Best Practices for Digital Marketing Analytics

Attribution is overrated. Incrementality is what actually matters Every new-age brand wants to know what’s working. Meta ROAS is looking good. CAC is steady. Revenue is growing But here’s the truth: Your Meta ad might get the conversion. But did it cause the conversion? That’s the difference between attribution and incrementality. Most dashboards, attribution tools, and agency reports stop at attribution. But if you’re a brand selling across Amazon, Flipkart, GT, MT, Q-com, and D2C—pure attribution will always lie to you Because the sale might happen on Amazon. But it might have been nudged by a Meta video or a YouTube bumper ad 4 days ago. You don’t need a full-blown Marketing Mix Model to get started. There are simpler, street-smart ways to directionally understand what’s working—and what’s not. Here are 4 that have worked for us at Atomberg: 1. Geo Split Testing Pick two similar markets. Run campaigns in one. Don’t run in the other. Then track: • Branded search volume • Sell-through on marketplaces • Secondary sales from GT counters If the test market moves faster than the control, you’re seeing true lift. That’s incrementality. 2. First-Time Buyer Growth vs Returning Buyer Growth Track whether your growth is coming from first-time buyers or repeats. If your campaigns are just bringing back old customers—you’re not creating net new demand. But if there’s a spike in new buyers across Amazon, Flipkart, D2C—your campaigns are likely working at an incremental level 3. Paid Traffic vs Organic Trend Lines If paid traffic, clicks and spends are going up—but your organic sales or branded search isn’t moving—you’re likely just harvesting demand that already existed. But if organic lifts alongside paid—your ads are creating interest. Not just closing it. Directionally, this is one of the simplest sanity checks most teams ignore. 4. Channel Crossover + Offline Signal Mapping Your Meta ad may not show up in last-click attribution. But it might have nudged the consumer to visit your store or buy on Amazon. You can detect this through: • Post-purchase surveys (Where did you first hear about us?) • Branded search + store footfall spikes in campaign-active cities • And most powerfully—offline signals passed back to Meta At Atomberg, we pass back data from installations and warranty registrations—including pincode and purchase timelines Sometimes, we’re even able to identify this at a unique customer level through their cookies for warranty registration This has helped us understand true incrementality of perf marketing campaigns even for offline sales If you’re only measuring ROAS, you might scale what’s only taking credit for sale about to happen anyway If you chase incrementality, you’ll scale what’s working. For more details, read the full post- link in first comment.

(FMCG Blueprint) Sales forecasting in FMCG is both an art and a science. Let’s break it down using some basic matrices with a relatable example. Imagine we’re working for a brand that sells a spicy instant noodle, “HotBowl Ramen”. 1. Historical Sales Data (Your Crystal Ball) The first step is to look at past sales. For example: Month Sales (Units) January 10,000 February 11,000 March 10,500 April 12,000 Now, let’s assume you notice a 5% growth trend every month. For May, you might forecast: May Sales = April Sales * (1 + Growth Rate) = 12000 * (1 + 0.05) = 12600 Tip: This works well unless your sales suddenly nosedive because people discovered a new health fad: “No-Spice Life!” 2. Seasonality (Your FMCG Calendar) People eat more noodles in winter because “cozy food” vibes. Let’s adjust for seasonality: • Winter months: Add 10% • Summer months: Subtract 15% If your May forecast is 12,600 units but May is peak summer, adjust like this: Adjusted Sales = Base Sales * (1 - 0.15) = 12600*0.85 = 10,710 Reality Check: Your product is spicy. Some brave souls will still eat it even in May, sweating like they’re in a sauna. 3. Market Dynamics (Your Frenemy) Suppose your competitor, “MildBowl Ramen,” launches a huge promotion in May. You estimate a 10% impact on your sales. Final Sales Forecast = Adjusted Sales * (1 - 0.1) = 10710*0.9 = 9,639 4. Promotional Impact (Buy One, Cry One Free?) Now, your marketing team swoops in with a “Buy 1 Get 1 Free” promo. Promotions can boost sales by 20%, so: Promo Adjusted Sale = 9639*1.2 =11,566.8 Realistic Case Summary Step Forecasted Sales Base Sales Forecast 12,600 Seasonality Adjustment 10,710 Competitor Impact 9,639 Promo Impact 11,566 Funny Perspective Imagine your boss: • Before Forecast: “We need 15,000 units this month!” • After Your Analysis: “Hmm… okay, but let’s add another promo to reach 12,000 at least!” Your real hero? The customer who eats your spicy noodles even in May, sweating but happy. Moral: Forecasting is like cooking ramen—balance your ingredients (data) and adjust for taste (market trends)!

