Ginny Marvin

Super insightful summary of the Google documentation leak around features used in its ranking algorithm. Thanks Michael King for the great deep dive. I continue to be an advocate for observing what actually has effects for your page in SEO over what Google sais and claims. This leak is another confirmation that blindly trusting Googles statements, e.g they are not using click data, is likely not the best idea. For all SEOs, PMs or entrepreneurs responsible for the organic rankings and traffic of their product this is a really worthwhile deep dive. As always in SEO: it doesn’t get as precise to tell you what exactly you need to do. But it’s important to know the variables and features that are important. But in the end one thing that has been true in SEO for years: It always boils down to build a great and engaging product with valuable and engaging content and promote yourself well enough so that you earn qualitative links to your page and content.

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Big Internet companies are making suboptimal decisions to push market valuation. We see more and more ads in search results, in news feeds, everywhere, and not enough care for the people who are actually creating content. Ahrefs intends to stay independent and focus on helping people to make useful content and to make it more accessible, with the ultimate goal of building a search engine that shares profit with content creators. https://ahrefs.com/ Ahrefs #SEO #BusinessStrategy #Marketing

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This is a very important read: Google quietly testing "Message Multiple" through LSAs and offering zero transparency to advertisers. This means, while you may think you are paying for a phone call from a prospect, you really are just one of four competitors getting a message.... and you have NO IDEA how much each of those different leads costs you. If you are an LSA advertiser, this is a must read (and also... track your economics for LSAs very very carefully.

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The current state of marketing attribution just isn’t working—we really need a new consensus around how we measure marketing effectiveness in B2B. I see two main issues: 1) Touchpoint-based attribution places a straightjacket on marketers, decreasing the quality of marketing 2) Multi-touch models produce misleading results and are used to claim credit, not improve performance Let’s examine: 𝗛𝗼𝘄 𝗠𝗧𝗔 𝗟𝗶𝗺𝗶𝘁𝘀 𝗚𝗼𝗼𝗱 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 For most companies, measurement is the Overton window of marketing tactics. It’s the yardstick by which every activity is judged and the gatekeeper of what strategies are considered viable.  If you must adhere rigidly to a touch-based attribution framework, then many marketing activities become difficult to justify • Un-gating your content • Podcasts / videos • Non-direct response channels • Mass advertising • Brand investments Unless you have a CEO who simply believes in the importance of these things and is willing to invest without hard numbers—you’re out of luck. As a result, many marketers actively shy away from hard-to-measure activities, opting to limit creativity for the sake of measurement. That’s a loss for everyone. 𝗠𝗧𝗔 𝗜𝘀 𝗠𝗶𝘀𝗹𝗲𝗮𝗱𝗶𝗻𝗴 The more you examine the premises of MTA, the harder they are to defend: • How can dividing up credit in a zero-sum game be accurate when so many touchpoints aren’t tracked or included? • How can we assume causality just because a purchase happened after some marketing touchpoint? • How can we trust attribution credit when models are based on arbitrary weightings, and a shift in model can dramatically change the outcomes? None of these practices would pass muster in any other discipline that works deeply with data and statistics. I don’t argue against the benefit of tracking what we can of the customer journey. But the illusion of precision offered by multi-touch attribution models is really problematic. 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼 𝘄𝗲 𝗴𝗼 𝗳𝗿𝗼𝗺 𝗵𝗲𝗿𝗲❓ It’s an absurd situation to be in, and millions of dollars in marketing budget are on the line. I think we urgently need a better understanding of how to measure marketing effectiveness, with methods that are statistically defensible.   I chatted recently with Pranav Piyush, founder of Paramark (a marketing analytics platform), and our chat on incrementality testing blew my mind. How do we tell if marketing is actually causal? The same way we measure this in “hard” disciplines like medicine: randomized controlled trials. He argues that a truly controlled experiment is the only way we’ll know the impact of a specific activity with certainty—AND you don’t need any new software to do it. Is this THE answer to our measurement problems? I don’t know. But I think these are the conversations we need to have to build a new consensus on measurement. Check out RevOps FM wherever you get your podcasts to hear the full chat where we explore this in a lot of depth. 

