Target Audience Research

What makes India’s festive season so unique is how it reveals consumer behavior in real time. Flipkart’s Big Billion Days 2025 offered a perfect example. In the first 48 hours, the platform clocked 606 million visits, a 21% jump from last year, with unique visitors rising 28% year on year. Gen Z alone contributed 201 million visits, growing at twice the overall pace, while metros still expanded by 23%. The most striking story was in appliances. Through early September, shoppers browsed but held back. Once the sale started and GST 2.0 reforms became visible, demand surged overnight, making appliances the fastest growing category. Beauty and personal care had already seen 20–30% unit growth before the festive week and accelerated further during BBD, especially from tier-2 and tier-3 towns. Fashion reflected two different tracks, with Millennials driving branded categories and Gen Z chasing what trends. To me, this year’s numbers highlight how Indian consumers are no longer impulsive. They are informed, deliberate, and confident, waiting for the right moment when policy, timing, and value align to act at scale. #EcommerceIndia #FestiveEconomy #ConsumerBehavior #GenZShoppers #MillennialTrends #DigitalIndia #GSTReforms #RetailInsights https://lnkd.in/gFtGuadx

Most teams think of predictive models as answer machines. You ask a question, you get a score. Will this customer churn? How likely is this lead to convert? That's valuable. But it's only half the story. The real gold sits in the SHAP values behind each prediction. SHAP (SHapley Additive exPlanations) breaks down exactly which variables pushed a prediction in each direction, for every single customer or record. Not just "this customer is likely to churn," but why. Was it their purchase frequency? The channel they came from? How much revenue they've generated? Think of it as doing BI on AI. When you analyze SHAP values with a business lens, you stop looking at individual predictions and start seeing patterns. You can identify entire segments that share the same risk drivers. Maybe your high-revenue customers from one acquisition channel are three times more likely to leave than those from another. That's not just a prediction. That's a strategy. This is one of the most overlooked benefits of having a strong predictive model in place. The predictions tell you what's coming. The SHAP values tell you what to do about it. Want to go deeper? Here's a solid breakdown of how SHAP values work: https://lnkd.in/dxMMyhFH

Consumers are not spending less. They are spending deliberately according to our recent IBM Institute for Business Value 2026 Consumer Research Study ‘Own the agentic commerce experience - Consumers are ready’. Economic pressure is widespread. More than half of consumers globally feel it, including 39% of affluent households. Yet behaviour is not simply defensive. One in three consumers report trading down to cheaper alternatives. 32% say they actively make trade offs to stay within budget. 29% are buying more private label. At the same time, 25% still choose trusted brands even when they cost more. Among Affluent AI Leaders, that rises to 41%. Among Conscious Connectors, 30%. One in five consumers describe themselves as price sensitive in some categories while selectively indulging in others. The lipstick effect is real, but more intentional. Wellness is a major anchor. 30% say diet and nutrition shape purchasing. 26% cite health and fitness goals. Health and wellness and beauty categories are now seen as essential on par with groceries and household goods. This matters because these behaviours generate the data that will train shopping agents. AI will learn not just what people buy, but when they compromise and when they refuse to. Value is no longer cheap versus expensive. It is justified versus not. — The IBM #IBV is the global number one rated consulting thought leader that delivers research led insight at the crosshairs of business, technology society. Sign up to the IBV here: https://lnkd.in/eav5Dc6R Our Consumer 2026 report combines surveys of 18,000 consumers across 23 countries and 200 retail and consumer products executives across 11 countries to examine how AI enabled shopping, trust and precision spending are reshaping commerce. Read the report here: https://lnkd.in/eCvGijDa The paper was authored by me and Dee Waddell, Richard Berkman, Hiroshi Hasegawa, Carlos Capps, Sabu Gopinath, Joe Dittmar, Milad Safadi, Jeremy (Jez) Bassinder, Shantha Farris and led by the inimitable Jane Cheung, our Global Leader for #ConsumerIndustries at the IBV. We are extremely grateful to the Industry Leaders who contributed to this report including Katherine Cullen of The National Retail Federation, Byron Ells of Sobeys, Matthieu Houle, CIO, ALDO Group, Stanislas Vignon, Head of Insights at Louis Vuitton Moët Hennessy (LVMH) as well as the numerous other clients who were interviewed. Also the IBM contributors: Hugo Catarino, Pierre Charchaflian, Kostas Didaskalou, Karl Haller, Mark Innes, Colm O'Brien, Mary Wallace, and the IBV team Sara Aboulhosn, Steve Ballou, Douna Daou, Kathy Martin, Thiago Sartori and Joanna Wilkins. #AgenticCommerce #AIinRetail #ConsumerBehaviour #AI #Retail #ConsumerProducts #CommerceStrategy #Luxury #RetailInnovation #AICommerce #DigitalCommerce #CustomerExperience #ItsAGreatTimeToBeAnIBMer #IBMIBV #Consumer2026

