The more I study, the more I want to learn. If you work in Performance Marketing, I recommend doing this. If you have been in the field for years, enhanced conversions, consent mode, conversion modeling, and attribution will not surprise you. What inspired me most was the AI powered bidding and especially Google Meridian: Marketing mix models are becoming a real path to solving the key measurement challenges we deal with every day. What is the one measurement topic you wish more marketers understood?

A few months ago, a founder friend asked me why his ROAS kept slipping even though his team was "on top of the dashboards every day." I asked him: when something broke, how long did it take to find out, and how long to know why? He didn't have an answer. Most marketers don't. That's the actual problem in performance marketing. It's not a lack of data. It's that nobody has time to turn data into a decision before the budget is already spent. So we built Outstrip, an AI Growth Operating System that does that turning, automatically. It audits every ad account for waste and risk, watches your competitors' moves in real time, and hands you ranked, one-click recommendations instead of one more chart to interpret. Early results from teams running on it: 📉 28% less ad waste ⏱️ 12 hours saved weekly on reporting 🔍 34 issues caught on day one 🚀 9+ new opportunities surfaced every week If you want to see what this looks like on your own account, sign up and we'll send you a complete June growth report, free. You'll see exactly where you're bleeding spend and what we'd do about it. Comment "REPORT" or sign up below, and I'll personally make sure you get it. #PerformanceMarketing #GrowthMarketing #AI #StartupGrowth

What if your marketing data could talk? What would you ask it? 🤔 I built an AI chatbot to track my own bugs and feature notes while building my attribution platform, AttriPath. Then I realized it could do more than that. It could tell me what the numbers actually mean and what to do next, saving me hours of number crunching and analysis. Tired of dashboards that don't actually answer anything? Swipe through.

Half of consumers are already intentionally using AI-powered search to discover information before they ever visit a website. That means more consumer decisions are being shaped before traditional website analytics even begin measuring the journey. If discovery is changing, shouldn't the way we measure marketing change too? At DMscore, we understand visibility has become a significant stage in the customer journey. That's why we created our AI Search Score, helping businesses understand how they appear in one of the fastest-growing discovery channels. Read more about why we believe the next generation of marketing measurement starts before the click ➡️ https://lnkd.in/e66kw_j6 Source: McKinsey & Company #MarketingMeasurement #DigitalVisibilityMeasurement #OnlineShareOfAttention #AISearch #ConsumerBehavior #MarketingStrategy #DigitalMarketing #MarketingAnalytics #ThoughtLeadership #DMscore

Most AI-powered marketing programs I see have a measurement problem hiding in plain sight, and it tends to show up when teams are already deep into scaling. When you run campaigns built on predictive bidding and AI-personalized journeys but evaluate them with legacy attribution, the data you get back is not neutral. Channels that do meaningful work early in the customer journey get ignored because the model does not credit them, and the algorithm quietly learns to optimize toward whatever looks clean in a dashboard rather than what actually drives revenue. What makes this expensive is that it compounds. Each budget cycle, spend shifts toward channels that appear to perform under a flawed model, and the real growth drivers get starved. A team I know pulled apart their attribution setup before a major campaign scale-up and found that two of their highest-spend channels were cannibalizing credit from a mid-funnel touchpoint that was doing most of the actual conversion work. Measuring an AI-driven program with pre-AI infrastructure is probably the most common way marketers end up with confident numbers that are quietly wrong. #MarketingStrategy #DigitalMarketing

You have more marketing data than ever. And somehow it's harder to know what to do next. More dashboards. More reports. More campaigns producing more metrics. But questions like — why did this deal close, what's shifting in how your buyers think, which part of your market is warming up before they raise their hand — those still don't have clear answers. Meanwhile, a competitor shows up one quarter with sharper positioning and more specific language. Directly addressed to a fear you also solve, but hadn't articulated that way. They learned something from their market and acted on it while you're still sitting on the data. The gap is that nobody has been turning your campaigns into intelligence — connecting what goes out to what the market says back, and feeding that into every decision that follows. If you haven't sat with this question yet — what does our marketing operation look like if we redesign it with AI at the centre — it's probably the most important one on your table right now.

Most analytics tools tell you how many people visited your website. They don't tell you what actually made someone become a customer. So I built hight. Instead of chasing pageviews and vanity metrics, hight helps you understand what actually drives conversions: 📊 Which marketing channels bring real customers 🎯 Which campaigns are actually performing 🚪 Where visitors drop off before converting and more It's privacy-friendly (no cookie banners), and I'm experimenting with an AI assistant that explains your analytics to you — instead of leaving you to dig through dashboards. Here's what I've learned building this: Businesses rarely need more data. They need better answers. if you are a founder/marketer you can give it a try on hight.ai

Third-party data deserves a bigger role in marketing strategy than it usually gets. Your own data shows only your piece of the market, while third-party data helps you understand the larger opportunity. The strongest marketing strategy isn't choosing between first-, second-, or third-party data, but combining all three to create a more complete picture of customers and make AI and marketing decisions more effective. LiveRamp's John Shaughnessy breaks it down in this episode of "Here's the deal..."

Had fun highlighting how LiveRamp Data Marketplace helps our network earn better marketing and business outcomes. Big thanks to Anne Acker and Jeff Dubois for the opportunity. And also to colleagues and partners who strive daily to make third-party data a safe, trusted tool to power smarter decisions.

First-party data provides valuable customer insights, while third-party data delivers audience intelligence that helps advertisers discover new audience opportunities and reach consumers beyond their existing customer base. At AlikeAudience, we provide trusted third-party audience data at scale to help advertisers discover high-value audiences, expand their reach beyond existing customers, and power more effective omnichannel campaigns. Great perspective from LiveRamp💡