Digital Advertising Metrics

Real-time data analytics is transforming businesses across industries. From predicting equipment failures in manufacturing to detecting fraud in financial transactions, the ability to analyze data as it's generated is opening new frontiers of efficiency and innovation. But how exactly does a real-time analytics system work? Let's break down a typical architecture: 1. Data Sources: Everything starts with data. This could be from sensors, user interactions on websites, financial transactions, or any other real-time source. 2. Streaming: As data flows in, it's immediately captured by streaming platforms like Apache Kafka or Amazon Kinesis. Think of these as high-speed conveyor belts for data. 3. Processing: The streaming data is then analyzed on-the-fly by real-time processing engines such as Apache Flink or Spark Streaming. These can detect patterns, anomalies, or trigger alerts within milliseconds. 4. Storage: While some data is processed immediately, it's also stored for later analysis. Data lakes (like Hadoop) store raw data, while data warehouses (like Snowflake) store processed, queryable data. 5. Analytics & ML: Here's where the magic happens. Advanced analytics tools and machine learning models extract insights and make predictions based on both real-time and historical data. 6. Visualization: Finally, the insights are presented in real-time dashboards (using tools like Grafana or Tableau), allowing decision-makers to see what's happening right now. This architecture balances real-time processing capabilities with batch processing functionalities, enabling both immediate operational intelligence and strategic analytical insights. The design accommodates scalability, fault-tolerance, and low-latency processing - crucial factors in today's data-intensive environments. I'm interested in hearing about your experiences with similar architectures. What challenges have you encountered in implementing real-time analytics at scale?

𝐀𝐌𝐂’𝐬 𝟐𝟎𝟐𝟓 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: 𝐀 𝟑-𝐒𝐭𝐞𝐩 𝐒𝐎𝐏 𝐭𝐨 𝐄𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐞 𝟐𝟐% 𝐀𝐝 𝐖𝐚𝐬𝐭𝐞 & 𝐒𝐞𝐜𝐮𝐫𝐞 𝟔.𝟏𝐱 𝐑𝐎𝐀𝐒 𝘝𝘢𝘭𝘪𝘥𝘢𝘵𝘦𝘥 𝘣𝘺 1.2𝘉 𝘉𝘪𝘥𝘴, 𝘔𝘐𝘛’𝘴 𝘐𝘯𝘵𝘦𝘯𝘵 𝘞𝘪𝘯𝘥𝘰𝘸 𝘙𝘦𝘴𝘦𝘢𝘳𝘤𝘩, 𝘢𝘯𝘥 8-𝘍𝘪𝘨𝘶𝘳𝘦 𝘉𝘳𝘢𝘯𝘥 𝘊𝘢𝘴𝘦 𝘚𝘵𝘶𝘥𝘪𝘦𝘴 🎯 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗩𝗮𝗹𝘂𝗲 𝗳𝗼𝗿 Amazon Ads 1. 𝙋𝙧𝙤𝙗𝙡𝙚𝙢-𝙩𝙤-𝙎𝙤𝙡𝙪𝙩𝙞𝙤𝙣 𝘼𝙡𝙞𝙜𝙣𝙢𝙚𝙣𝙩 Boardroom Pain Point: 79% of brands hemorrhage budget via outdated tactics (per Amazon’s 2025 Ad Waste Index). SOP-Driven Fix: AMC’s 3-Step Real-Time Optimization Engine cuts waste by syncing bids to MIT’s “47-Minute Intent Window.” 2. 𝙍𝙊𝙄-𝘽𝙖𝙘𝙠𝙚𝙙 𝙈𝙚𝙩𝙧𝙞𝙘𝙨 For CFOs: ↓19% CPC | ↑55% conversion velocity (Motif Digital Case Study). For CMOs: 41% new-to-brand growth via AMC’s ASIN Autopsy Protocol. 📐 𝗧𝗵𝗲 𝗟𝗲𝗮𝗱 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸: 𝗔𝗠𝗖’𝘀 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗦𝗢𝗣 𝙎𝙩𝙚𝙥 1: 𝘿𝙞𝙖𝙜𝙣𝙤𝙨𝙚 𝙕𝙤𝙢𝙗𝙞𝙚 𝙆𝙚𝙮𝙬𝙤𝙧𝙙𝙨 Tool: AMC’s SearchTermIQ Audit → Identifies keywords with <37% post-72hr intent (Source: 2025 Amazon Search Decay Report). Action: Automate bid pauses via API integration with Seller Central. 𝙎𝙩𝙚𝙥 2: 𝘿𝙚𝙥𝙡𝙤𝙮 𝘾𝙤𝙣𝙫𝙚𝙧𝙨𝙞𝙤𝙣 𝘾𝙋𝙍 Tech Stack: Nielsen’s purchase-intent AI + AMC’s Bid Defibrillator → Auto-injects bids during MIT’s validated intent spikes. Outcome: 6.1x ROAS in 90 days (per 2025 Seller Central Dashboard benchmarks). 𝙎𝙩𝙚𝙥 3: 𝙊𝙥𝙩𝙞𝙢𝙞𝙯𝙚 𝙋𝘿𝙋 𝙑𝙚𝙡𝙤𝙘𝙞𝙩𝙮 Metric: Core Web Vitals’ 2.1s load threshold → Flags ASINs with >48hr conversion lag. Fix: AMC’s Speed Surgeon tool + AWS’s edge-compute caching. 🧩 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗧𝗵𝗶𝘀 𝗦𝗢𝗣 Scalability: 92% of workflows auto-pilot via AMC’s AI (no added headcount). Risk Mitigation: Peer-reviewed by 3PL Analytics Guild (0 critical flaws in 2025 audit). Competitive Edge: Top 1% sellers deploy these tactics 47 days faster than peers. 📆 𝗡𝗲𝘅𝘁-𝗦𝘁𝗲𝗽 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 Comment "𝗔𝗠𝗖" to get a success blueprint for AMC. Pilot: 45-day AMC sprint → Guaranteed 15% CPC reduction or fee waived. #amazonads #amazonPPC #AMC #AMCuses