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Most ecoms overutilize Meta & neglect Google hardcore—a huge mistake. Here’s the 3-part, modern playbook top brands run: This isn’t anecdotal… - Based on aggressive ad testing - Across both Meta & Google - For 10+ high-growth brands - All between $5M-$75M in rev Here it is: 1. TOFU PMAX (Prospecting) - Non-brand, new customers only - Single asset group * Later: Test separate PMAX - For high margin products, cats, etc. * Large accounts: Test Demand Gen (needs time) - To feed PMAX high intent, cold traffic - Requires great video; arbitrage from Meta - Test all types: statics, videos, & feed - Run Lookalikes (only campaign that has) 2. MOFU Standard Shopping - For manual bidding - So can control “branded ad” cost - 2nd PMAX gatekeeper to funnel off brand terms * Later: Harvest keywords - To test Generic PPC * Large accounts: Test Display Remarketing - E.g., YouTube - Audience = visitors (non-purchased) 3. BOFU Exact Match Brand PPC - Target Impression Share (95%+) - Should have massive ROAS - 1st PMAX gatekeeper to funnel off brand terms Stop sleeping on Google. Diversify and get all your eggs out of Meta’s basket 👊

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4 Comments

This Is The Change That Is Needed Google's algorithms are literally created to provide a good user experience and is evaluated to that standard. From Google's perspective the algorithm is working if user satisfaction metrics say it's working, wholly indifferent of the publisher's viewpoint as search traffic diminishes with each new Google Search Feature. The disconnect in how Google measures success and the indifference to publishers is built into Google Search. It starts with Google's indifferent mission statement to make information "universally accessible and useful" and ends with the rollout of an algorithm that is 100% indifferent to how publishers experience it. https://lnkd.in/g3FwSGHP

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I like the perspective on spammers not advertising. It's a unique take that sheds light on how things work. #interestinginsight In summary, if you have unique data on relevant statistics that could be potentially interesting for your target audience, you could employ the method Gael and Mark are discussing. If you currently do not have this data, you may want to consider using this method at a later time and begin collecting unique data that would be of interest.

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With Generative AI, do companies really need to hire content marketing agencies anymore? We surveyed 600+ marketers in partnership with a few Databox research partners, including AI copilot software, Jasper. 301 of the respondents who were not agencies answered the question, "In your own opinion or experience, which of your outsourced sales & marketing efforts can be replaced by generative AI?" 212 (or 70%) responded "written content." If you're a content marketing agency & that doesn't scare you, I'm hesitant to tell you that only 45 (or 15%) selected, "None of my outsourced sales & marketing efforts can be replaced by Generative AI" In the chart below, you can see the long list of things they have brought (or are planning to bring) in-house. When we survey people, we ask open-ended questions to gather quotes that add context to the data we're collecting. After reading through many answers, it's obvious that companies are already saving ⏰ & 💰 by leveraging AI in their content production processes. Here's examples... "Generative AI has saved me hours when it comes to enhancing content for our LinkedIn Newsletter. I am able to spend more time in the R&D phase of the process, which is a critical component of providing relevant and newsworthy material for our subscribers." - Amelia Cristiani, Trading Heritage International, LLC "We recently conducted an email marketing campaign where between 75-90% of the written content was generated by AI & not only saved a substantial amount of time with the copywriting process, but also saw some of the highest conversion rates we've ever achieved." Jake Talhami, TruVersity "We're a mom & pop shop so our resources are very tightly constrained. We do not have a customer relations team or a content creation team, so anywhere we can save time by having generative AI provide us templates for blog posts, email marketing, or even search marketing headlines has really allowed us to survive & thrive. " - Mike Baron, Marley & Moose "We save many hours on generating relevant content using generative AI as a starting point." - Michael Smart, Egress Solutions, Inc. "We use Synthesia, Jasper, ChatGPT and others for content creation, which has been a huge time saver, but we do spend time on accuracy." - Shelley Maberry, Maberry Consulting & Evaluation Services LLC If I were running a marketing agency where "content creation" is a big part of my billables, I'd be shi!!ing myself right now. PS. 20+ of us will be reviewing and discussing the rest of this survey data live this Friday, June 21, 11AM EST. Connect with Fedor Šimić or Nikos Ntirlis for an invite. All are welcome. Be prepared to share your relevant experience in breakout rooms. PPS. You can get immediate access to the whole data set by contributing to the survey: https://lnkd.in/enDG5dk7

