compared to 58 percent for "open bar" events Thats why we're doubling down on niche events. Dinners and intimate VIP exeperiences. Why they worked so well: Step 1: ICP first targeting Every attendee list starts with sponsor aligned ICP firmographic filters: Company size, role seniority, industry fit, existing buying intent. Step 2: Personalized outreach Dedicated in house teams send direct invites framed around relevance. We track weekly acceptance rates and optimize touchpoints if we fall below 30 percent. Step 3: Pre event intel Sponsors get attendee insights two weeks before the dinner. They know which companies and titles are coming so they can plan the content PRECISELY for that audience to make it hyper relevant. Step 4: Structured conversations No loud music. No random crowds. Strategic seating charts and guided conversation topics aligned to the topics attendees and sponsors care about. This makes the experiences great for BOTH the company sponsoring and the attendees. Ends in a win win for everyone. Example for you: At our Austin dinner for a sponsor in Jan - 17 handpicked senior leaders attended - 76 percent of attendees booked follow up demos within 21 days - The sponsor sourced $3.2 million in net new pipeline which was 3.1 times their original goal TLDR Invest in more dinners ✌️ 

When The Walt Disney Company and Formula 1 align, it’s not just a mascot moment — it’s a structural experiment in how fandoms, franchises, and nations converge around velocity, emotion, and IP. On Nov 8, the Fuel the Magic collaboration debuts in Las Vegas. But beneath the spectacle is a strategic test of global complementarity rarely seen at this scale. 🌎 A tale of two global empires — finally overlapping. F1 is dominant in Europe, Latin America, and the Gulf — markets driven by prestige, performance, and sponsorship capital. Disney has strong gravitas in North America and Asia — regions defined by storytelling, family travel, and retail IP ecosystems. 💡This isn’t about cross-promotion, it’s portfolio synergy across continents — a great example of how a single activation can rebalance regional brand asymmetry. 🧩 Complementarity as strategy, not coincidence. F1’s active fan base exceeds 800M, 43% under 35. Disney’s ecosystem touches 250M+ people each year, skewing family and young-adult. Disney’s retail footprint — spanning ~6,000 stores, 12 parks, and thousands of licensees worldwide — gives it a physical storytelling layer F1 has never owned. Meanwhile, F1’s experiential architecture — 24 races, 22 nations — gives Disney a calendarized event system to drop content, merch, and narratives into. 💡 In audience terms: F1’s adrenaline economy meets Disney’s imagination economy. The overlap creates a new commercial species — the “aspirational family” segment — consumers who want emotional storytelling and elite experiences. ⚙️ The multiplier effect. When F1 gains access to Disney’s footprint, its lifetime value per fan can rise sharply. When Disney gains entry to F1’s live-event cadence and affluent fan psychographics, its average revenue per household diversifies beyond films and parks. Add it up and the synergy math looks compelling: Incremental reach: +25–30% global overlap potential. Earned-media value: double-digit lift during Vegas week. Merch & licensing: $15M+ year-one upside, with long-tail park integration. 🏎️ Execution Will Be the Differentiator The blueprint is already visible — what will define success is precision in execution. From day one, this collab has a clear go-to-market strategy built for acceleration: a synergistic blend of PR, creator/talent activations, digital storytelling, and retail presence designed to convert global buzz into measurable traction. The partnership’s first year hinges on sequenced storytelling — leading with flagship categories, and experiential activations, while supporting with secondary verticals that extend reach and shelf life. In other words, this isn’t just launch hype — it’s a multi-channel flywheel engineered to turn one race week into a year-long franchise. Can't wait to see what Emily, Tasia, Liz, Joss, Joslyn, and team unveil in a few weeks. #Disney #Formula1 #Licensing Disney Experiences