Businesses often want to exclude people their business already has a relationship with: - Platform users. - Current customers/prospects. - People who've already converted. But how to do it? Many exclusions can be done by using native targeting but the above type of exclusions require a little bit more work. Some options you have: #1. Website Actions: The ability to create audiences from Buttons now allows you to accurately remove anyone who completes a certain behaviour on your site. You can exclude end users by creating an audience from a button such as people who've clicked 'Login' or 'Submit' or/ pages such as your 'User portal' or 'Member portal'. #2. Website Retargeting: Similar to Website Actions you can create an audience from anyone who completes a URL based behaviour. A good example is you can create an audience of people using your product or community by using the unique URL identifier. Example below: Website URL 'contains= memberportal'. #3. Manual list uploads: Contact List: Upload a list of contacts from your CRM. However, this will have a low match rate (~30%) as it matches by email address. Company List: This will have a high match rate but may exclude the whole company and take away the opportunity to cross-sell to different departments in complex organisations e.g. Amazon vs AWS vs Audible. #4. Third-party integrations Rather than upload your lists manually every week. You can integrate with one of the third-party partners of LinkedIn (Hubspot, Salesforce etc.) to create dynamic lists that update in Campaign Manager automatically. This way you can have a list uploaded that constantly feeds to LinkedIn exclusions from your CRM/Martech. Implementing the above will go a long way to helping you remove waste. But, something to keep in mind is that there are plenty of situations where these audiences should continue to see your ads. Especially if they're only at the pipeline stage. #linkedinads

👉 Leveraging Behavioral Economics in Digital Transformation In today's fast-paced digital landscape, understanding how behavioral economics can drive user engagement and digital adoption is crucial for businesses looking to thrive. 📱 Behavioral Economics and Digital Interfaces: When designing digital products, such as apps or websites, the principles of behavioral economics are invaluable. ▪️ For example, the Endowment Effect plays a crucial role here. This effect suggests that once users feel ownership of a part of your digital platform, whether it's a customizable profile or tailored preferences, they are more likely to continue using and valuing the product. ▪️ Example of Default Choices: Another powerful application is the strategic use of default settings, which leverage the Status Quo Bias. By setting eco-friendly choices as default options in an app's settings, companies have successfully nudged users toward greener behaviors without diminishing user satisfaction. ▪️ The Power of Micro-Commitments: Encouraging users to make small commitments early in their digital journey can lead to higher engagement rates. This strategy uses the Commitment Bias to subtly guide users to gradually adopt more features or services. ▪️ Practical Takeaway: Next time you interact with a digital platform, notice how certain designs or default options seem to guide your choices. These are not random; they're carefully crafted using behavioral economics to improve user experience and engagement. #DigitalTransformation #BehavioralEconomics #TechInnovation #ServingMarketing #SirviendoMarketing

80% of influencer marketing campaigns focus on mainstream audiences but overlook one crucial opportunity: tapping into niche communities with untapped potential. Did you know by only collaborating with influencers who have mass appeal, you’re actually missing out on hyper-engaged audiences that drive higher conversions? Think about it like this instead: Identify micro and nano influencers: These creators often have smaller but highly engaged audiences in niche spaces, from sustainable fashion to vegan food to gaming in India - you name it and there are creators in every niche. Leverage local experts: Regional influencers who speak to hyper-local audiences can create trust and resonate in ways mass campaigns cannot. Don't under-estimate the power of regional creators. Focus on community-driven content: Collaborate with influencers who actively engage with their followers through Q&A sessions, polls, or live discussions��building authentic connections within niche groups. But this won’t happen if you rely solely on follower counts or trending influencers without assessing audience relevance. The goal is to drive meaningful engagement and conversions by reaching the right people, not to dilute your message in a sea of uninterested followers. Ready to uncover hidden opportunities in influencer marketing? Start by researching untapped niches where your brand’s message truly matters. What’s the most niche audience you’ve targeted with influencer marketing? Let’s discuss your experience in the comments!