A Senior Data Engineer candidate was asked to design a real-time analytics pipeline during his interview at Netflix. Another candidate in a different loop at Uber got the same prompt. Real-time dashboards look simple until you add one layer of reality: – Add late arrivals? Now you need watermarks, session windows, and late-firing logic. – Add out-of-order events? Now event-time vs processing-time becomes your entire correctness model. – Add exactly-once semantics? Now idempotent sinks and transactional commits are non-negotiable. – Add backpressure? Now Kafka is lagging or your sink is choking and alerts are firing. – Add historical corrections? Now you're reconciling streaming state with batch recomputes. Here's my checklist of 15 things you must get right when building real-time analytics: 1. Start with your latency and correctness contract → Define what "real-time" actually means: sub-second? 5 minutes? End-to-end or just processing? And define correctness: approximate is fine, or must be exact? 2. Choose your processing model: Lambda vs Kappa → Lambda = separate batch + stream paths, eventually consistent. Kappa = stream-only, simpler but harder to backfill. Most companies say Kappa but run Lambda in disguise. 3. Pick your event-time strategy early → Use event timestamps, not processing timestamps. If events don't have timestamps, you're already behind. Decide: use producer time, log append time, or application time? 4. Design your windowing logic to match business semantics → Tumbling windows for fixed intervals. Hopping for overlapping aggregations. Session windows for user activity. Getting this wrong means your metrics lie. 5. Implement watermarking to handle late data → Watermark = "no events before this timestamp will arrive." But late data still arrives. Set your watermark delay based on observed lateness, not wishful thinking. 6. Build a late-firing strategy that doesn't break downstream → When late data arrives after the window closes, decide: update the past metric (retractions), append a correction, or drop it. Each has trade-offs for downstream consumers. 7. Handle out-of-order events with buffering and sorting → Events rarely arrive in order. Buffer and sort within your watermark delay. If you don't, your aggregations are wrong and nobody will notice until the CEO asks why revenue dropped. 8. Design for exactly-once semantics from source to sink → Kafka supports exactly-once within Kafka. Flink supports exactly-once with transactional sinks. But your sink (Postgres, Elasticsearch) must be idempotent or transactional too. 9. Make every sink operation idempotent → Assume every write happens twice. Use upsert patterns: INSERT ON CONFLICT, MERGE, or idempotency keys. Never use blind INSERT or INCREMENT operations. (Continued in comments)