I spent three years as a CRO with marketing reporting to me. We hit our numbers. The board was happy. Our attribution looked clean. But over time, I realized something about marketing. We were hitting our short-term goals while quietly stripping marketing of its purpose. When marketing reports into sales, measurement becomes a filter. Everything is judged on short-term pipeline impact. Campaigns that don’t convert fast enough disappear. And without meaning to, you stop speaking to 95% of your market, the people who aren’t ready to buy yet but will remember you when they are. I still remember one campaign that showed this clearly. It ran for 90 days, targeting new accounts in our key segment. Pipeline impact? Almost none. But six months later, those same accounts started showing up in inbound forms, webinars, and sales conversations. The campaign hadn’t failed; our lens of measurement had. That experience changed how I think about marketing and data. If marketing wants to stay strategic, it needs to take back ownership of how results are captured and shared. It starts with knowing what to measure. Not every touchpoint leads to revenue right away, but every meaningful interaction contributes to it. The right tools connect data from across CRM, ad platforms, and automation systems, and then translate that into a story that leaders can actually trust. That story matters. Because a graph can show spend and ROI, but a connected dataset can show influence, intent, and momentum. That’s what boards and finance teams understand when they see how marketing moves the business forward, not just that it did. We speak with hundreds of CMOs every year at Dreamdata who are trying to close that credibility gap. They’re not looking for another dashboard. They’re looking for measurements that help them show their impact clearly, so they can protect their ability to invest in the work that shapes future growth. That’s what real alignment looks like. Not marketing reporting into sales, but both functions working from the same truth, with data that earns trust instead of questions.

𝐋𝐞𝐭’𝐬 𝐬𝐨𝐥𝐯𝐞 𝐚 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐂𝐚𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫, If you're preparing for Data or Product Analyst roles — this is exactly the type of case round you should practice. It’s not about jumping into queries — it’s about structured thinking. 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨: You're a Data Analyst at a food delivery company like Zomato or Swiggy. In the past 15 days, there’s been a 5% drop in active customers. You’re asked: “What could be the reason behind this churn, and how would you investigate it?” 𝐒𝐭𝐞𝐩 𝟏: Clarify the Problem Before solving, ask: Does “churn” mean no orders? Or no activity at all? Is it across all users or specific cohorts (new users, Prime, etc.)? Any specific regions more impacted? These questions help you define the problem — not just guess a solution. 𝐒𝐭𝐞𝐩 𝟐: Structure Your Investigation Break down your thinking into: 🔹 Internal Factors (platform-level issues) App crashes, login issues → Check crash logs, screen exits Delivery delays → Compare SLA metrics over time Key restaurant unavailability → Partner downtime, stockouts Reduction in discounts → Drop in coupon usage or redemptions Checkout issues → Cart-to-payment funnel drop-offs 🔹 External Factors (outside control) Weather/strikes/curfews → Regional impact data Seasonality → Historical trends from previous years Competitor activity → Market-level discounts or ad campaigns 𝐒𝐭𝐞𝐩 𝟑: Go Deep on the Root Cause Let’s say the team confirms: “Yes, we reduced discount campaigns.” Now prove it with data: Analyze sessions reaching the “Apply Coupon” page Compare order completion rate before & after discount application Study cart abandonment after coupon screen Look at this metric over last 15 days vs previous months This validates the impact of discounts on churn — using real funnel data. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲? Case rounds like this aren’t about correct answers. They’re about how you think, how you structure messy problems, and how well you connect business context with data. This is the exact type of round I’ve seen in companies like Zomato, Blinkit, Flipkart, Meesho, etc. So if you’re preparing — don’t stop at SQL or dashboards. Practice thinking like a business analyst. If you want more real case problems like this — drop a “Case” in comments. I’ll share a few more from my interview experience.

