Ali Manning

Under pressure, B2B launch teams default to information density. Features, architecture, positioning language. It signals effort. It rarely signals value. The buyer notices. B2B buyers are evaluating risk, time, and trust. They are not looking for volume. They are looking for proof. When a launch leads with capability claims, it asks the buyer to do the translation work. Most will not. When a launch works, the product is not described first. It is demonstrated. A task is completed. A workflow is improved. Friction is removed. Competence is visible before innovation is claimed. The examples are everywhere once you look for them. A single prompt turning into a usable output in seconds. A problem shown clearly before the feature is named. Humor used to demonstrate usage rather than decorate the brand. Different creative expressions. The same discipline underneath them. Show the work before making the claim. A launch is a credibility test. Most teams treat it as a press event. The ones that work take a simpler path. They respect the buyer's time. They make the value visible. They trust the product to carry the argument. Show the work. Respect the buyer. The rest follows.

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Measurement doesn’t just influence decisions. It shapes how teams work together. Because the data you get from you marketplaces is siloed from your DTC data (and very different in terms of the data that’s available), teams organize themselves around these data siloes… because it’s easier to measure. The problem with this approach is that customer behavior isn’t siloed in this way… customers discover your brand on Youtube, research you on Reddit and buy on Amazon. If you’re not set up to measure the relationship between these marketing and sales channels, you’re making platform-centric rather than customer-centric decisions. More evidence that the path of least resistance is rarely the path of wisdom!

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I periodically stumble on Michael Kaminsky's musings, blogs and research, which are consistently well-grounded and seemingly really good science. When I read his most recent article in AdExchanger, his career-long data science track record came through loud and clear. Folks ... AI has tremendous potential. There is no doubt that the advancements over the last few years are nothing short of revolutionary. But buyer beware ... hundreds of "powered by AI" technology companies have popped into the market, but most remain heavy on promise and light on reality. Please don't take this as being a Debbie Downer. Quite the opposite. We are still in the nascent stages of AI in marketing and media. Some use cases are further along than others. But holistically, we still have a long way to go before many of us will clock measurable AI-powered improvements to our businesses. Now is the time for organizations to figure out which business functions will most benefit from AI -- for competitive differentiation, speed, cost reduction -- and gain a comfort level with AI's capabilities to achieve these goals. Start learning the tool-sets so that you can be ahead of the pack when AI shifts from experimental to real utility and value.

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There’s been a lot of talk in the DTC space about whether AI can actually drive real business outcomes. At The Whalies, it became real. For months, we’ve been sharing where Triple Whale is headed, how agents will transform decision-making, execution, and performance across ecomm teams. But this wasn’t more talk. What happened at The Whalies was the shift from concept to conviction. You could feel it. Last year, the conversations were about what’s possible. This year, they were about how fast can we go. Customers weren’t asking “When is this launching?” They were opening their laptops mid-event and asking, “Can I buy this right now?” One agency partner pulled me aside and said: “We’re running a hackathon on the hotel roof tonight to build SOPs with your agents.” That level of urgency, that clarity, wasn’t there even a few months ago. And when AJ kicked off a live agent run during the keynote, someone in the crowd muttered: “Holy $**t.” (In the best way possible.) That’s when it clicked. This isn’t hype. It’s impact. And while not everyone could be in the room, we’re doing everything we can to bottle that energy and bring it to you. Because this isn’t just momentum anymore. It’s a movement.

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Most measurement systems force trade-offs that are hard to accept. Attribution gives speed, but ignores the full funnel. MMM gives scope, but is too slow. Incrementality offers causality, but only in snapshots that mean channel comparability is lost. This trade-off leaves marketers without full-funnel, daily, ad-level inputs they need to plan and optimize spend with confidence and act on emerging opportunities… As brands head into BFCM, that gap matters more than ever. And it’s exactly why we built Fospha.

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One of the biggest breakthroughs building our agents was an insight hiding in plain sight... Agents perform better when given more agency. We're featured in Anthropic's customer stories on how Emergent applied this with Claude, giving our agents full autonomy to build and debug. The result? Production-grade full-stack apps shipped without human intervention. Read the full story: https://lnkd.in/gqPHRpSv #anthropic #claude #vibecoding

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Excited to see the IAB Tech Lab Agent Registry continuing to grow! 🚀 We’re expanding the registry with new agents from Amazon Ads, Burt Intelligence., Optable, Dstillery, and HyperMindZ.ai! The registry continues to help the advertising industry discover trusted capabilities powering agentic advertising workflows. We’ve also introduced a new deployment classification to make agents easier to understand and integrate: - Remote: Accessible to anyone via the internet, even if authentication is required. Best for hosted services, cloud APIs, and SaaS offerings. - Local: Must be downloaded and installed locally. Best for downloadable software, CLI tools, npm packages, or standalone applications. - Private: Deployed to a customer's private network or infrastructure. Best for enterprise on-premise installations or VPC deployments. This is another step toward building a transparent, discoverable ecosystem for advertising agents. Explore the registry and register your agents. It's free! https://lnkd.in/emEFdexH The agentic ecosystem is just getting started. 🤖 #AAMP #AgenticAdvertising #Agentic #AI #IABTechLab #Standards

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The shift from human-optimized to machine-interpreted marketing is already here — and it's moving fast. This one's worth your attention.

