Kevin Brody

The easiest way to rank in 2025? Don’t launch a site. Launch a system. 1️⃣ Aged domain w/ backlink profile 2️⃣ Parasite pages for high comp KWs 3️⃣ Programmatic AI site for ultra longtails 4️⃣ CTR & user signal manipulation 5️⃣ High ticket lead monetization where possible You don’t need a blog. You need a f*cking pipeline!

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So what happens when Swivel has a seller agent, a publisher has an agent, and an ad platform has an agent? How does a buyer agent figure out the best agentic path? What can a dedicated stand alone seller agent product offer that a single seller agent or a platform seller agent cannot? Scale. Swivel Seller Agent works across ad platforms and sellers to catalog thousands (millions soon) of ad products. What does this mean for a buyer agent? One access point for agentic supply. One financial transaction. The AdCP spec accounts for this inevitability with a solution similar to ads.txt, AdAgents.json Publishers will have more than one agent approved to sell there inventory. So what does Swivel do for a seller in this context? Swivel helps publishers create and manage ad products, control pricing, and bring to market their highest performing most exclusive supply that is difficult or impossible to transact programmatically. And most importantly, stay current with a fast evolving and shifting agentic landscape.

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Agencies are underrated as hell with AI. 1) Agentic workflows will expand margins significantly 2) As work becomes more decentralized, demand for specialized agencies will grow 3) Agencies will be ground zero for internal AI tools that can ultimately become software products 4) Don’t have to raise money. Can cash flow from day 1 and control your destiny.

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“Best practices” are averages. They describe what worked somewhere else, for someone else, at a different price point, with a different audience. Your customers don’t care about best practices. They care about clarity. CRO replaces borrowed advice with first-party truth.

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Across our entire portfolio, every client has increased investment with us YoY. Why? Performance. For one client, leads are up 50% while CPL is down 20% For another, total revenue is up 178% And we do this while maintaining privacy compliance. Privacy-first marketing doesn’t have to mean loss of performance.

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Different retailers, different on-site ad tactics. 12 US RMNs compared 👀 Where do retailers place Sponsored Products on search and category pages? We analyzed how Sponsored Products appear across 12 major US retail media networks, looking at placements at the top of the page, mid-page, and bottom of the page. Each RMN structures its site differently and activates ads in different locations. These choices directly affect how much inventory is available for ads versus organic products and how seamless the user experience feels. 👉 Swipe through the carousel to see the full breakdown by retailer. 𝐀 𝐟𝐞𝐰 𝐭𝐚𝐤𝐞 𝐚𝐰𝐚𝐲'𝐬 𝐟𝐨𝐫 𝐑𝐞𝐭𝐚𝐢𝐥𝐞𝐫𝐬: RMNs using modern ad-serving technology, like Pentaleap, can unlock significantly more inventory while still protecting a clean, high-quality UX. These new capabilities raise two important questions. 1️⃣ How do RMNs generate enough advertiser demand to fill this expanded inventory? 2️⃣ And how do they attract real media budgets instead of relying primarily on trade spend? The most promising answer to both is 𝐑𝐓𝐁 (𝐟𝐨𝐫 𝐨𝐧-𝐬𝐢𝐭𝐞 𝐒𝐩𝐨𝐧𝐬𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬). Retailers are beginning to open up their walled gardens and meet brands on the ad platforms they already use. This makes retail media easier to buy, easier to scale, and better aligned with how brand budgets are actually allocated. Amazon, Google, Microsoft, The Trade Desk, Teads, etc. — sooner or later, RMNs will tap into media budgets from these platforms, unlocking incremental dollars beyond direct sales. 💬 What do you think is the most effective way for RMNs to escape the trade trap and capture true media budgets? LMK in the comments.

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People new to MMM: don't make the same mistakes you made with MTA. • MMM: marketing mix modeling • MTA: multi-touch attribution I've been in this industry a long time, and have helped to build out custom multi-touch attribution systems using fingerprinting, DMPs, clickstream parameters, IP matching, and all of the 1PD/3PD bells and whistles you can think. Most brands never got that advanced, instead they just: • Bought a tool • Used Google Analytics • Trusted the tool and used it to make decisions The biggest mistake -- which I'm begging you not to repeat -- is the third bullet above. You can't just blindly trust ANY methodology. And I'm seeing it happening right now at fusepoint and beyond. The measurement gurus (myself included) can be very convincing, and people who aren't as close to this are just eager to finally "get some answers." And while MMM is undoubtedly a much better method than MTA, it isn't without its flaws. In general, you should have healthy suspicion of anything. I'll give you an example: • Many MMM use Bayesian time-series methods (we do too) • Bayesian allows you to inject some "opinions" and priors • Bad priors means a bad model output A highly simplified version of this might be something like: • Run an MMM for a brand with Meta, Google, TikTok • Put ROI "priors" (read: assumptions) for each at --- Meta: 1-2x --- Google: 2-3x --- TikTok: 3-4x • Compute the model (with these priors) The results will be heavily influenced by those ROI ranges that we put in from priors. This is a "good" thing if you have those priors calculated from recent incrementality experiments or a body of historic models, but if you're just putting them in cold it can really invalidate results. For this brand they might have completely different results in reality, even though the model is saying something like: • Meta ROI of 1.5x • Google ROI of 2.5x • TikTok ROI of 3.5x In this case the brand would likely conclude that they should divest from Meta and more heavily reallocate into TikTok. However if the model was allowed to run "cold" with few priors, it might have come back with totally different results. For six months maybe they continue to allocate budgets in this way, and they see that their performance isn't getting any better (or actually worse). So they decide to run some incrementality experiments and see: • Meta ROI is actually 4x • Google ROI is actually 1x • TikTok ROI is really only 2x Completely different from what the assumptions were input as priors to the model. Maybe those assumptions were based on a different brand or some other kind of "opinion" from whoever was running the model. Happens all the time. Takeaways: • All measurement methodologies have flaws • Humans have a tendency to overly trust these things • Develop a healthy skepticism and scientific approach • Keep validating what you're doing is showing up in the P&L Stay scientific out there.

