Kevin Szypula

AIO Lawsuit from a big publisher -> Penske Media sues Google, says ~20% of Google search results linking to its sites now include AI Overviews, and affiliate link revenue fell 33%+ since late 2024 “Siphoning and discouraging user traffic to PMC’s and other publishers’ websites in this manner will have profoundly harmful effects on the overall quality and quantity of the information accessible on the internet,” the complaint alleges, using the acronym for Penske Media Corp." https://lnkd.in/emDqVEFs

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Claude + Facebook Ads MCP is absolutely WILD 🤯 This AI system turns Claude into your personal ads analyst using MCP integration. And generates full client reports in seconds. Perfect for agencies & ecomm operators who hate wrestling with Ads Manager. But instead of manually pulling data, building charts, and formatting reports... Just give Claude one natural language prompt and get a complete visual storytelling report with insights. Each report includes account performance, campaign breakdowns, demographics, and actionable recommendations. All with a single prompt. Here's what it does: → Connects directly to your Facebook Ads account → Pulls all performance data automatically → Calculates ROAS, CPA, conversion rates on demand → Creates interactive charts and visualizations → Generates beautiful reports → Builds everything from scratch in real-time Built with Claude MCP. Zero manual work. Want the complete MCP setup? Comment "MCP" + like and I'll send it over (must be connected so I can DM)

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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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Seismic shifts (part 2): what’s happening in agency world? Tough read for agency land. 15% cuts is punchy - but talking with industry friends, it could be choppier on the creative side. AI creative started at the “bottom”, but quality is improving fast. Forget the “can it win an Emmy?” debate- look at the sea of “OK” creative we all see. If AI can match that at a fraction of the cost and time - and spin up endless personalised versions and social cuts - that’s the direction of travel. Are we far away? Not from what I’m seeing. There are bright spots in the story. Creative agencies taking control of creator marketing is smart. If AI means near‑infinite content, then what’s possible and needed shifts significantly. People are still essential, but in different roles. On the media side – are we going to see a replay programmatic (automation = more people, just different)? Programmatic was a hot mess under the bonnet and needed bodies to sort it. If AI really collapses plan - traffic - optimise - report, this time looks different. And what will clients pay for when AI does a big % of the work? No surprise principal trading is hot. Bright spots? >Humans in the loop, focused on bigger thinking across a broader set of challenges that actually move the brands business forward. >AI generating and adapting content across those challenges in ways we didn’t think possible and driving real outcomes = more investment. Looks like a bumpy ride ahead. If you were placing bets for 2026/27, where are the winners and losers  - and why? https://lnkd.in/eb5s9bAw

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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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Google Ads makes it easy to launch a campaign...but far too easy to launch it wrong. In The Ward Group's latest blog post, we break down some of the most overlooked (yet costly) platform defaults hiding in plain sight. Read the article for a closer look at how to protect your budget - and your brand - from the silent inefficiencies of automated advertising.  👉 Read the full post here https://lnkd.in/gw5RUcjp #MediaBuying #DigitalStrategy #GoogleAds #MarketingROI #TheWardGroup

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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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P-Max doesn’t need better targeting. It needs better signals. In my conversation with Marc Jordan Waldeck, we dug into why P-Max can feel hit or miss for lead gen. The answer comes down to data volume and data quality. P-Max needs enough conversions in a short window to learn. The common benchmark is around 30 conversions in 30 days, although Marc has seen it work with as few as 10 to 15 when the signals are strong. The real difference maker is importing offline conversions from tools like HubSpot or Salesforce and telling P-Max to optimize only for those qualified outcomes. Without that, you’re asking the algorithm to guess. And guessing is expensive. Before you judge P-Max results, ask whether you’re feeding it the right data. #PerformanceMax #PaidSearch #LeadGeneration #B2BMarketing #Impactable Website LinkedIn Ads Agency: https://lnkd.in/guEafPKk B2B Strategies and Guides: https://lnkd.in/gB-WQ82f Impactable YouTube Channel: https://lnkd.in/emYVDn_T

