Demand Generation Insights

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.

How to sell (without feeling salesy): First, understand the Ethical Wealth Formula: (Value First × Trust Building) × Authentic Positioning  ———————————————————  Frequency of Asks × Pressure Tactics This isn't abstract theory. It's practical math: • Increase the numerator: deliver more value, build more trust, position more authentically • Decrease the denominator: reduce frequency of asks, eliminate pressure tactics • Watch revenue soar while your integrity remains intact Ethical doesn't mean unprofitable. It means sustainable. Principle 1: Value-First Monetization The approach that generates $864,000 monthly without a single "hard sell": • Deliver so much value upfront that buying feels like the obvious next step • Create free content so good people say "If this is free, imagine what's paid" • Solve small problems for free, big transformational problems for a fee Give until it feels slightly uncomfortable. Then give a little more. Principle 2: Trust Through Consistency I've never missed weekly content in 3 years, through vacations, illnesses, market crashes. The trust-building machine that works while you sleep: • Show up reliably when competitors disappear during tough times • Do what you promise, when you promise it • Maintain quality across every touchpoint One founder implemented this and saw conversions increase 74% in 30 days, without changing offer or price. Trust isn't built in grand gestures. It's built in boring consistency, most won't maintain. Principle 3: Authentic Positioning The approach that helped me raise prices 300% while increasing sales: • Own your expertise unapologetically, confidence is not arrogance • Speak to specific problems you solve, not vague benefits you provide • Tell detailed stories of transformation instead of listing features You don't need to be perfect to sell effectively. You need to be authentic about how you help. Principle 4: Invitation Vs. Manipulation The ethical alternative to high-pressure tactics: • Invite people when they're ready, don't push when you're ready • Create genuine scarcity (limited capacity) not fake urgency (countdown timers) • Respect "no" as "not now" rather than objection to overcome My most profitable sales sequence has zero countdown timers, zero artificial scarcity, zero pressure. Ethical selling feels like extending help, not hunting prey. — Enjoy this? ♻️ Repost it to your network and follow Matt Gray for more. Want to improve your sales strategy? Join our community of 172,000+ subscribers today: https://lnkd.in/eTp4jain

FMCG brands like Mamaearth, Emami Ltd, and Dabur India Limited are capturing rural India through effective marketing. If you want a share of the 35% of total sales from this market segment, then follow this roadmap! The rural FMCG sales are increasing at 7.6% compared to 5.7% in urban areas. And this sector will continue to grow because the government has allocated ₹2.66 lakh crore for rural development and ₹1.52 lakh crore for agriculture. Also, the higher crop prices and a good monsoon forecast, shows a potential 6-7% volume growth from rural consumers in fiscal year 2025. But urban-centric strategies won't work in these markets. You need to think out of the box to reach this segment. Here are 3 strategies that every brand should follow: 1️⃣ Hyper-localized content: Adopt local cultures into your campaigns. Take Coca-Cola's 'Chota Coke' campaign for instance. In this campaign, they featured Aamir Khan in different regional looks. It boosted their rural reach from 9% to 25% within one year. 2️⃣ Mobile-first campaigns: Use SMS marketing and WhatsApp broadcasts because more people are now using phones. Take HUL’s Kan Khajura Tesan campaign for instance. They created a radio channel and run product ads while providing free entertainment. 3️⃣ Audio marketing: Use radio or voicemails to create audio content. This is a powerful way to build brand recall because of low literacy in rural India. horlicks did this well by partnering with mSamvaad and using actor Ravi Kishan’s voice, reaching 4 million unique users. By combining these strategies, FMCG brands can effectively tap into the growing rural market. And the festive season is coming, so this is the right time to create campaigns P.S. Which campaign you remember worked really well in rural India? #FMCG #RuralSales #Marketing