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Performance marketers are no longer treating CTV as a test budget. They’re expecting the same transparency and revenue accountability they get from search and social. Streaming has the reach. Now it has to prove the business impact. I share the key takeaways in the video below. #etailwest #etailPalmSprings #adtech #ConnectedTV #performancemarketing

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Why First-Party Data Integrity Matters in Demand Generation   There’s a narrative out there that data is “abundant and cheap,” so the size or integrity of a demand gen company’s database doesn’t matter.  I don’t see it that way — and most marketers I talk to don’t either.   Data only feels “abundant and cheap” when you’re renting it, buying it, or outsourcing it to a third-party with no real visibility into where it came from or how accurate it actually is.   And let’s be honest… that’s a very different model than actually owning your data.   When you build and maintain a true first-party database — verified, permissioned, and collected the right way — the quality isn’t comparable.  Not even in the same neighborhood.   First-party data isn’t a commodity.  It’s an asset.  And it’s a responsibility.   It takes transparency, accountability, and a willingness to own the entire process end to end.  Anyone can buy cheap records.  Not everyone is willing to build the database themselves and stand behind it.   Marketers today want transparency, accuracy, and real intent.  Quality matters more than ever.  And that’s why data integrity — and owning your own first-party data — actually does matter in demand gen.  A lot.

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We're heading to SampleCon in Atlanta this weekend. For a decade, the survey research industry has treated data quality as a marketing claim. "99.8% accuracy." "Best-in-class quality standards." Vague promises backed by nothing. Every panel company is scrambling to prove their data is real, and the reality is most can't. Most data quality solutions are rules engines dressed up as AI. They flag based on completion time or straight-lining. Bots learned to beat those checks two years ago. Proof of Human measures the process, not the output. Cognitive signatures that synthetic respondents can't replicate at scale. If you're at SampleCon and want to see what actual behavioral traces look like come find our team. The bar is changing. The companies that can prove their data is human will charge more, retain longer, and close faster.

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Excited to share that Aperiam has invested in Newton Research’s oversubscribed $9M Series A, alongside Greycroft, Bessemer Venture Partners, S4S Ventures, and LiveRamp Ventures. Newton is redefining what agentic AI can do for marketing analytics, enabling brands and agencies to: - Run customer 360 journey analysis across touchpoints - Segment audiences and build predictive models - Automate attribution, incrementality, and media mix modeling - Generate dynamic, natural language-driven reporting - Save up to 126 hours/month and achieve 10x analytics output Their pre-built “blueprints” let non-technical teams securely run advanced analytics on their own data with just a few clicks. John Hoctor, Matthew Emans, and Steven Bennett have successfully built and exited multiple companies. We love to back repeat founders on their most ambitious ideas, like Newton Research. Link to more in comments.

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This is the *CRAZIEST* stat from Advertising Week NYC’s Retail Media track today ~ Just 19% of 788(!) Retail Media Network respondents offer real-time, AI-driven targeting and optimization. That is 81% (!) of RMNs **NOT** targeting with readily available technology - this should be table stakes Particular Audience data demonstrates a 4x lift in fill rate using AI-driven targeting, with incremental value to organic conversion benchmarks and CTR engagement rates can be as much as 2.5x higher - and NO manual effort required by AdOps teams - it has got to be the investment 81% of RMNs make next. …and *NO*, most Retail Media platforms do not offer this despite the AI-washing of their marketing materials. RMN leaders need to learn about Collaborative Filtering, NLP, Computer Vision Vector-Based and Transformer powered Search and Recommendation technology so they can ask the right questions and rapidly level-up: or risk being left behind. As Nikhil Lai put it today “..the promise of Retail Media was personalization, without more PII. Really sophisticated personalization [machine learning] required” … “the problem is, the industry is porting display based infrastructure and transferring it to Retail Media” It is 2025 and this is totally unacceptable. - It is killing your customer experience, and - as competition between RMNs intensifies it risks killing your advertiser experience too. To quote Forrester and Koddi’s slide: “Publishers are progressing beyond the basics, but the maturity curve remains steep. Only 12% still rely on simple pre-set segments and 19% on basic demographic/behavioral tools, while the majority now enable deeper audience strategies: 23% integrate advertiser first-party data and 26% support custom audience creation with purchase, loyalty, or CRM data. Yet just 19% offer real-time, Al-driven targeting and optimization that will ultimately differentiate mature commerce media networks from those still scaling.” Thanks Nikhil and Nicholas Ward for the great research and presentation.

