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KI-Systeme wie ChatGPT oder Perplexity verändern die B2B-Suche komplett. Wer auf Kundenfragen nicht die richtigen Antworten liefert, wird durch die KI nicht zitiert. Wir machen eure digitale Präsenz gemeinsam fit für die Zukunft und steigern eure Sichtbarkeit. Alles fällt leichter mit der Hilfe von Friends ❤️

Wer im B2B-Umfeld nicht sichtbar ist, findet in hart-umkämpften Märkten nicht statt. Wir verknüpfen strategische Markenführung mit wirkungsvoller Vertriebskommunikation. Das Ergebnis für euch? Echte Brand Awareness, spürbar mehr Leads und ein messbarer Beitrag zum Umsatz. Alles fällt leichter mit der Hilfe von Friends ❤️

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We’ve relaunched Fospha Measurement System, so brands can see everything, sell anywhere and move instantly. Each measurement method answers a different question. Attribution shows what moved this morning. Marketing Mix Modelling shows where next year’s budget should go. Incrementality Testing shows whether a big bet is working. The trouble starts when a team has to stitch those answers together. It takes longer, and the numbers don’t always agree. Fospha’s Measurement System puts them in one connected system, across every channel and every place a brand sells, online and offline. Our daily media mix attribution is for bottom up, tactical and granular optimization, the marketing mix model is for top down strategic decisions, and incrementality testing is there for point in time validation. Less time reconciling the numbers. More time acting on them. Speak to our team to see all three methods working in one view. Link in comments

Measure every channel the same way and budget calls get easier. Huel has been a Fospha partner for more than six years. When it started, each ad platform claimed credit for sales using its own rules. The team could calculate a blended customer acquisition cost (CAC), but not at channel level, so decisions came down to gut feel. What changed once Fospha was in place: - One consistent source of truth, built on clicks and impressions - CAC compared across every channel, like for like - A morning check of performance by channel, in one place In the Fospha Measurement System, Media Mix Attribution puts every channel, campaign and ad into one model, refreshed daily. If every platform in your plan reports a different number, speak to our team. Link in comments.

Brands don’t grow channel by channel or market by market. So why do we still measure them that way? Following a successful test phase, Nexxen has chosen Happydemics as their third-party brand lift partner for advertisers. Early results are promising: campaigns measured so far rank in the top 15% for brand consideration, the top 20% for purchase intent and the top 25% for ad recall against our benchmarks. For Nexxen advertisers, it means a clear view of how their campaigns shift perception, consideration and intent, not just who they reached. “Today, knowing whether a campaign was delivered to the right audience is table stakes for advertisers – we need to know how these campaigns are influencing audiences and driving outcomes,” said Alexandra Macon-Cooney, Senior Vice President, Partnerships and Data Strategy at Nexxen. Learn more: https://lnkd.in/e3Fmc9Ym

Aunque el contenido sea digital, detrás siempre hay una persona. La tecnología nos ayuda a crear más rápido, pero las ideas, el análisis y la intención siguen siendo humanas. En FlandeCoco creemos en lo digital, pero sobre todo en quienes le dan sentido. 👇 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗯𝘆 𝗵𝘂𝗺𝗮𝗻𝘀. https://lnkd.in/dNxNeM-q

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In 2027, DTC brands will keep the agencies whose ads keep scaling after every Meta update. And drop the rest. On 27 October we’re hosting a free live webinar where 4 operators who run ad accounts for DTC brands every day show what’s working right now. What you’ll take back to your clients: → Marc Wirbeleit, 12 years inside Meta: what Andromeda actually changed, and what to stop chasing → Sergio Zaragoza, co-founder of ViralClip: how to brief UGC creators so the ads scale past $1M in spend → Erwan Liébot 🧠, founder of OhMyGads: how to localize UGC ads for 5 markets, so you can take clients into new countries → Stefano Mazzei, co-owner of Cosma: how many creatives you need to find a winner, so you can plan creative volume and budget with your clients My co-founder Sebastian Novinsek hosts the event and moderates the live Q&A. And a big thank you to Luka Podobnik, who planned and organised the whole thing. 🎙️How to win in paid social as a DTC brand in 2027 📆 Tuesday, 27 October ⏰ 17:00 CET / 12 PM ET / 9 AM PT 💻 Live online, free If you run paid social for DTC clients, save your free seat here 👇

It takes at least 2 to 5 months (depending on the model) for published content or new mentions to affect the model’s parametric knowledge, whereas new content can be cited (via search) in days. * New models released by OpenAI, Anthropic, and Google are typically trained on data that is several months old. * The latest date when pages are included in the training set is called the knowledge cutoff. * We use the term knowledge cutoff gap to mean the amount of time between the knowledge cutoff and the release date. * The knowledge cutoff gap is the minimum time-to-impact for newly published content. The attached shows knowledge cutoff gaps for models released since 2022. Why is this important? We recommend complementing strategies based on citations with parametric knowledge optimization (PKO): * Most AEO strategies focus on citations and ignore the model’s parametric knowledge, the information stored in the model’s weights. * But not all responses use search, and even when they do, the model’s parametric knowledge can shape both its fan-out queries and how it uses search results to answer. * Therefore, we should also focus on PKO, which aims to improve how models describe and recommend a brand by influencing the training set. Recommendations: * Be aware of knowledge cutoffs and gaps. Do not expect impact from parametric knowledge optimization in two weeks when the knowledge cutoff gap is five months. * Do not expect impact in a specific model when changes were made after the knowledge cutoff for that model. * When new models are released, supplement standard prompt tracking with parametric knowledge visibility tracking. Use APIs with web search disabled to ensure responses are based only on parametric knowledge. Collect at least fifty responses per prompt. This process is only needed once per pinned model version, as the response distribution will not change as long as the prompts, system instructions, and sampling settings remain fixed. Paper link in the comments.