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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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Mantis Analysis Reveals 56% of Oscars Content Blocked Due to Blunt Keyword Blocklists! At Mantis, we assessed more than 460,000 page views between Dec 28, 2024, and Jan 27, 2025, and found that a master keyword blocklist used by multiple ad agencies flagged 56% of the views as potentially problematic. That's a significant portion of the audience missed out on! With nearly half of consumers trusting the #Oscars as a key indicator of top films, celebrities and performances, brands are missing out on vital opportunities to engage with an audience that's actively tuned in during one of entertainment’s peak moments. It’s crucial for brands to think carefully about the technology they’re using to protect their advertising efforts. Mantis offers a far smarter, AI-powered solution that helps brands avoid these missed opportunities.   Read the full article by Kendra Barnett including my insights here ADWEEK https://lnkd.in/eVZRsbtF You can also catch this article in AdExchanger https://lnkd.in/et6eGzGn roundup/tuesday-2025-04-03/ and eMarketer https://lnkd.in/ew6C6XRz #KeywordBlocklist #BrandSafety #ContextualAdvertising #MantisResearch #Oscars2025

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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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Your users don’t remember ‘3-click automation.’ They remember logging off at 5 p.m. I often see marketers make the mistake of selling speed over substance: “Automate your workflow in 3 clicks” “Save 10 hours a week” “Cut reporting time by 80%” Sounds good. But it’s not what users remember. It’s not what they buy either. Imagine this: A CFO is using your platform to save 10 hours a week. And as a result… → They no longer have to chase 4 teams for updates → They makes decisions without waiting on ops → They finally shuts their laptop at 5 p.m - without guilt You didn’t just save them time. You gave them permission to log off. So instead of saying “Save 10 hours a week” You say: “Close your laptop at 5 p.m. without guilt”. “End your week without feeling behind”. Instead of: “Cut reporting time by 80%” You say: “Walk into every meeting already knowing the answer”, “No more last-minute scrambles. Your numbers are ready when you are” That’s what real product marketing looks like. Don’t just promise efficiency. Promise freedom.

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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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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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I’ve been thinking about why so many programmatic campaigns underperform and it often comes down to trade-offs that just aren’t talked about enough. - Want higher quality inventory? You'll pay higher CPMs. - Want massive scale? You'll likely compromise on quality in places. - Want rock-bottom costs? Forget premium placements. Most tools in our industry pretend these trade-offs don't exist. But just because the brief is to maintain quality AND reduce costs AND increase reach - doesn’t mean that’s how it actually works.. The best media strategies aren't about avoiding all trade-offs - they're more about making conscious ones that align with the overall campaign objectives. For example: - A luxury brand might happily trade scale for quality - A performance marketer might accept higher risk for better cost efficiency - A brand awareness campaign might prioritise reach over in-depth engagement The problem isn't that these trade-offs exist (because they always will!). The issue is actually how often we make them blindly, without any real data to help understand what we're actually trading off. That's why transparency in inventory intelligence matters. When you can see the quality, risk and opportunity signals of your inventory, then you can make more deliberate choices rather than hoping for the best.

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Tough truth: Channels with smaller ad budgets often get overlooked in MMMs. When a channel represents a small portion of the mix, brands don’t always prioritize collecting detailed data on it. Instead, it gets lumped into a broad “everything else” category with minimal granularity. When the data is aggregated, inconsistent, or incomplete, it creates real problems for MMM. Without granular, well-structured data, it’s tough to get a robust measurement. And when we can’t measure impact properly, it’s hard to make the case for optimizing or increasing investment in that channel. So how do we fix this? By improving MMM data feeds. If we look at how social media platforms evolved their measurement capabilities, it’s clear that better data leads to better outcomes. When Meta, TikTok, and Google started providing structured, MMM-ready data, their impact became much easier to measure. As a result, brands felt more confident investing in those platforms. The same needs to happen across all media channels. To ensure a media channel can be effectively measured in MMM, there are three key data standards that should be met: - Periodicity: Data should match the periodicity of MMM models, which typically require weekly or daily breakdowns rather than aggregated monthly figures. - Metrics: Spend alone isn’t enough. To truly understand a channel’s impact, we need exposure and engagement data - impressions, reach, frequency, and actions taken by users. - Dimensionality: The more information we have on targeting, formats, and execution strategy, the more accurate and meaningful the measurement will be. If media owners want to see their share of ad spend grow, they need to follow the same path: providing clean, structured data. Without this, they risk being deprioritized, no matter how valuable they may be in reality. I believe this shift is not just necessary but inevitable. As the industry moves toward more sophisticated, data-driven decision-making, media platforms will need to adapt to ensure they remain a measurable and strategic part of the marketing mix. What challenges have you faced in measuring different media channels? And what steps do you think need to happen next?

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