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The only "threat" curation plays to DSPs is it makes it much easier for clients to pick up and move to a new DSP. Tying your audience targeting strategy into a bundled deal makes ad buyers more "portable".

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I've been asked the same questions frequently lately regarding labelizer tools, segmentation scripts, and the Shopping Booster. Specifically about how to handle data post-sales period, when the data no longer reflects typical performance. 🛒📊 During sales periods, conversion rates, order values, and purchase intent are generally higher, while profit margins may be lower. 📈 This inflates product performance data, and when advanced campaign setups rely on past data to move products between performance buckets, it can become problematic. Historical data that doesn’t reflect future performance can lead to overspending on products that performed well on sale but may underperform afterward. Google offers a fantastic tool called Exclusion Period 🛠️, which helps adjust its algorithm post sale periods. However, this feature only impacts the algorithm itself, leaving reporting in Google Ads unchanged. Since tools like labelizer scripts and ProfitMetrics Shopping Booster base their product movement rules on historical reporting data from the API, that data still reflects inflated metrics. So, the question is: Should we disable these tools and scripts after sales end, then reactivate them with a shorter lookback window for performance data? The challenge is that many products won’t have enough data with a shorter lookback window. One workaround is to download custom labels for products before the sales period begins. Afterward, you could pause the tools/scripts, then re-upload those labels to restore pre-sales data. But let’s be honest, this can be time-consuming. ✨ That’s why we’ve introduced the Exclusion Period feature directly into Shopping Booster! ✨ This feature allows you to exclude specific sales periods directly in the settings: - With a 90-day lookback window, segmentation rules will consider 90 days of data while excluding your sales periods, such as Black Week (November 24–30). This ensures data reflects typical product performance. - You can also schedule exclusions! For example, you can set the exclusion period to activate on December 1, allowing you to continue segmentation during Black Week and seamlessly normalize your data afterward. 📅✅ Used alongside Google’s native Exclusion Period, this feature gives you better control over both algorithms and reporting while correcting for periods of inflated metrics. 💡 I'd love to hear your thoughts, how do you currently handle this challenge in your setup with labeling tools? 🤝

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Good points here. (A "gatekeeper" company is going to get a lot of unwanted attention if they make it really easy to turn off every competitor's cross-context user tracking while leaving their own cross-context user tracking turned on. Whatever form the cookie preference ends up taking, it's going to also have to control Google tracking that isn't technically cookie-based but would otherwise be unfairly/illegally advantaged)

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Younger Americans are starting to use generative AI in place of search engines, like the traditional Google experience. Most trusting the results. Consumers continue to gravitate to AI tools. Note, this was before Google released Gemini fully to the current search experience, it is obviously growing. When mobile started to take hold as the NEXT platform, builders focused on responsive design for the physical device. We wanted experiences optimized for each platform - mobile, PC, or Tablet. Will generative AI represent responsive design by way of the software experience? Creating personalization truly at scale for every single interaction digitally... by way of context from one search to the next. This fundamentally changes the keyword strategies for SEO and SEM marketers... how do you model conversation? We provided the whole deck in the link below. - Thanks to Andrew Melnizek, Helen Havlak, Jacob Kastrenakes, Kara Verlaney - Design from the awesome Diana Y. - Research and thought leadership from Sebastian Fernandez at The Circus