Are your A/B tests actually telling you the truth? 💡 Throughout my career as an advertiser and product manager, A/B testing has been a go-to tool for validating hypotheses, gathering insights, and making data-driven decisions. 📊 But when it comes to advertising, the digital platforms brands rely on may be influencing the results more than we think. 🧐 New research, “Where A-B Testing Goes Wrong” (Aug 2024), sheds light on a concept called “divergent delivery”. It turns out, algorithms may be clouding the real impact of ads: 1️⃣ Algorithmic Targeting: Ads are shown to different, optimized user mixes—skewing A/B test outcomes. 2️⃣ User Heterogeneity: Diverse responses from users complicate the true measurement of your ad’s effectiveness. 3️⃣ Data Aggregation: Aggregated results may not accurately reflect how ads perform in different user segments. So, what does this mean for advertisers? ❌ Are your decisions based on flawed data? It’s critical to understand the limitations of A/B testing in online advertising. ✅ How will you adjust your strategies? Tailoring your experimentation settings and leveraging independent measurement solutions can help you get more reliable insights. #advertising #media #tech

Here is the Playbook I'd use to find a balance of DR and Brand if I were to do it again. If you’re looking to find a way to invest in brand in a way that’s accountable to revenue so you can get out of the DR and Discounts race to the bottom, this post is for you. Or, if you're seeing increasing customer acquisition costs with no end in sight and know you need to find a way to invest in the longer term growth of the business, but can't because you're not able to measure the revenue impact, this post is for you. Chubbies' transition from a fast-growing, money-losing, short term revenue obsessed brand to a fast growing, profit generating, short AND LONG term revenue obsessed brand was a multi-year mess, but helped save the company. Based on everything we learned, here's how I might approach it if I were to do it again Hope this helps -- ⚖️The 3-Month Playbook for Balanced Performance Marketing 🏆Goal: Drive as much resilient revenue as short term paid revenue with your paid marketing ✍️Definitions: Resilient Baseline Revenue: - The revenue you have left over when you turn off short term ads and discounts. - Revenue from organic search, direct and organic social referral sources with short term influences removed to get to true base. Paid revenue: Revenue that’s not from resilient baseline or from email / sms 📊Results & Measuring Success 💥 Immediately: Increased quality engagements (shares, saves, comments). 🔍 30 Days: Boost in branded search, organic, and direct traffic 💵 30-90 Days: Increased revenue from organic search and direct, with high revenue per session Part I: Mindset Shift 🤔 Step 1: Rethink ROAS 🚫Increasing ROAS doesn’t drive profit growth 🔻Lower ROAS is the goal 💡Ensure team knows that Part II: Get Your DR Right 📊 Step 2: Optimize Short Term DR 🧐Run short-term incrementality tests. Ensure spend is incremental 🧮Use Marginal CAC to inform where, when and how to allocate spend Part III: Start Small. Start Now. 💸 Step 3: Put Money Behind Existing Top Organic Content ✅Use 5% of budget to boost old posts with high shares, comments and saves ✅5% for conversion-optimized ads from top organic posts ✅5% for engagement optimized ads from top organic posts Part IV: Create Content Machine 🎥 Step 4: Hire Hungry Content Creators Hire 3 creators who are hard-working learners and loyal customers 🎯 Step 5: Define Your Brand's Content Arena Identify your brand’s unique gaps (product, positioning, etc.) and the feeling/moment you want to own 🎬 Step 6: Content Machine ✌️Double your video output every week until you can’t 🛠️Constantly improve concept quality 🔻Constantly decrease cost per content piece Part V: Go From Testing to Balance 📈 Step 7: Test, Measure, and Learn Track results and apply lessons in an objective way 🆙 Step 8: Scale Budgets and Incorporate New Content 🔁Go back to Step 3 and increase budgets 🤗As the Creative Machine makes new content, incorporate it 🌗Get to 30% - 50% of budgets

