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?

10 Ways to Use ChatGPT to Improve Your Copy: (With Simple Copy-and-Paste Examples) 1) Trimming Down Goal: Condense your copy for clarity and impact. Focus on: Complex sentences Redundant phrases Long paragraphs Example prompt: "Trim down this [phrase/sentence/paragraph] of my copy." 2) Finding Word Alternatives Goal: Find better synonyms for certain words to enhance readability and engagement. Look to replace: Fillers Jargon Clichés Adverbs Buzzwords Example prompt: "Provide [adjective] alternatives for the word [word] in this copy." 3) Doing Research Goal: Gather detailed information about your target audience to tailor your copy. Consider: Likes Habits Values Dislikes Interests Behaviors Challenges Pain points Aspirations Demographics Example prompt: "Create an ideal customer profile for [target audience]." 4) Generating Ideas Goal: Brainstorm multiple copy elements to keep your content fresh and engaging. Do this for: CTAs Stories Leads Angles Headlines Example prompt: "Generate multiple [element] ideas for this copy." 5) Fixing Errors Goal: Identify and correct any errors in your copy to maintain professionalism. Check for: Spelling mistakes Grammatical errors Punctuation issues Example prompt: "Check this copy for any [type] errors and suggest corrections." 6) Improving CTAs Goal: Make your call-to-actions more compelling and click-worthy. Play around with: Benefits Urgency Scarcity Objections Power words Example prompt: "Give me [number] variations for this CTA: [original CTA]." 7) Studying Competitors Goal: Gain insights from your competitors' copy to improve your own. Analyze their: CTAs USPs Offers Leads Hooks Headlines Example prompt: "Provide a breakdown of [competitor]'s latest [ad/email/sales page]." 8) Nailing the Voice Goal: Refine the tone and voice of your copy to align with your brand and audience. Consider: Target audience Brand guidelines Advertising channel Example prompt: "Make this copy [adjectives] to suit [target audience]." 9) Addressing Objections Goal: Anticipate and address potential customer objections to increase conversion rates. These could be about: Price Quality Usability Durability Compatibility Example prompt: "Analyze this copy to find and address potential objections." 10) A/B Testing Goal: Create variations of your copy's elements to determine what works best. Try different: CTAs Hooks Angles Closings Headlines Headings Frameworks Example prompt: "Generate variations of this [element] for A/B testing: [original element]."

One of the most practical AI use cases in eCommerce right now isn’t a chatbot or a fancy personalization layer. It’s predicting a shopper’s future LTV before you spend the budget, and routing spend toward the people most likely to buy again. This is what I learned recently from Pecan AI which is quite interesting to me. And because most teams can’t do that today, they keep allocating budget evenly and running broad promos, hoping it works. 𝐏𝐞𝐜𝐚𝐧 𝐂𝐨-𝐏𝐢𝐥𝐨𝐭 changes the workflow: • You define the goal (e.g. “Predict 90-day LTV by channel and creative”) • It builds the predictive model for you • Then outputs ranked audiences and campaigns to scale, cap, or test, pushed directly into the tools you already use (ad platforms, CRM, email) No dashboards. Just actionable predictions. 📚 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐭𝐡𝐞𝐲 𝐬𝐡𝐚𝐫𝐞𝐝: A DTC apparel brand had strong AOV but low repeats from a few ad sets. Pecan flagged those cohorts as low predicted LTV, capped spend, and shifted budget to a lookalike built from high-LTV buyers → ROAS went up and discount costs dropped. This is the kind of AI that actually drives growth, not just adds another layer of complexity. Demo link → https://hubs.la/Q03BJHTF0 #AI #ecommerce #predictiveanalytics #martech

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)