Marketing is shifting fast as public web data meets AI. Teams can finally see markets in motion instead of snapshots and act on what is happening now. In my conversation with Yanay Sela from Bright Data on The Ravit Show, we walked through the full flow. Start with the big picture. Public web data fuels better targeting, sharper research, and faster creative cycles. AI turns that stream into insight you can use. Most teams still have blind spots with external data. Coverage gaps, stale sources, and messy formats slow them down. The fix is disciplined collection, clear permissions, and pipelines that keep data fresh and usable. Where does the lift show up. Ad spend gets smarter when you see real demand signals. ROI improves when research reflects live market shifts, not last quarter. The strongest results come from combining broad web signals with your first party data. Competitive intelligence is now real time. Customers track pricing, assortments, launches, and promotions across markets and act on what they find. That means faster reactions, better positioning, and fewer surprises. GenAI sits on top of this. Assistants can research competitors, summarize changes, draft briefs, and cite sources. End to end workflows move from manual scraping to guided analysis that is transparent and repeatable. Decisioning is the last mile. Signals like price changes, stock status, ratings, and content shifts improve attribution and budget moves. Bright Insights helps place these signals into a modern stack so they flow into dashboards and planning tools. #data #ai #bigdataldn #brightdata #theravitshow

Most businesses treat paid media like a fire report. They wait for the monthly numbers. - They review the P&L. - They analyse what happened. But reports tell you what already burned. Paid media is not just demand capture. - It is a signal engine. - Rising CPCs are heat. - Falling CTR is friction. Conversion rate shifts are airflow changes. Search query trends are early sparks. Those signals appear weeks before revenue moves. When paid sits in a silo, the signals stay trapped in the channel. - Product does not adjust. - Creative does not rotate fast enough. - Finance sees margin pressure after decisions have compounded. Separation slows learning everywhere. High-performing teams use paid media like a smoke alarm. - They monitor signal quality, not just outcomes. - They move budget when margin bands tighten. - They refresh creative before fatigue spreads. - They feed demand signals back into pricing and product. The goal is not to report on damage. The goal is to respond before it spreads. Paid media is not just about capturing demand. It is about detecting change early enough to act. #PaidMedia #RetailMarketing #PerformanceMarketing #DecisionQuality

I think we’re measuring the wrong stuff… and it’s quietly killing momentum. 2026 has to be the year we fix it. Impressions. Clicks. MQLs. “Engagement.” The real game is happening in DMs, Slack threads, forwarded newsletters, and meetings. Here are 6 metrics I’d focus on in 2026 GTM (and why they matter). 1) Conversations → conversions What it is: Of the conversations your content starts, how many turn into a real next step (intro, meeting, opp). Why it matters: Content doesn’t “generate leads.” It generates conversations. Pipeline comes from what you do next. How to track: Tag every inbound convo (DM/email/reply) and mark the outcome: no fit / nurture / meeting / opp. 2) REAL ICPs engaging with content What it is: Not “engagement.” Engagement from the right people (titles, seniority, company tier, intent). Why it matters: 1 CFO at a target account > 1,000 random likes. How to track: Maintain an ICP list (titles + account tiers) and measure: % of engagers who match ICP of target accounts engaged per week repeat ICP engagers (X touches in 30 days) 3) Brand mentions inside ICP-relevant conversations What it is: How often your brand comes up when your ICP is discussing the problem you solve (not when you post). Why it matters: This is the difference between “content that performs” and a brand that gets recommended. How to track: Collect signals: customer calls (“we heard about you from…”), community moderators, partner chatter, dark social screenshots, and sales intel. Even a simple monthly “mention log” works. 4) Conversation velocity What it is: The speed from publish → first qualified conversation, and from convo → meeting. Why it matters: Velocity is the earliest indicator your messaging is landing. If it’s slow, you’re not sharp enough yet. How to track: time-to-first-ICP-convo after a post/report time-to-meeting after first touch “conversation depth” score (comment → DM → problem share → meeting ask) 5) Brand + category position What it is: Are you being associated with a clear “lane” (category/point of view) or just “a vendor who posts”? Why it matters: In 2026, positioning is distribution. If people can’t summarize your POV in one sentence, you’re invisible. How to track: Quarterly “message recall” check: ask prospects/customers: “What do we do?” “What do we believe?” “What are we known for?” 6) Dark social + word-of-mouth What it is: The off-platform sharing that actually drives deals: forwards, screenshots, Slack drops, “my friend sent me this.” Why it matters: A huge percentage of B2B buying happens in private. If your GTM can’t see dark social, you’re flying blind. How to track: “How did you find us?” (mandatory field) inbound screenshots / Slack mentions private replies after posts If your 2026 GTM dashboard doesn’t include conversations, ICP quality, dark social, and category position, it’s going to keep optimizing for attention… while someone else captures intent.