One of the most misunderstood concepts in Programmatic Advertising is the difference between Viewable Impressions and Measurable Impressions. Many marketers use these terms interchangeably, but they measure two completely different aspects of campaign performance. 🔹 Measurable Impression answers the question: "Was this programmatic advertisement technically measured?" This metric tells you whether the ad server or verification partner (such as IAS, DoubleVerify, or MOAT) was able to track the impression. If an impression isn't measurable, you can't accurately determine its quality or performance. 🔹 Viewable Impression answers a different question: "Did the user actually have an opportunity to see the programmatic advertisement?" According to the Media Rating Council (MRC) guidelines: ✅ Display Ads: At least 50% of the ad's pixels must be visible for 1 continuous second. ✅ Video Ads: At least 50% of the ad's pixels must be visible for 2 continuous seconds. This distinction is critical because: 📊 An impression can be measurable but not viewable if: • The ad loads below the fold. • The user never scrolls to it. • Less than 50% of the creative is visible. • The user switches tabs before the time threshold is met. Likewise, a served impression may not even be measurable because of: • Ad tag implementation issues • Ad blockers or privacy settings • Browser or app limitations • Network interruptions Why does this matter? If you're only looking at impressions, you're measuring delivery. If you're looking at measurable impressions, you're evaluating measurement reliability. If you're looking at viewable impressions, you're evaluating the quality of exposure. The best-performing programmatic campaigns don't optimize for just one metric-they optimize for both. 💡 Key Takeaways ✔ Measurable Impressions = Reliable measurement ✔ Viewable Impressions = Opportunity to be seen ✔ Every viewable impression is measurable, but not every measurable impression is viewable ✔ Monitoring both metrics together provides a more accurate picture of campaign effectiveness Understanding this difference helps media buyers, campaign managers, and advertisers make smarter optimization decisions, improve inventory quality, and maximize return on advertising spend. 👉 𝐅𝐨𝐥𝐥𝐨𝐰 Kautilya Roshan 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧𝐬 𝐨𝐧 𝐃𝐕360, 𝐓𝐡𝐞 𝐭𝐫𝐚𝐝𝐞𝐝𝐞𝐬𝐤 & 𝐂𝐌360. How do you prioritize viewability in your programmatic campaigns? Share your experience in the comments. 👇 #ProgrammaticAdvertising #DV360 #TheTradeDesk #DigitalAdvertising #MediaBuying #AdTech #Viewability #Measurement #DisplayAdvertising #VideoAdvertising #AdvertisingTechnology #MarketingAnalytics #CampaignOptimization #DigitalMarketing #AdOperations

Day (22) - The importance of frequency with LI Ads. ↓ Which is better? (A) A million people seeing your ads once.  (B) 100k people seeing your ads 10x times. With LinkedIn Ads and in B2B in general, the answer is nearly always option B. When I worked at LinkedIn, there was a report I used to runevery quarter on Frequency. That report, more or less every time, showed that LinkedIn members need to see an ad between 6+ times from a brand before they take an action. And in this instance, an action means the initial click. Not even considering the frequency needed before a buying decision. In short, low frequency can actually be extremely wasteful. — You can increase the probability of being noticeable by managing ad frequency. The aim with LI Ads, should be to be as frequent as possible without frustrating your audience. It’s a balance, but if your audience are only seeing 1-3 of your ads monthly, you’re probably not being noticed at all. It evolves as the activity in the LinkedIn feed evolves but the rule of thumb is the below: Low Frequency: Below 5 impressions per member per month.  Optimal Frequency: 6-10 impressions per member per month. High Frequency: 11+ impressions per member per month. If your campaigns achieve the mid-high range, you’re much more likely to become noticeable. — But can you push it too far? Yes, you absolutely can. I've seen it many times. So then, how do you know when you've pushed too far? The first sign is declining CTR and/or Conversion rate, depending on your objective. Once the graph week-over-week is consistently in decline, it's probably time to refresh your ads. The second and most vital sign is negative sentiment. If someone takes the time to comment something like "I see this ad every day" or "How can I stop seeing this ad" then you've probably entered the dangerous territory of your frequency being too high — There’s no automated way to control this on the LinkedIn Ads tool But. You can control it manually in 3 ways: 1. Increase/Decrease audience size  2. Increase/Decrease budget  3. Have at least 5 creatives per campaign (because of unique creative frequency caps) And over some weeks, track the difference in the delivery tab. This should do the trick. — Overall, you can definitely help your ads to be more noticeable and improve the effectiveness of your campaigns by working frequency. Your ads definitely still have to be good... but use the above to achieve high frequency with great ads and you’ll create much more of those ‘right place, right time’ moments. -- Want to make sure you don't miss any of the final 9? Follow me and/or join 1,202 LinkedIn Ads folks who will get it directly to their inbox → https://lnkd.in/dVxtm6JC #31LinkedInAdsLearnings