Is Data overload making a lot of chaos in real time?  Do you feel overwhelmed? 🔖 Leverage the capabilities of 𝐀𝐩𝐚𝐜𝐡𝐞 𝐊𝐚𝐟𝐤𝐚: ➖Throughput: Millions of messages per second ➖Latency: As low as 2ms ➖Data retention: Configurable, can retain data indefinitely ➖Scalability: Easily scales to handle petabytes of data daily ✅At its core, Kafka's architecture is elegantly simple yet powerful: -> Producers write events to topics (imagine high-velocity data streams from your applications) -> Brokers handle the heavy lifting of storing and replicating these events (ensuring nothing gets lost) -> Consumers read these events at their own pace (which is brilliant for decoupling systems) -> Topics are split into partitions (this is where the real scalability magic happens) Let's understand how to deal with real-time data and what functionalities it offers: 1. Identify proper streaming sources (logs, social platforms, customer activity) 2. Know the source data structures thoroughly 3. Implement appropriate connectors to extract data 4. To ingest and buffer the streaming data use Kafka 5. Transform raw data streams into organized formats 6. Design optimized consumption patterns for analytics and modeling Curious to understand why use kafka instead of other streaming framework? Key benefits of using Kafka for your real-time data pipelines includes - High throughput, Low latency, Persistence and scalability. What are the use cases that can make your data engineering journey with kafka? 1. Streaming Data: Real-time central hub for data like user activity in streaming services. 2. Centralized Log Management: Collects logs from many sources, like ride-sharing companies aggregating microservice logs. 3. Message Queuing: Enables asynchronous communication, like payment processors handling transactions. 4. Seamless Data Replication: Keeps databases in sync across data centers, used by large retailers globally. 5. Monitoring & Alerting: Tracks system health in real-time, like travel platforms monitoring user interactions. 6. Change Data Capture (CDC): Captures database changes quickly (milliseconds), used by professional networks. 7. System Migration: Smoothly transitions between systems, reducing risks for e-commerce platforms migrating billions of events. 8. Real-Time Analytics: Provides near real-time insights, like music streaming services personalizing recommendations. Explore these free projects: -> Stock Market real-time data analysis: Darshil Parmar - https://surl.lu/gtyknl -> Log Analytics Real-Time Data Pipeline: Shashank Mishra 🇮🇳 - https://lnkd.in/gFeJtK8V -> Real-time data streaming pipeline: Yusuf Ganiyu - https://surl.lu/hhrliz Image Credits: Shalini Goyal ▶️ Follow POOJA JAIN for more on Data Engineering! #data #engineering #kafka

Most marketing teams are still reporting on what happened. The most effective ones are acting on what is likely to happen next. In one case, a campaign exceeded every benchmark. Strong open rates, solid conversions, and clean dashboards. On the surface, it looked like a win. A few months later, revenue told a different story. The accounts that converted were not the ones that stayed. That is where the shift begins. Data alone is not the advantage. Interpretation is. Predictive conversion strategy changes the focus from past performance to future outcomes. It asks which prospects are most likely to convert, expand, or churn next. Planning around probability leads to sharper spend, cleaner pipelines, and more reliable forecasts. The real change is not more data or more reporting. It is precision. Understanding which segment drives the majority of revenue. Recognizing when high engagement does not translate to long-term value. Knowing when to rely on automation and when human judgment is needed. This week’s newsletter explores how to build a predictive system, rethink ROI through conversion velocity, and turn insights into measurable revenue impact. For teams ready to move beyond reporting and start anticipating results, it is worth a read.