Demand gen is utterly broken. It's overly complicated & lacks the soul and creativity that consumers deserve. I promise there's a better way. Here's the 3-pronged content engine we're building for companies at storyarb: Principles: - Treat your content as the product, not as marketing for another product - Unique insights + Unique voice + Unique packaging = Unique content - Pick topics that make your Market of 1 better at their job Channels: 1) Deeply researched long-form content Purpose: create data-driven OR interview-based website content that is deep enough & insightful enough such that a reader feels the need to bookmark & reference later. Good examples: Lenny Rachitsky: "How the biggest consumer apps got first 1,000 users" - Lenny interviewed hundreds of founders, identified patterns, and broke down the seven strategies consumer apps used to grow. Carta: "State of Private Markets: Q3 2024" Report - Using tons of internal funding data by Carta customers to pull together trends in startup funding for the quarter. HubSpot: "My First Million's Business Idea Database" - Aggregating & organizing 57 startup ideas shared by past MFM podcast guests into an e-mail gated database 2) Editorial email newsletter Purpose: create the best industry read for your market of 1 that allows you to build an owned audience of current/future customers. Good examples: - Content Examined by Alex Garcia: the best read for consumer content marketers, which acts as a perfect nurturing tool for his community, course, and agency - Big Desk Energy by Tyler Denk 🐝: a window into building a high-growth startup as it's happening by the founder of beehiiv - Exploding Topics: a snapshot of 4 emerging trends (based on google search data) that founders & investors should be aware of. 3) Personal brand social content Purpose: allow your market of 1 to build a parasocial relationship with your company through 1-4 personalities (execs, founders, etc) who enable connection with your faceless brand. Good examples: - Adam Robinson: fully transparent monthly breakdowns of his companies' (Retention.com & RB2B) performance with lessons learned & plans to fix key issues - Peter Walker: Head of Insights at Carta uses first party data from the company to share unique startup ecosystem trends + his own POV - Kieran Flanagan: AI & GTM expert who shares deep marketing insights, playbooks, and predictions that help build his & HubSpot's brand If you want help building this 3-pronged engine at your company, shoot me a DM or email at alex@storyarb[.]com.

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

Chobani built a $20B brand on a product they didn't invent. FAGE had Greek yogurt in America first. Better product. More time. More credibility. That's not a knock on FAGE. It's one of the most important demand gen lessons in CPG. When Chobani launched in 2007, Greek yogurt was less than 1% of U.S. yogurt sales. FAGE had been quietly selling it here for years. They had the product. The quality. The head start. But FAGE was harvesting demand from people who already knew what Greek yogurt was. Chobani went to work creating it. 𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝘁𝗲𝗹𝗹 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆: → Greek yogurt went from under 1% to nearly 50% of U.S. yogurt sales in about a decade → Chobani hit $1B in revenue in about four years → Chobani is now valued at $20B+ → FAGE is still profitable and respected, but it's not the name people say first 𝗦𝗼 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱? FAGE optimized for the consumer who already understands Greek yogurt. Chobani optimized for everyone else. • Pushed into the regular dairy set, not the specialty aisle • Priced accessibly • Expanded into Walmart, Kroger, Target, and club channels before the category was cool • Engineered trial through sampling and promotions at scale • Let the product close That's demand generation: not waiting for people already shopping the niche, but creating new buyers and making your brand their default. 𝗧𝗵𝗲 𝗱𝗲𝗺𝗮𝗻𝗱 𝗴𝗲𝗻 𝗹𝗲𝘀𝘀𝗼𝗻: Most brands optimize for better creative, smarter targeting, tighter spend. That's demand harvesting. Chobani did something different. They built demand that didn't exist yet. The question isn't "Do we have a great product?" It's: are we building a system that creates new demand, or just competing for the same buyers on repeat? FAGE built a premium brand. Chobani built a demand engine. Category creators set the table. Category capitalizers fill every seat. Is your brand harvesting demand or creating it?

Ask ten people if they would buy your product. Then tell those who said “yes” that you have ten units in your car and ask them to buy. The initial positive responses, given by people who want to be liked and aim to please, become polite refusals as soon as real money is at stake. To get an accurate indicator of commercial viability, don’t ask people if they would buy—ask them to buy. The response to the second is the only one that matters.