When I'm building reports on transactional data from database, I always recommend Change Data Capture (CDC)—not just for real-time analytics, but as the best way to replicate data from databases while minimizing impact and ensuring transactional consistency. OLTP systems are built for high-speed, small transactions, heavily relying on buffer cache to maintain efficiency. Running large analytical queries directly on these systems can increase cache pressure, pushing out critical transactional data and slowing down your operational performance. CDC offers an elegant solution. Instead of running heavy queries or full-table scans, CDC works by mining the transaction log, piggy-backing on the database’s existing logging process. This keeps overhead low since the database is already logging those changes. CDC then replicates just the incremental changes, which means your OLTP system stays optimized for its core purpose: handling transactions. Some people might consider "ZeroETL" or federation, but unless there's smart caching, these approaches still put pressure on the source database. Often, CDC is still needed in the background to move the data efficiently. In my experience, CDC is more than just a method for real-time analytics—it’s the best way to replicate transactional data with minimal performance impact while ensuring data consistency across your pipeline.

We took a founder to 4M+ impressions and 26,000+ followers on LinkedIn in under a year. The signal we were optimizing for the whole time isn't something LinkedIn shows you by default. Impression going up feels like progress, but if your goal is to get opportunities and become known in your industry, it can be the most expensive distraction in your content strategy. Because you optimise what you track for, and the metric that actually matters is what your content made someone do after they read it. For example, one of the posts of the same founder had 50,000+ impressions, 200+ likes, and  15 comments. Pretty average performance based on reach. But it also had 40 sends and 20 saves. It was being forwarded inside teams and saved by people planning to act on it. That's why for all our clients our focus is always on these 4 metrics- 1. Saves:  Someone saving your post is planning to use it. High saves almost always outperform on the pipeline because they're solving something specific enough to revisit. 2. Sends:When someone forwards your post to a colleague your content just entered a room you were never in. 3. Followers gained per post: Reach means nothing if it's bringing the wrong people. We track which posts are converting the right audience to her profile. 4. Profile visits: Someone read your post, wanted to know more, and went looking. That's a prospect one step from a DM. Our team reviews all the posts on these metrics every week for all our clients.  👉 High saves and sends get repurposed and turned into new formats. 👉High impressions with nothing else get ignored. Over time, we built a library of formats that actually convert.  PS: What metric are you optimising for right now? #LinkedInGrowth #ContentStrategy #PersonalBranding #FounderMarketing #B2BMarketing

Most DV360 campaigns don’t fail because of strategy. They fail because of inefficiency. After managing large-scale programmatic spends, one pattern is clear. Brands are not losing money on bidding. They’re losing money on distribution. Here’s what typically goes wrong: • 30% of impressions go to users already overexposed • Low viewability inventory quietly eats budget • Audience segments are scaled without performance validation And yet, teams keep optimizing CPM. The real optimization framework looks very different: 1. Fix reach vs frequency imbalance If your frequency is above 6–8, you’re not scaling — you’re repeating. 2. Eliminate cost leakage Inventory source and viewability reports often reveal 20–30% wasted spend. 3. Rebuild audience strategy High CPM + low engagement segments should never scale. 4. Shift bidding logic Manual CPM is control. Automated bidding is efficiency. 5. Optimize distribution, not just delivery The goal is not more impressions — it’s better allocation of impressions. Because in programmatic: More spend ≠ more impact Better distribution = better performance The brands that win in 2026 are not spending more. They’re spending smarter. #programmatic #optimisation #adtech #marketing #leadership