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Yext just published "A statistical look at regional and industry variation in local SEO ranking factors". Really interesting, especially this graphic. Here are the quickest takeaways: All effects are weak. Every correlation is small (roughly −0.10 to +0.14), so treat these as gentle signals, not causes. Biggest positives, by industry: Hospitality: Has Business Hours (+0.14, strongest overall), Has Merchant Description (+0.11), Profile Completeness (+0.10), Rating (+0.09). Healthcare: Review Count (+0.12) and New Reviews/Month (+0.11) lead; several review/engagement metrics sit around +0.07 to +0.08. Retail: Review Count (+0.08) and New Reviews/Month (+0.07); smaller bumps from Total Attributes and Photo Count (~+0.05). Financial Services (FINS): Review Count (+0.08), New Reviews/Month and Rating (+0.06), plus modest lifts from Attributes/Photos (+0.05). Food & Dining: Very small signals across the board; nothing much above +0.04. Consistent positives across industries More reviews (total and recent) and higher ratings are positive everywhere. Owner review responses (year/month/week) are positive but small. Consistent negatives Slower average review response time is negative in every vertical (worst in Healthcare at about −0.07). Notable outliers In Hospitality, having a local landing page shows the strongest negative (≈−0.10), and photo count is slightly negative (≈−0.04) - unique compared to other industries. Popular Times (presence of that feature) is more helpful for Healthcare and Hospitality (~+0.07–0.08) than elsewhere. Practical gist Focus on review volume, recency, and ratings everywhere. Respond quickly to reviews (slow replies correlate with worse outcomes). Hospitality: make sure hours, descriptions, and profiles are complete; don’t rely on a local landing page to help visibility. Retail/FINS: keep attributes and photos filled out; keep reviews flowing. Food & Dining: expect weaker gains from these particular profile tweaks, other factors likely matter more. ---- What do you think? #localseo #localseotips #localsearchrankings

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🚀 Key Insights from 16,000+ Paid Search Campaigns from WordStream by LocaliQ (Apr ’24 – Mar ’25): CTR: 6.66% on average - but over half of industries saw a drop CPC: $5.26 on average - 87% of industries faced higher costs CVR: 7.52% on average - 65% of industries saw improvements CPL: $70.11- up 5% year-over-year 📊 By industry: Legal continues to be the priciest for leads ($131) Auto Repair remains the most efficient ($28) Takeaway: Costs are climbing, so you need to have smarter, more relevant campaigns paired with stronger landing pages to drive better results. Reach out to the Digital Strike - Targeted Marketing team if you are not getting the results you need! 🔗 Full report: https://lnkd.in/ejvjkGxy #PPC #PaidSearch #DigitalStrike #GoogleAds #CampaignOptimizations

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🚨 This is your sign to check GA4 for bot traffic. Recently, we noticed suspicious traffic coming from China and Singapore, despite these not being primary markets. What’s concerning is the pattern: -Small spikes starting in October -Gradual increase in November -Clear peak during December-January We’ve now seen this across dozens of e-commerce websites. If your Ads performance dropped for no obvious reason, the problem might not be your strategy, it might be bad data feeding your algorithms. Cheers to better data and better decisions this quarter. #googleads #ga4 #paidsearch

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Walmart just gave every seller a full-body scan for their listings. Not a vibe check. A funnel CT scan. They didn’t shout about it, but this week’s Seller Center update might be the most powerful one all year. Here’s why sellers are actually excited 👇 🧠 Search Funnel Ranks You now get percentile scores (0–100) for each SKU on: 🔍 Impressions 👀 Clicks 🛒 Add-to-Carts No more “low / medium / high” vibes. This tells you exactly where your listing is leaking. High impressions but terrible click rank? Your hero image or title needs work. ⚙️ AI-powered listing fixes Walmart also added generative copy suggestions that rewrite your bullets, titles, and long descriptions based on real top-performers. You can tweak or accept them with one click. 🤯 Bonus: it flags missing attributes like Fabric Type or Voltage based on what your top competitors include. 📊 All of this lives in Seller Center You’ll find it under: • Analytics → Insights → Search • Growth → Success Hub → Top items to optimise This is what modern marketplace tooling should look like. Smart. Fast. Specific. Walmart just made it easier to fix the SKUs that are dragging you down, without guessing, without spreadsheets, and without paying a consultant to tell you what’s already in the data. 💡 Smart sellers will now build SOPs around these ranks, not just reports. Let the funnel tuning begin.

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