Esoos Bobnar, MBA

A compelling argument for the importance of experiments and the use of Bayesian modeling. The objection/notion of isolated, walled garden experiments is slowly becoming obsolete. As advertisers increasingly leverage open-source or proprietary geo/user-based experimentation on their own internal metrics, the need for incrementality testing as the gold standard for establishing media causality becomes undeniable. Incorporating experimental data into a Bayesian framework should enable more accurate and actionable insights in this regard.

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Is Bing intentionally monetizing organic map listings in search? For a large #retail client’s  #affiliatemarketing program, we uncovered an anomaly in the Bing Map SERP (search engine results page) that turned a seemingly benign search into an affiliate-sponsored result. We’ve outlined some findings here: https://lnkd.in/dQr_DqiU #SEM #affiliatemarketing #marketingROI #searchengines

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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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What if messy data isn’t the problem we think it is? Earlier this year, I was with a brand director, and the both of us were staring at data and reports from 14 different agencies—each speaking their own "data language": Different conversion definitions Varying standards for qualified leads Unique counting methods for events "Should we just standardize everything?" he asked, clearly frustrated. "Have them all do it the same way." It’s the obvious answer. The safe answer. But maybe...it’s the wrong answer. Imagine getting 14 agencies to change their processes. This would: - Take at least 6-8 months - Cost $500K+ - Risk breaking their existing processes - Create new inconsistencies So I asked a different question: “....what if we didn’t try to fix it?” So we built the team a translator, an AI-powered “Rosetta Stone” 🗝️ for all those different data languages that: → Learns each agency’s unique "language" → Adapts to new data patterns → Gets smarter with each inconsistency The results were surprising. Within 3 months, we had: ✅ 86% data consistency across vendors ✅ Reports in days, not 4-6 weeks ✅ 60% less time spent fixing errors ✅ $300K+ saved in standardization costs And the best part? The system kept getting smarter, learning from every inconsistency and unique data quirk.  4 months in, we hit 88% consistency.  By 6 months, 92%. And we didn't ask a single person to change how they worked.  Not one process needed updating.  Not one spreadsheet needed reformatting. The Takeaway: In a world where everyone has access to the same tools, tech, and capabilities, the competitive edge isn’t in having perfect data—it’s in how you handle imperfect data. Think about it. While others spend months cleaning data, what if you could start extracting value from it today? If you're leading a team juggling multiple data sources and need faster, smarter insights without the cleanup chaos— Lets chat. #DataStrategy #AIinBusiness, #DigitalTransformation, #Analytics #Innovation, #BusinessIntelligence Repost for others ♻️ Thanks!

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🚀 One Platform, Infinite Value! We’re revolutionizing the way brands connect with their audiences. By seamlessly integrating content, community insights, and creators, we empower you to: 🎯 Speak to the right audience 📝 Deliver the right content ⏱️ Engage at the right time 💡 Imagine a world where every audience in your funnel enjoys a personalized, effortless experience with your brand. We’re making it possible. 🌟 Check out this video to learn more about how we’re shaping the future of audience engagement. #Innovation #Marketing #CommunityInsights #Creators #AudienceEngagement #ArtificialIntelligence #MarketingTechnology #LLM #DataScience #GoogleCloudPlatform

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Listen to Statistician and Data Science expert, Allison Olson break down for you how to leverage existing data and move beyond generalized "personas" to hyper-target your specific ideal audience ---- to save money, increase quality lead generation and conversion rates! She spoke last year at SMASH and she is here again to dive into this with me today! Watch the latest episode of The Drake Insights Podcast by Drake Strategic The Drake Insights Podcast Ep 14 - Master Audience Hyper-Targeting with Data Science & Analytics, Allison Olson https://lnkd.in/d5X9Pgnj #seniorlivingmarketing #digitalmarketing #TheDrakeInsightsPodcast #datascience #audiencetargeting #marketingdata