Most creators have this backwards. They're chasing views and subscribers, hoping they'll magically become clients. But after analyzing over 10,000 videos across 150+ channels, I realized something profound: YouTube success is about viewer intent. Last month, I worked with a founder who had 87K subscribers but was only making $12K monthly. She was creating "viral content" instead of strategic content. When I shared my systems with her, everything shifted. Here's what I learned about building in public through strategic YouTube content: 1. Foundation First Build genuine authority by teaching what you know deeply. This creates the trust foundation that turns viewers into clients. When you share knowledge transparently, you become the obvious choice for implementation. Your expertise becomes undeniable when demonstrated consistently. People trust those who teach before they sell. 2. Solution Showcasing Demonstrate your expertise through real problem-solving. People hire those who prove their value transparently. Your public work becomes your most powerful sales tool without feeling salesy. Live problem-solving builds immediate credibility and rapport. Prospects see exactly what working with you would look like. 3. System Transparency Share your actual methodology and frameworks openly. Building in public creates magnetic attraction to your process. 4. Transformation Stories Show real before/after results from your work. Transparency about outcomes builds unshakeable credibility. Success stories create emotional connection and logical proof. Other founders see themselves in your client transformations. 5. Strategic Bridge Building Use clear CTAs that guide viewers toward next steps. Every piece of content should have a purpose beyond views. 6. Value Ladder Creation Design a clear path from free content to paid services. Building in public means showing the entire journey. 7. Intent-Based Content Target purchase-intent keywords that your ideal clients search for. Strategic transparency attracts ready-to-buy audiences. 8. The PVT Formula Address specific problems, validate your understanding, then reveal your transformation approach. This converts 273% better than standard tutorial content. This is about creating beautiful, systemized, and impactful brands together. When you build your expertise in public through strategic YouTube content, you create multiple conversion opportunities while establishing deep trust. We 10X'd her revenue within 90 days using fewer videos but with clear strategic intent behind each one. The future belongs to those brave enough to build their systems in public. It starts with sharing your methodology transparently. __ Enjoy this? ♻️ Repost it to your network and follow Matt Gray for more. Want help applying this in your business? DM me ‘Blueprint’ and let’s chat. Only for founders ready to scale.

One thing so many founders misunderstand: leading a company isn't about being right all the time. It's about knowing when you're wrong. This kind of brutal honesty about what’s working and what’s not is hard, but it's the only way forward. When I started Traceable by Harness, we got almost all of our assumptions wrong — and this was my third company. The What: We assumed companies wanted API protection. Turns out, they first needed API discovery. The Who: We targeted mid-size companies. But enterprise customers felt the real pain — and urgency. The How: We started with a PLG model. Turns out, classic enterprise sales was the right path. We pivoted hard. And often. Eventually, we were able to make Traceable a success and years later, many assumptions proved true — but there’s no prize for being “eventually right.” My advice to founders: don’t be afraid to pivot and don't cling onto assumptions you can't validate.

Fascinating paper from researchers at Pandora that explores how revenue can be optimized across subscriptions and advertising by personalizing ad load. While personalized pricing for digital products is a well-explored topic, the optimization of "implicit prices" like ad load is relatively fertile ground for research. This paper explores how a firm that monetizes with both ads and subscriptions can utilize ad load as a mechanism for maximizing revenue. The paper's authors conduct a large-scale field experiment in which 7MM Pandora users are separated into seven experimental conditions based on "pods" of ad load: FxL, or number of ad breaks per hour * number of ads per break (eg., 3x2, or 3 ad breaks per hour comprised of 2 ads each). Note that these levels of ad load only reflect *intended* ad load, and not realized, since Pandora can't control whether any given impression is filled. The paper's authors then implement a set of neural-network-based structural models, trained on the variation revealed through the experiment, to test how different users respond to the varying levels of ad load. This personalization policy: 1) estimates individual-level counterfactual subscription uplift 2) estimates individual-level ad-revenue uplift and 3) assigns heavier loads to users for whom the predicted subscription uplift outweighs the loss in ad-supported listening and ad revenue. The paper finds that, while increased ad load is associated with decreased ad-supported listening hours, the personalization strategy is associated with a 7% increase in subscription profits while ad revenue remains constant. This is associated with a roughly 2% decline in consumer welfare, disproportionately felt by users with a high willingness to pay. The authors determine that achieving this same revenue result without a personalization policy would require a roughly 22% increase in universal ad load. Note that the authors highlight one limitation of this approach: impressions remain roughly fixed in the immediate term, so personalizing ad load in this way merely shifts impressions across users, holding the total number of ads served fixed. Link to paper below