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Big News: Nowspeed + Longview Launch the GEO Authority Builder to Help Brands Win in AI Search The way we “search” is being completely rewritten. At Nowspeed, we’ve been watching closely as generative AI tools like ChatGPT, Perplexity, and Google’s AI Overviews shift how people discover and trust information. The traditional SEO playbook isn’t enough anymore. If your brand isn’t cited by these platforms, you’re invisible — even if your site ranks #1 on Google. That’s why I’m thrilled to announce the launch of the GEO Authority Builder, a powerful new solution we’ve created in partnership with Longview Strategies to help brands earn visibility and credibility in AI-driven search. What is GEO — Generative Engine Optimization? GEO is a new discipline focused on helping brands appear in generative search results, where AI summarizes and cites information directly. Unlike SEO, which focuses on ranking web pages, GEO helps your brand become a trusted source that AI platforms want to reference. And that’s where the GEO Authority Builder comes in. The GEO Authority Builder includes three core services: 1. GEO Visibility Audit – We assess how your brand shows up in AI tools like ChatGPT and Gemini. Are you being cited? Are competitors taking your space? 2. Source Influence Mapping – We identify which publishers, authors, and types of content AI platforms are citing — giving you a clear roadmap for increasing your visibility. 3. Credibility-Building Campaigns – Longview uses its earned media relationships and editorial strategy, combined with Nowspeed’s digital expertise, to build the authority signals generative engines rely on. Why now? As I said in the press release: “The game has changed. Brands that fail to adapt to AI search will disappear from the buyer journey.” We built the GEO Authority Builder to give companies a proactive path forward — one that aligns content, PR, and digital marketing for a new kind of visibility. What makes this different? This is more than an audit or a one-time SEO tweak. It’s a full visibility strategy built for AI-first discovery. You’ll get measurable insights into your current presence, and a real plan to improve it — including the articles, mentions, and sources that matter most. Ready to see where you stand? We’re offering a free GEO Visibility Audit to help you benchmark your AI presence and take your first step toward becoming an authority in generative search. 🔗 Request your audit and let’s build your authority — before your competitors do. Evan Zall #GEO #GenerativeSearch #AISEO #DigitalMarketing #ThoughtLeadership #Nowspeed #LongviewStrategies #AuthorityBuilder #MarketingInnovation #AIvisibility https://lnkd.in/eK7FSVgS

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“Good enough” data used to be enough for advertising performance. It isn’t anymore. As signals fragment and scrutiny increases, performance has to be explainable, defensible, and grounded in identity you can trust by being able to answer confidently, "Who is real?" "Who is reachable?" "Who is persistent?" "Who has changed?" "Who should not be acted on at all?" I break down what’s changing, why it matters, and what defensible performance actually looks like in practice here 👇 https://lnkd.in/g2RDbB4y

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AdExchanger covered the growing gap between retail media measurement and in-store behavioral reality. Adoption isn’t a feature problem it’s a clarity problem. Customers don’t experience “AI.” They experience confusion or simplicity. If in-store systems require explanation, adoption lags. If interaction is intuitive, behavior changes naturally. Technology works when it disappears into the workflow. https://lnkd.in/emph8_Un

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AI isn’t just knocking at the door, it’s rewriting the playbook. IAB's recent report says 50% of marketers are leaning into GenAI for targeting and content, with rollout plans across the board. But only a third have governance dialed in, while 70% have already hit snags - bias, off-brand slipups, all the friction that erodes trust. That’s a dangerous gap. Let’s get real about what holds brands back. Ethical risks, creative dilution, and 37% of consumers flat-out rejecting AI-crafted ads? That’s not a minor hiccup, it’s a business threat. If governance isn’t built into AI adoption from day one, every campaign risks feeding the churn cycle rather than driving retention. RocketSource’s frameworks nail two priorities: 1. Structure matters. Use a proven sequence (StoryVesting and Bowtie Funnel) to align AI automation with real human insight. Adopt AI in phases: build your data backbone, activate predictive analytics, deploy Agentic AI - with human validation at every loop. 2. Engineer trust. Don’t just talk responsibility - make it measurable. Quality audits, transparent outputs, and closed-loop feedback keep your message credible, compliant, and on-brand. Behavior tells the truth. Brands who orchestrate AI with governance, behavioral modeling, and empathy win more than attention - they build durable lifetime value. Retention isn’t luck. It’s engineered. Ready to transform your AI the right way? Tim Mitchell and let's do an AI maturity assessment for your brand. https://lnkd.in/g7UNB797

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