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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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Budget ← [ EXPERIMENTATION TALENT GAP ] → Need There’s a HUGE gap in experimentation & optimization adoption → On one side you have your FAANG + another 50 or so organizations Running 100s to 10s of 1000s of experiments each year. Employing an army of analysts, researchers, engineers, designers, managers & economists. How do you compete with businesses who are so large they need economists to better understand the impact they are having on their users & customers?  (but that’s a whole different topic for another day) → On the other side you have small marketing teams (1-5 people) Barely trying to grasp the complexity of running experiments (let alone at scale) Fighting tooth and nail for budget & headcount Trying to fill the missing expertise with UNICORN 🦄 talent. → Right smack in the middle are all the rest They can’t compete with the giants So they end up levitating towards the bottom of the scale On the hunt for Unicorns as well. Individual employees who can: -Identify (research, analysis)  -Execute (engineering, design, QA) -Deliver (present, document, brief, educate) If you are thinking… “I know there are no Unicorns but I still need to find one or something close to it” You are not alone. And it shows. This week I’m tackling this gap. My goal(s) for this week: - Why it’s dangerous to hunt for experimentation unicorns - What you can do today to try and fill this talent gap - How to compete and win even against giants Happy Monday & have a wonderful week! — — — Still reading… 1. Follow me Hristian 2. DONATE SOCIAL DOPAMINE to a worthy cause: EXPERIMENTATION* *→→ like, comment or share — — — P.S. “Experimentation Confidential | Off the Record” ↳ Private conversations. Actionable insights. → Let’s talk → #experimentation #gtm #b2bmarketing #cro #hiring #employees

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Great insights from firstmovr and Circana! 💡📈 Key takeaway? Greater Frequency ⬆️ 🔊 of Updating PDP Content 📷 = Higher / Above Average Sales Growth for Your Brand 🛒 💰 Your Brand needs a Steady Pipeline of Content🚰 to continually Refresh Your PDPs 🔄 Mike Black: "It reinforces the value of Competitive Insights to inform when the landscape has shifted and you must refresh content to stay relevant." Your Brand must continually be gathering Consumer Insights to stay current on the latest of what consumers are talking about in your category, what they're searching for, what they value, what they're concerned about, excited about, new competitors that are making a splash, etc. These insights empower your Brand to stay ahead of the game and provide crystal clear direction 💎 🧭 for the types of Content that will juice your PDPs and place them in the best position for success 🙌 

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Content measurement requires a more sophisticated model than just pageviews, but simpler than perfect attribution. https://lnkd.in/gjf6hF3T #contentmarketing #attribution #measurement

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An easy way for you to forecast reach and audience penetration is to use Census data. How accurate is it? Well, it depends how accurate you need your forecasting data to be? Census data can be a useful tool if you're building out a geographically-specific persona or researching the potential audience reach within a community or to integrate via a data append. There are hundreds of use cases. But, if you need more precision, and not just directional data, consider harnessing customer insights from real-time brand shoppers using primary research. Nothing is better (and more accurate) then curating insights directly from your customers. https://data.census.gov/ #research #customerinsights #censusdata