Mapping Mongabay’s readership in 2025 In 2025, Mongabay reached 111 million unique visitors through its websites, a 46% increase over the previous year. That figure captures direct readership only. It excludes circulation through social media, messaging apps, and republication by more than 100 partner outlets, all of which extend the audience well beyond what web analytics record. Even so, the geographic distribution of those readers offers a picture of where our journalism is being consumed and, relative to population size, where it carries particular weight. Mongabay does not orient its journalism toward maximizing general-audience traffic. Our reporting is designed to reach specific user groups rather than the widest possible readership. Asia and the Americas accounted for the largest shares of readership, each drawing just over 46M unique visitors. Asia’s total reflects strong readership in countries where environmental pressures are acute and where Mongabay publishes in multiple languages, including Indonesian, Hindi, and others. Given Asia’s population of more than four billion, the per capita reach is modest. Yet the scale still matters. Tens of millions of readers represents a meaningful channel into policy, research, and civil society debates. The Americas show a different profile. With far fewer people than Asia, the region’s comparable readership implies a higher per capita reach. North and South America together account for under one billion people. Drawing more than 46 million readers means Mongabay reached roughly one in every 20 residents through our websites alone. In countries such as the U.S., Brazil, Peru, and Colombia, the site functions as a regular source for practitioners, journalists, and decision-makers working on forests, oceans, and climate policy. Europe contributed just over 8.4M unique visitors. European readers tend to cluster in policy, finance, and advocacy communities, where information flows can translate quickly into regulatory or funding decisions. Africa recorded about 4.7M unique visitors. Relative to a population of roughly 1.4 billion, this is the lowest per capita reach of any region. It also highlights a gap. Many of the places most affected by biodiversity loss and climate impacts remain under-served by independent media. Mongabay’s recent expansion in Africa aims to address that imbalance over time. Oceania, including Australia, had the smallest total audience at 2.6M, but the highest per capita reach. With a population of roughly 45 million, this implies that around one in 17 people accessed Mongabay’s reporting directly in 2025. Traffic is not impact by itself. But geography matters. Where readers live shapes who uses the information and how it circulates. In 2025, Mongabay’s readership was global in scale, uneven in distribution, and, in several regions, concentrated among audiences positioned to act on the information they receive.

Here's how I use AI to bootstrap a Wardley Map with capabilities—or at least get to a solid starting point. The *hard* works starts after this! 1. It starts with a prompt. I frame capabilities using "the ability to [blank]" and use GPT to break them down into sub-capabilities in JSON. (I built a tiny front-end for this, but totally optional.) Example: "Buy lunch for team" → breaks down into planning, sourcing ingredients, managing preferences, etc. 2. I then pull these into Obsidian—my tool of choice—to visualize and view the relationships. 3. Next, I run a second prompt to place each capability on the Y-axis (how close it is to the customer), using roles as a proxy: ops leaders, org designers, engineers, infra teams, etc. This helps with vertical positioning in the value chain. Tip: I always ask the model to explain why it placed something a certain way. Helps with tuning and building trust in the output. 4. Then I add richness: I use another prompt to identify relationships between capabilities—either functional similarity or one enabling another. These are returned in structured JSON. Think: "Analyze data insights" ↔ "Trend analysis" → Similar. This helps expand our graph. 5. To tie it all together: I feed the data into NetworkX (Python) to analyze clusters—kind of like social network graph analysis. The result? Capabilities grouped by both level and cluster. 6. The final output is a canvas in Obsidian—grouped, leveled, and linked. It's a decent kickoff point. From here, I’ll nerd out and go deep on the space I'm exploring. This isn’t a polished map. It’s a starting point for thinking, not a final artifact. If you’re using LLMs for systems thinking or capability modeling, I’d love to hear your process too.