"New metrics for marketers: How to measure real ROI in 2025"

Did you know that nearly 65% of Google searches now end without a click? And on mobile, that jumps to over 75%. This seismic shift is changing how brands connect with audiences online. Zero-click searches mean users get answers directly on search results pages through AI Overviews, featured snippets, and knowledge panels. But here's the twist - this isn't necessarily bad news for marketers who adapt their strategy. The key is that zero-click doesn't mean zero opportunity. It means evolving beyond traditional SEO to embrace new approaches like Generative Engine Optimisation (GEO), and integrating digital with proven offline channels (yes, we mean direct mail) 😉 Read our complete guide to thriving in the zero-click era: https://lnkd.in/eRsi4gt2 #ZeroClick #Marketing #DirectMail #MarketingStrategy

A couple of weeks ago, I mentioned how people over-attach themselves to last-click attribution — so let’s unpack that a bit. Marketing is simple: run ads → people learn about your product → some of them buy. But how do you know they bought because of your ads? With cookies, UTMs, and referrers, you can track user journeys — each touchpoint leading up to a conversion. (If you were oblivious to marketing until this post, start paying attention to what URLs look like after you click on an ad, particularly what comes after the question mark.) Attribution models then split credit across those events. The most common one, last-touch attribution, gives all credit to the last event. Sounds fine — until you realize how often people google a brand they were already planning to buy. Google gets the credit, but didn’t cause the sale. (That’s the tip of the iceberg. There are so many other problems, but let’s keep it brief for LinkedIn.) That’s the core issue: last-touch attribution captures correlation, not causation. It can’t tell which conversions happened because of a channel versus those that would’ve happened anyway. If you ignore that, you’ll over-invest in lower-funnel channels — the ones closest to the purchase. To fix this, you need to measure incrementality — the causal effect of a channel on conversions. And there’s no (perfect) shortcut: you have to run experiments. Cut spend on a channel and see what happens. If your model says a channel drives 40% of sales, but sales drop only 20% when you pause it — now you know. That’s incrementality. With that knowledge, you might then be able to steer your attribution models towards a more accurate picture. Caveat: this is an everchanging ocean, so you’ll have to re-test the waters once in a while. It may feel cheaper to just stick with the last-touch model, but that is just because ignorance is bliss. And as a data scientist, I get to have some fun helping steer the boat, because at center of all of this is the beautiful world of causal inference. 🙃

I've said before I just do not understand how the whole attribution idea even became popular (or rather I do, the idea is simple and easy to explain, plus you need not learn econometrics). but, as an econometrician entering the world of marketing for the first time, I was (and still am) absolutely horrified to discover this practice be used and indeed accepted by professional, serious people, as a way for guiding decisions. It cannot offer a causal solution.

From vanity clicks to durable lift. Google's collapsible ads change more than where people click. They change what you should measure. Most teams will keep tracking average position and raw CTR. Those metrics assume stable visibility. That assumption just died. Collapsible ads mean exposure is variable now. Users can hide sponsored results after scrolling past them. Your ad might appear above the fold, below AI Overviews, or inside a Shopping cluster. Each placement behaves differently. Here's what I'm tracking instead: → Viewable reach (did the user actually see it before collapsing?) → Qualified click rate... clicks divided by viewable impressions, not total → Blended CPC, segmented by placement context → Assisted conversions that survive visibility gaps Segment everything by where it appeared. Above fold has different conversion mechanics than below AI Overviews. Shopping ads have their own curve entirely. The goal isn't more clicks. It's knowing which placements drive qualified traffic under variable visibility. One shift in how you measure, less noise when you report, better decisions when budgets get tight. What's the first metric you're rethinking? Share if you'd rather build durable measurement than chase vanity numbers → #GoogleAds #PPCMetrics #DigitalMarketing #AdPerformance #MarketingAnalytics

🚀 𝐆𝐨𝐨𝐠𝐥𝐞 𝐀𝐝𝐬 2025 = 𝐀𝐈 + 𝐃𝐚𝐭𝐚 > 𝐊𝐞𝐲𝐰𝐨𝐫𝐝𝐬 + 𝐌𝐚𝐧𝐮𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐀𝐈 𝐚𝐧𝐝 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐚𝐫𝐞 𝐝𝐫𝐢𝐯𝐢𝐧𝐠 𝐫𝐞𝐚𝐥 𝐥𝐢𝐟𝐭𝐬: ➤ Smart Bidding Exploration campaigns show ~18% broader query coverage and ~19% more conversions on average. ➤ Performance Max campaigns in real-world cases delivered ~44% more conversions + ~47% lower CPA. ➤ Uploading first-party customer data and strong audience signals is now table-stakes for maximizing the algorithm. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚𝐧𝐬 𝐟𝐨𝐫 𝐦𝐚𝐫𝐤𝐞𝐭𝐞𝐫𝐬: ��� Feed the machine with clean, high-quality data (first-party audiences, conversion value) ✔ Move beyond keyword-only thinking, the algorithm now cares about who and when, not just what. ✔ Creative + audience signal are now as important as bid and budget. ✔ Manual tweaks alone won’t cut it, train the algorithm instead of just managing it. #GoogleAds #PaidMarketing #AIinMarketing #PerformanceMax #DigitalAds #Marketing2025

𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗱𝘀 𝗶𝗻 𝟮𝟬���𝟱: 𝗪𝗵𝗮𝘁'𝘀 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 (𝗔𝗻𝗱 𝗪𝗵𝗮𝘁'𝘀 𝗡𝗼𝘁) The playbook changed. Here's what I'm seeing across 30+ active accounts: ❌ 𝗪𝗛𝗔𝗧'𝗦 𝗗𝗬𝗜𝗡𝗚: Broad match keywords (wasted spend) Single-ad campaigns (AI needs data) Manual bidding only (can't compete) Generic landing pages (low Quality Score) Ignoring mobile (60% of searches) ✅ 𝗪𝗛𝗔𝗧'𝗦 𝗖𝗥𝗨𝗦𝗛𝗜𝗡𝗚 𝗜𝗧: Performance Max campaigns (when set up RIGHT) Video ads in search campaigns Audience signals + first-party data AI-powered bidding with conversion tracking Server-side conversion tracking (recovers 40% lost data) 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘀𝗵𝗶𝗳𝘁: Google's AI is actually good now. But only if you feed it quality data. My current winning formula: Performance Max for prospecting Search campaigns for high-intent keywords YouTube for retargeting GA4 server-side tracking for accuracy Blend manual + smart bidding Results: Average account ROAS up 60% vs last year. What's working for you in 2025? #GoogleAds #DigitalMarketing #PPC #MarketingTrends

🚀 Google Ads Update You Shouldn’t Miss Two major shifts just landed: 1. Ads in Search are now grouped under a “Sponsored results” label that stays on screen — and users can collapse that section with “Hide sponsored results.” 2. With Google Ads API v22, generative AI for ad creation and new Smart Bidding metrics are now live. What this means for us as advertisers: • Stronger emphasis on ad relevance & creative excellence • Faster scaling with AI-powered asset generation • Sharper analytics to optimize performance It’s a great time to revisit your ad strategies and ensure your campaigns are built to stand out — not just show up. #GoogleAds #PPC #DigitalMarketing #AdTech #AIinAds

⚔️ SKAGs Are Dead. Long Live HTAGs (Highly Themed Ad Groups). The old religion of Google Ads the Single Keyword Ad Group (SKAG) is officially obsolete. If you're still building campaigns around it, you're actively fighting the machine. SKAGs were designed for a time when we managed bids manually and needed tight control over ad copy. In the era of Smart Bidding and AI, SKAGs kill data volume and starve the algorithms that power your success. The New Structure: HTAGs The future is the Highly Themed Ad Group (HTAG). The formula for the modern, scalable Google Ads structure is: HTAG = Right Keywords Match + Audience Signals + Smart Bidding What is an HTAG? It's an Ad Group built around a single core theme (e.g., "SaaS Sales Software"), containing 5-10 theme related keywords, highly-relevant RSAs (Responsive Search Ads), and crucially, robust audience signals (Customer Match lists, Custom Segments) to guide Google's automation. Why HTAGs Win: ✓ Data Consolidation: They aggregate conversion data faster, allowing Smart Bidding to exit the "Learning" phase and optimize performance sooner. ✓ Long-Tail Capture: They empower Broad Match to efficiently capture unexpected, highly-relevant, long-tail queries you'd never find in keyword research. ✓ Efficiency: You spend less time micromanaging keywords and more time perfecting the high-leverage assets (copy and landing pages) that actually drive conversions. If your agency is still promising to deliver the SKAG model, it's a red flag. The complexity of modern search demands thematic structure over granular control. Are you still holding onto your old SKAG structure? If so, why? Let's discuss the hesitation! #GoogleAds #PPC #SearchMarketing #SmartBidding #DigitalStrategy

In the first few days of my paid media project, I focused on understanding the foundational data landscape of paid media campaigns. Paid media involves advertising through paid channels like Google Ads and Facebook Ads, generating critical data such as campaign settings, ad creatives, impressions, clicks, conversions, and spends. My key takeaway was the importance of designing scalable and reliable data ingestion pipelines that consolidate diverse ad platform data into a unified warehouse, while ensuring data consistency and quality for actionable analytics.

Turning Data into Growth: Performance Max Campaign Success Story  Data never lies — but optimization makes it shine. Recently, we ran a Performance Max campaign that delivered remarkable growth across key metrics: ✅ 74 Conversions : +24% growth ✅ Cost per Conversion: ₹34.36 — Improved by 56% ✅ Clicks: +24% ✅ Impressions: +4% By refining audience signals, enhancing creative assets, and leveraging Google’s AI-driven optimization, we achieved stronger ROI and lower acquisition costs — without increasing spend. When strategy meets machine learning, Performance Max can outperform even the best manual setups — if you know how to guide it. #GoogleAds #PerformanceMax #DigitalMarketing #MarketingStrategy #PPC #AdOptimization #GrowthMarketing #DataDriven