How Long Does It Take To Impact AEO? * I have previously said that while SEO takes time (3+ months usually), optimizing for AI can happen faster (days). This is partially true. * Many AI answers do not use search and do not have citations (10%-80% of answers have citations depending on how you decide which prompts to focus on). Answers without search and citations rely entirely on the underlying model's parametric knowledge. * According to our analysis of public disclosures from LLM companies, it takes at least 2 to 5 months for AEO initiatives to affect parametric knowledge. Why Does It Take So Long? * New models are released every few months. Further, new models have a "training data cutoff" which ranges from a few weeks to a few months old. So, a model released today isn't trained on data released yesterday. There is a time delay to train these new models. Why This Matters We recommend complementing strategies based on citations with parametric knowledge optimization (PKO): * Most AEO strategies focus on citations and ignore the model’s parametric knowledge, the information stored in the model’s weights. * But not all responses use search, and even when they do, the model’s parametric knowledge can shape both its fan-out queries and how it uses search results to answer. * Therefore, we should also focus on PKO, which aims to improve how models describe and recommend a brand by influencing the training set. Measurement * Rather than only measuring weekly and looking for incremental trends, also look for large discrete jumps among different model versions. * When you see a change in a new model, check our paper or the LLM to see the training data cutoff and only draw conclusions about changes you made prior to the training data cutoff. Anything after necessarily cannot have an effect until a newer model has it in its training data. * Use APIs with web search disabled to ensure responses are based only on parametric knowledge. Collect at least fifty responses per prompt. This process is needed only once per pinned model version, since the response distribution will not change as long as the prompts, system instructions, and sampling settings remain fixed. Question Has anyone experimented with ways to optimize the parametric knowledge? Paper link in the comments. + Gregory Druck Who is the primary author on this paper.

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Meta changed the rules. Again. 🤦♂️ To show DTC brands how to win anyway, we've organised a free live webinar with Influee on the 27th of October. 4 operators who run ad accounts every day will show you how to scale through every Meta change and make 2027 your most profitable year yet. Here's who's speaking and what they'll cover: → Marc Wirbeleit, 12 years as a creative strategist inside Meta: what Andromeda actually changed, and what to ignore → Sergio Zaragoza, co-founder of ViralClip: how to brief UGC creators to scale past $1M in ad spend → Erwan Liébot 🧠, founder of OhMyGads: how to localize UGC ads for 5 markets → Stefano Mazzei, co-owner of Cosma: how many creatives you need to find a winner in 2027 By the end, you'll know exactly what to change in your ads, briefs, and testing so 2027 becomes your best year yet, not another year of chasing Meta updates. 🎙️ How to win in paid social as a DTC brand in 2027 📅 Tuesday, 27 October ⏰ 17:00 CET / 12 PM ET / 9 AM PT 🎤 Moderated by Sebastian Novinsek, co-founder of Influee 💻 Live online, free Save your free seat here 👇 https://lnkd.in/ezTxKysP

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Ever had to tell an agency to cut the budget on a “winning” Meta ad—then pay them to find another one? Does the cycle look something like this? An agency finally finds a winner. Spend goes up, the ad stops making money, and the budget gets cut. So the hunt starts again—until the next winner hits the same ceiling. Then another agency promises it can finally get the business scaling. But if its answer is another round of tests and another “winning ad,” how is that fixing the problem? It’s the same cycle with a new retainer. At GrowRev, we’ve invested hundreds of thousands of dollars into solving this exact issue. We built an entire team of scaling strategists to tackle it. That work led to our proprietary Escape Velocity Framework. Our strategists use it to find what’s keeping the business stuck and build a plan for breaking through. It’s designed to give the business enough profitable momentum to get past that ceiling and keep growing. We’ve managed $400M+ in ad spend and generated $2B+ in client revenue, with work including campaigns for Tony Robbins and Dean Graziosi’s Mastermind.com. Click below, book an Escape Velocity call right now, and let’s talk about getting the next winner past the point where the last one broke.

Academy Sports + Outdoors didn’t pick a paid social partner on vibes. Meta’s marketing science team ran a 4-week Conversion Lift Study instead. Hunch Enhanced DPAs went in: every product image cut out by Hunch AI and rebuilt with Academy’s colors, price and logo. All 350K of them. The study only counted revenue the ads actually caused. Result: 2.3x incremental ROAS. Academy picked Hunch as its paid social partner.