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This is not an attack on agencies, but... I often hear CMOs and CEOs saying: "If we want to scale this channel, let's hire an agency." Let me be clear: this is (often) not the best way to scale any channel. Here's why: 1. Resource allocation While agencies may look experienced and know what they're doing, you'll rarely have their best talent managing your account. The reality? You often get junior profiles learning on your budget. 2. Time investment Because of agencies' business model, they will never invest as much time as you want in your account. Think about it: is 2 to 4 days of work per month enough for a core marketing channel? 3. Strategic alignment After working with numerous agencies, I can tell you that the best and only way to scale a channel you're serious about is to run it yourself. The solution? → Hire someone who is a field master and give them full ownership → If you're early stage and can't afford a specialist yet, working with an agency can be a temporary solution → But once you find traction, it's time to bring it in-house 🔍 How to make the right decision: Agency vs In-house When to consider an agency: - Early-stage with limited budget - Testing new channels before committing - Non-core marketing activities - Need for specific expertise on short-term projects - Geographic expansion requiring local knowledge When to go in-house: - Core marketing channels - Need for deep product/market knowledge - Long-term strategic importance - Budget allows for dedicated expertise - Need for rapid iterations and testing 💡 Pro tip: Before making your decision, calculate the real cost of both options: Agency costs: - Monthly retainer - Additional fees - Management overhead - Knowledge transfer costs - Platform access fees In-house costs: - Salary & benefits - Tools & training - Recruitment costs - Management time - Team integration 🤔 Your turn to share! I'm curious about your experiences:  - Have you successfully transitioned from agency to in-house? - What challenges did you face? - For agency leaders reading this - what's your perspective? Let's make this discussion valuable for everyone - share your insights in the comments below!

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One pattern I've noticed is that many brands make the same mistakes when trying to find the right influencer partners.  ​ They focus on the wrong metrics. They fail to do their due diligence They approach influencer relationships as transactional rather than collaborative. ​ These missteps can lead to wasted budget, misaligned content, and lackluster ROI.  ​ But the good news is, they're all avoidable. ​ Based on my experience, here are the top 5 mistakes brands make when searching for Instagram influencers - and what to do instead: 👇 

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Ready to enhance your privacy-first advertising strategy? Seedtag Academy is excited to introduce ‘Privacy-First Advertising Pro.’ This new certification brings you the essential knowledge and skills to excel in privacy-first advertising. Discover everything to implement effective strategies that prioritize user privacy, including: 📈 The evolution of privacy in advertising 👨‍💻 How user data is collected and implications for advertisers 🔐 Key elements for crafting privacy-first campaigns 🚀 Emerging trends that are shaping the future Get certified for free in less than an hour: https://lnkd.in/e2YS_V4M #SeedtagAcademy #Certification #PrivacyFirst #DigitalAdvertising

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Focus on Local? I'd love to say this is some super-sophisticated exploit, but it's not. You can literally just move someone's pin and their local rankings tank. Wow -> Google Maps exploit lets you move your competitors map pin far away, resulting in their local rankings drops & even the business being suspended (Note, it's been an issue for months)... via Barry Schwartz Coverage by Joy Hawkins and Darren Shaw #google #seo #local

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CallRail's James O'Neal and Mockingbird Marketing's Robert Williams get into Dual Source Attribution - what it is, how to do it and why it is so important. In a much more, multi-channel world, the simplistic direct response models of the past don't (always) correctly showcase how your marketing channels are working together.

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buying search ads on the competitor's brand name is an interesting real-world example of an iterated prisoner's dilemma game. Both companies are better off when neither one spends ad money running search ads on the other's brand name, and I have seen the cooperate/cooperate state emerge in other software categories (I doubt competitors actually communicated about it, they could have reached cooperate/cooperate without it) This is another good reason why brands have to be careful allocating ad money...if it's too intermediated or automated, some person or code in the middle could end up costing the brand a lot by defecting in the search ad game and starting a retaliation cycle

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