Your marketing team is guessing what your sales team already knows. I see it every single week: Marketing creates campaigns. Sales talks to customers. Zero collaboration. Wasted opportunity. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: - Marketing creates personas (guessing) - Sales hears actual pains (knowing) - Marketing writes messaging (guessing) - Sales handles objections (knowing) - No information sharing - No collaboration - No growth 𝗧𝗵𝗲 𝗱𝗶𝘀𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝗰𝗿𝗶𝘀𝗶𝘀: Your marketing team creates content, campaigns, and messaging based on assumptions, marketing research, and industry reports. In contrast, your sales team has actual conversations every single day with prospects who share their real pains, objections, and buying criteria. Yet somehow, these valuable insights never make it back to influence marketing strategy. [𝐖𝐚𝐭𝐜𝐡 𝐭𝐡𝐢𝐬 𝐰𝐚𝐥𝐥 𝐜𝐥𝐢𝐦𝐛𝐢𝐧𝐠 𝐯𝐢𝐝𝐞𝐨] One person creates the foundation and the other leverages it to reach new heights. Your sales and marketing teams need to function as a single unit. Sales should provide real-world insights and direct customer language, while marketing should amplify and scale these proven messages through channels that reach more people. 𝗧𝗵𝗲 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: 1. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐡𝐚𝐫𝐞𝐝 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 Not separate worlds: - Weekly sales-marketing sync - Marketing joins sales calls - Sales reviews all content - Customer language documented 2. 𝐁𝐮𝐢𝐥𝐝 𝐂𝐨𝐦𝐦𝐨𝐧 𝐆𝐨𝐚𝐥𝐬 Unite the metrics: - Pipeline over MQLs - Revenue over activities - Quality over quantity - Customer success over volume 3. 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 Loop Make it systematic: - Sales validates personas - Marketing tests messages - Results shared transparently - Continuous improvement 𝗬𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗽𝗹𝗮𝗻: 1. Schedule weekly sales-marketing sync 2. Create a shared customer language doc 3. Have marketing join sales calls 4. Build a unified dashboard Remember: Like those wall climbers, Neither one could make it alone. But together, they're unstoppable. ---- ❤️ 𝐈𝐟 𝐲𝐨𝐮 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐭𝐡𝐢𝐬. ♻️ 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐧𝐞𝐭𝐰𝐨𝐫𝐤. 🔔 Follow me for more helpful and entertaining videos to improve your go-to-market approach. 🤟

ROAS↓ CAC↑ Most performance marketers know what to do next: check the funnel. But.. Even then, they miss something obvious. Let me explain. Let’s say I ask, “ROAS has dropped by 30%, what do you do?” They jump straight to diagnosing funnel drop-offs. They spot that click to payment has gone down. And now they get obsessed with the landing page or the journey post-click. But here’s the thing: Just because click-to-payment fell doesn’t always mean it’s a landing page problem. It could also mean: → The kind of users landing on the page have changed. → The quality of traffic has dropped. So while fixing that specific funnel step is important… Don’t go blind to the steps before it. Sometimes, the issue isn't in the funnel. It's in the audience that enters it. Diagnose holistically. Not just where the leak is showing but where the leak might be starting. Now, next time if someone says CTR% has gone down Let's jam all the possible reasons other than creative in comment 👇 #PerformanceMarketing #MetaAds #FbAds #GoogleAds

Not all skills are created equal. To thrive in data analytics, you need to focus on the essentials first. Here’s how to separate what’s critical from what’s good to have: 𝐌𝐮𝐬𝐭-𝐇𝐚𝐯𝐞 𝐒𝐤𝐢𝐥𝐥𝐬: ↳ SQL for querying databases. ↳ Excel for quick data exploration. ↳ Data visualization tools like Tableau or Power BI. ↳ Communication skills to translate data into business actions. ↳ Problem-solving mindset to focus on insights, not just numbers. 𝐍𝐢𝐜𝐞-𝐭𝐨-𝐇𝐚𝐯𝐞 𝐒𝐤𝐢𝐥𝐥𝐬: ↳ Experience with ETL tools. ↳ Big data tools like Hadoop or Spark. ↳ Cloud platforms like AWS, GCP, or Azure. ↳ Domain knowledge in the industry of your interest. ↳ Python or R for advanced analytics and automation. Remember, your career journey can elevate these "𝐧𝐢𝐜𝐞-𝐭𝐨-𝐡𝐚𝐯𝐞𝐬" into "𝐦𝐮𝐬𝐭-𝐡𝐚𝐯𝐞𝐬." Focus on your goals and prioritize learning accordingly! Which skills are you currently working on? What else would you add?