Incrementality testing is crucial for evaluating the effectiveness of marketing campaigns because it helps marketers determine the true impact of their efforts. Without this testing, it's difficult to know whether observed changes in user behavior or sales were actually caused by the marketing campaign or if they would have occurred naturally. By measuring incrementality, marketers can attribute changes in key metrics directly to their campaign actions and optimize future strategies based on concrete data. In this blog written by the data scientist team from Expedia Group, a detailed guide is shared on how to measure marketing campaign incrementality through geo-testing. Geo-testing allows marketers to split regions into control and treatment groups to observe the true impact of a campaign. The guide breaks the process down into three main stages: - The first stage is pre-testing, where the team determines the appropriate geographical granularity—whether to use states, Designated Market Areas (DMAs), or zip codes. They then strategically select a subset of available regions and assign them to control and treatment groups. It's crucial to validate these selections using statistical tests to ensure that the regions are comparable and the split is sound. - The second stage is the test itself, where the marketing intervention is applied to the treatment group. During this phase, the team must closely monitor business performance, collect data, and address any issues that may arise.  - The third stage is post-test analysis. Rather than immediately measuring the campaign's lift, the team recommends waiting for a "cooldown" period to capture any delayed effects. This waiting period also allows for control and treatment groups to converge again, confirming that the campaign's impact has ended and ensuring the model hasn’t decayed. This structure helps calculate Incremental Return on Advertising spending, answering questions like “How do we measure the sales directly driven by our marketing efforts?” and “Where should we allocate future marketing spend?” The blog serves as a valuable reference for those looking for more technical insights, including software tools used in this process. #datascience #marketing #measurement #incrementality #analysis #experimentation – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWKzX8X2 

We just published new research on the TikTok Halo Effect and the results are hard to ignore. Most brands still measure TikTok Shop in isolation. Platform-level profitability. Did it 'work' on TikTok or not. That approach is fundamentally broken. We analysed aggregated data across TikTok Shop brands to understand what actually happens after someone discovers a product on TikTok. What we found: • TikTok Shop activity and Amazon sales show a strong correlation of ~0.86–0.87 once customer decision timing is accounted for • Amazon sales consistently rise 2–3 days after TikTok activity increases • On average, every £1 of TikTok Shop GMV is associated with ~£0.50–£0.60 of incremental Amazon revenue • TikTok is acting as a demand creation engine, not a standalone checkout channel In short: People discover on TikTok. They often convert on Amazon. And most attribution models miss this entirely. If you are judging TikTok Shop purely on same-day profitability, you are almost certainly underestimating its true impact. We published the full research here 👇 https://lnkd.in/ezWP3j6y This is exactly why cross-channel measurement matters in discovery-led commerce. Would be curious to hear how others are currently measuring TikTok’s downstream impact.

We just built a Claude skill that scans 227 demographic segments across a LinkedIn Ad account and tells you exactly where your budget is working and where it's being wasted. Takes about 90 seconds. Used to take our team at least 1 hour per client for each run. Here's what it actually does: It pulls every demographic facet LinkedIn tracks (geo, function, seniority, industry, company size, company name) and cross-references impressions, CTR, and conversions across all of them. Then it sorts every segment into three buckets: 1. Top Performers. High conversion rate segments you should be scaling into. These are your PRIORITIZE moves. The segments where real pipeline is coming from, not just clicks. In this example... CXO seniority is converting at 2.37%. CEOs at 2.74%. Canada as a geo is outperforming at 2.80% conversion rate. Business Development function is driving the most raw conversions at 66. Each one gets a specific recommendation. Not "looks good keep going." Actual next steps like "dedicated campaign candidate" or "best converting geo, scale." 2. Hidden Gems. High CTR segments with low volume that deserve dedicated creative testing. These are segments the algorithm is burying because they're small, but the engagement signal is screaming. GrowthMentor community: 0.976% CTR. RevGenius community: 0.927% CTR. Both with tiny impression volume because they're niche. Both worth testing with dedicated creative and budget. You'd NEVER catch these manually scrolling through Campaign Manager. 3. Budget Wasters. High impression share, zero conversions. These are your EXCLUDE moves. San Francisco County: 118K impressions. 0.196% CTR. Zero conversions. That's 3.17% of total impressions going to a geo that produces nothing. France, Spain, Alameda County... same story. Impressions burning, nothing converting. Immediate exclusion candidates. This is one of about 40 skills we've built for our account teams. Every skill answers one specific question. Not a dashboard. Not a generic "how's my account doing" report. One question, one answer, one set of actions. LinkedIn and tools like DemandSense give you the raw data. AI is helping speed up the process of turning it into decisions because it's then also based on our years of knowledge, frameworks, and playbooks. The agencies and teams that build these systems first will operate at a speed and precision that manual account management can't touch.