How to improve marketing performance without sending more money! Most marketing decisions are influenced by incomplete data. Platforms only show part of the picture. The solution is not to chase perfect attribution but to build reliable data pipelines. Use consistent tagging, first party tracking, server side setups, and unified reporting. Start by identifying which numbers you trust. Then build a framework that reduces variation. Use trending rather than single day data. Compare channels by movement, not isolated metrics. When your data is consistent, your decisions become faster and more reliable. Strong marketing is built on strong data, not guesswork. https://lnkd.in/dEg93Pj #data #analytics #digitalmarketing #marketingstrategy #ppc

Inflation isn't just about rising prices; it's a catalyst for changing consumer behaviors. As purchasing power shifts, businesses must adapt swiftly to meet evolving demands. Hindustan Unilever Limited (HUL), a leader in the FMCG sector, showcases how embracing AI can turn these challenges into opportunities. 📌 The Challenge #HUL observed significant fluctuations in demand across its diverse product portfolio during inflationary periods. Premium products experienced slower sales, leading to overstock situations, while budget-friendly items frequently faced stockouts. Traditional forecasting methods, relying heavily on historical sales data, struggled to keep pace with these rapid changes in consumer preferences. 📊 The Solution: AI-Driven Demand Forecasting To address this, HUL integrated AI-powered analytics into its demand forecasting processes. This advanced system enabled the company to: Analyze Real-Time Consumer Behavior: By examining current purchasing patterns and consumer sentiment, HUL could detect emerging trends and shifts in preferences. Incorporate External Economic Indicators: The AI model factored in various economic indicators, such as inflation rates and consumer confidence indices, to predict their impact on product demand. Optimize Inventory Management: With precise demand forecasts, HUL adjusted its inventory levels accordingly, ensuring optimal stock across all product categories. 🔹 Key Insight: The AI-driven approach revealed that demand for budget-friendly products was increasing at a rate three times higher than traditional models had predicted, while premium product sales were declining in specific regions. 📈 The Impact 20% Reduction in Unsold Premium Stock: By aligning inventory with actual demand, HUL minimized excess stock of premium items. 35% Improvement in Stock Availability for Budget-Friendly Products: Ensuring that high-demand, cost-effective products were readily available led to increased customer satisfaction. Enhanced Revenue and Profit Margins: Optimized inventory management reduced holding costs and prevented lost sales, positively impacting the bottom line. 💡 The Lesson In times of economic uncertainty, relying solely on historical data can be a pitfall. HUL's proactive adoption of AI-driven demand forecasting exemplifies how leveraging advanced analytics allows businesses to stay agile and responsive to market dynamics, ensuring they meet consumer needs effectively How is your organization utilizing data analytics to navigate market fluctuations? #datadrivendecisionmaking #businessstrategies #dataanalytics #demandforecasting

More content isn’t the answer. More relevancy is. But most coaches do the opposite.  They post more.   Chase volume to the sky.   And hope trendy cheat sheets grow revenue.  The shift doesn’t happen with excess.   It happens with certainty.  Certainty in what you say. How you say it. How you shape it.  It shows the path you build for buyers to follow. Lots of posts float aimlessly: Random tips. No real direction. Scattered insights. But the best content: leads. It creates a pre-buy experience so strong, prospects don’t need convincing. They’ve already made the decision.  And this is before they get on a call. So that when they join, it's high intent. That’s what happened when I fixed Chris' content. Instead of posting just to stay visible, we built a strategy. Every post spoke to his buyer. It was all bottem of funnel vs fluff. Every piece moved buyers 1 step closer. And in 30 days a $35,000 sales pipeline. Sure, he still had to actually close the deals.  But imagine having that level of pipeline. That's how you'll stabilize revenue. Because the best content doesn’t just educate. It targets the buyer every time.   And directs them deeper. It makes decisions easier. It has story (for relevancy) + education (for hope). The coach who posts randomly isn’t the one who builds demand. Because the goal isn’t to say a lot.   It’s to say what actually matters.