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IAB Tech Lab’s launch of #ADMaP, the Attribution Data Matching Protocol, is less of a tech update and more of a survival kit for advertisers navigating the post-cookie wasteland. The so-called "cookie apocalypse" has left advertisers scrambling to measure campaign performance without creeping on users like they used to, and ADMaP is here to save the day. Armed with privacy tech like Private Set Intersection (PSI) and Trusted Execution Environments (TEEs), ADMaP lets publishers and advertisers share data without dragging users' personal info into the mix. Because, let’s face it, nobody wants another scandal in the style of "Facebook meets Cambridge Analytica." Anthony Katsur, CEO of IAB Tech Lab, summed it up: “This isn’t just about compliance—it’s about trust. ADMaP is a game-changer, allowing advertisers and publishers to collaborate safely and accurately while fully protecting their audience’s privacy.” He’s not wrong. The death of third-party identifiers has advertisers fumbling for solutions, and ADMaP aims to bring back some semblance of order. It’s like the Wild West out there, and ADMaP is trying to rein it in without letting privacy fall through the cracks. ADMaP leans into Privacy Enhancing Technologies (PETs), and if that sounds like something you’d hear in a Silicon Valley pitch, it kind of is—but these buzzwords are legit. PETs like PSI let companies compare data without actually seeing anything they’re not supposed to. Imagine two companies comparing customer lists—ADMaP ensures only the intersecting data is visible, keeping everyone’s private details locked up tight. But wait, there’s more. TEEs add another layer of protection by creating secure zones in a device’s CPU where sensitive data can be processed in isolation from everything else. It’s like a panic room for your data, and with ADMaP harnessing these technologies, advertisers can finally sleep at night knowing they’re not leaving user data exposed like sitting ducks. Edik Mitelman, General Manager at AppsFlyer, didn’t mince words either: “To exact change and to solve for measurement and attribution, the industry needs bold actions.” ADMaP is one such bold action, but as Mitelman points out, it’s only a first step in what’s sure to be a long journey. Still, it’s a necessary one, and as far as the ecosystem is concerned, this protocol is a big leap forward in aligning privacy-first solutions with advertisers' needs. Of course, it wouldn’t be an adtech rollout without a bit of humor. Imagine Katsur quipping, “If ADMaP doesn’t fix the measurement crisis, my next gig might be setting up a Witness Protection Program for advertisers.” (Not a real quote, but come on, we can all imagine it.) Angelina Eng

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Uh-oh. They gave Stu Eddins and me a mic. 🎙 Stu's unleashing his tidal wave of knowledge from 20+ years in digital marketing - and we're not holding back the sass. 💥 Have a listen and zap us your thoughts. We've got a lot of hot topics in the vault to share this summer! 🔥 #Podcast #DigitalMarketing #ContentMarketing #HigherEd #Healthcare

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READ OF THE DAY! Insights on #AI impact on #search from Yoast's Carolyn Shelby Want to future-proof your SEO strategy? Focus on being the most trusted answer on the web. E-E-A-T and QC are just labels – the real job is to show your work.

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A slightly more ☠️ fearful/doomsday view on AI Overiview (SGE)... Articles about AI Overview are flooding our trusted sources of daily search news 💡 Article: https://lnkd.in/dr_espE6 🏃‍♀️💨TLDR: The article discusses Google's new AI Overviews feature, which synthesizes search results instead of showing traditional links. This change aims to simplify searches but has led to more overall search activity, suggesting that it might not be effectively answering user queries. Critics argue that this system could: 1️⃣ drive users to alternative platforms 2️⃣ and hurt publishers by reducing clicks to their sites. There's concern that Google's focus on AI could complicate searches and affect how information is accessed, potentially impacting advertising costs and reducing website traffic. 💭 Hold your horses concluding anything. If this rollout progresses like PMAX did, we wont be able to conclude much anytime soon, as Google we continuously test until a balance is found. Now is a good time for searchers to start investing a bit more time on learning more about all the other platforms that have recently seen a bit of growth ;-)

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CONFIRMED: AI Overviews still almost entirely INFORMATIONAL in intent We looked at over 1 million keywords from our seoClarity Research Grid (US) and found that over 96% have informational user intent. AIOs show in purely transactional queries only 1.2% of the time. This means as suspected, Google is restricting AI Overviews to where they likely have the most utility for users: summarizing information on informational questions. Here's the breakdown by all user intents detected. (Be watching later this week when I'll share how this affects ecommerce sites in particular!) Informational = 96.5% Informational/Transactional = 1.79% Transactional = 1.2% Navigational = 0.4% Informational/Navigational = 0.07% Informational/Navigational/Transactional = 0.01% Navigational/Transactional* = 0.00% Local/Informational* = 0.00% *Only 2 keywords with this intent showed an AIO in these mixed categories

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