Predict, Personalize & Perform : From Leads to Loyalty Let’s be honest—customer lifecycle marketing (CLM) in B2B used to be a fancy word for “email nurture” and “CRM segmentation. But today, with AI, machine learning, and predictive data models, CLM is becoming something much more powerful: ➡️ A living, learning ecosystem that adapts to each buyer journey in real time. Here’s how we’re seeing AI and ML revolutionize CLM in B2B: 🔍 1. Predictive Journey Mapping Machine learning algorithms are helping identify where an account or contact actually is in the funnel—not just where your CRM says they are. ✅ No more generic MQL > SQL flows ✅ Dynamic scoring based on behavior, content engagement, and intent signals ✅ Real-time stage shifts based on predictive fit and readiness — 📈 2. Hyper-Personalized Nurturing (at Scale) AI models now create content clusters matched to personas, industries, and even buying committee behavior. 🎯 Email sequences, LinkedIn ads, and landing pages are personalized based on: Buyer role Past touchpoints Predicted product interest ICP match + firmographic data It’s not just segmentation—it’s micro-personalization powered by behavioral AI. — 🔁 3. Intelligent Retargeting & Re-Engagement Using ML-powered intent data and anomaly detection, you can now: Spot churn risks before they happen Trigger re-engagement sequences based on drop-off patterns Retarget accounts that show subtle buying signals across web, search, and social Retention is no longer reactive. It's predictive. — 📊 4. Revenue Forecasting + Attribution Modeling Thanks to data science, we can model: Which touchpoints actually move pipeline Which leads are likely to convert within a time window How to attribute revenue across full-funnel programs—not just the last touch This gives marketing the credibility and confidence we’ve needed for years. — 💡 The CLM Stack of a Modern B2B Org Should Include: ✔️ Customer Data Platform (CDP) ✔️ AI-powered segmentation + scoring ✔️ Predictive content engines (LLMs + RAG) ✔️ Lifecycle orchestration tools (e.g. Ortto, HubSpot, Marketo w/ ML layers) ✔️ Analytics + BI layer for optimization 🧠 Final Thought: In 2025, CLM isn’t just “marketing automation” with better templates. It’s about building an AI-powered engine that understands, anticipates, and activates each step of the buyer journey. You don’t need more content. You need smarter orchestration. 💬 Curious to hear from other B2B leaders: How are you bringing AI into your lifecycle marketing stack?

𝐅𝐨𝐫 𝐲𝐞𝐚𝐫𝐬, 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐫𝐚𝐧 𝐨𝐧 𝐡𝐢𝐧𝐝𝐬𝐢𝐠𝐡𝐭. Dashboards told us what already happened—open rates, MQLs, churn numbers. By the time we saw the problem, it was too late. 𝐋𝐞𝐚𝐝𝐬? 𝐃𝐞𝐚𝐝. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬? 𝐆𝐨𝐧𝐞. 𝐁𝐮𝐝𝐠𝐞𝐭? 𝐁𝐮𝐫𝐧𝐞𝐝. But AI and predictive analytics are flipping the game. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐲𝐦𝐨𝐫𝐞. 𝐈𝐭’𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞. 🔹 𝐋𝐞𝐚𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 Traditional lead scoring is broken. A whitepaper download? That’s not intent—it’s noise. When we actually analyzed behavioral data using platforms like HubSpot, we found that multiple pricing page visits and engagement with onboarding content predicted conversions 3x better than generic lead scores. 𝐖𝐢𝐭𝐡 𝐦𝐮𝐥𝐭𝐢-𝐭𝐨𝐮𝐜𝐡 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 and 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐜𝐨𝐡𝐨𝐫𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ✔ Leads with 𝐫𝐞𝐩𝐞𝐚𝐭 𝐯𝐢𝐬𝐢𝐭𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐩𝐚𝐠𝐞 had a 𝟑𝐱 𝐡𝐢𝐠𝐡𝐞𝐫 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 ✔ Prospects engaging with 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐝𝐞𝐦𝐨𝐬 moved through the funnel 𝟒𝟐% 𝐟𝐚𝐬𝐭𝐞𝐫 ✔ Combining 𝐢𝐧𝐭𝐞𝐧𝐭 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐰𝐢𝐭𝐡 𝐟𝐢𝐫𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬 increased lead quality 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐧𝐠 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐜𝐨𝐬𝐭𝐬 We stopped chasing the wrong leads. And our pipeline? Tighter than ever. 🔹 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 A churn report tells you what you lost. But by then, it’s a post-mortem. Advanced platforms flag disengagement before it happens. A simple tweak—triggering check-ins for inactive accounts—cut churn by 15% in six months. A simple intervention—𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐢𝐧𝐠 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐫𝐞-𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 when customers showed 𝟑+ 𝐝𝐢𝐬𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬—led to a 𝟏𝟓% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐜𝐡𝐮𝐫𝐧 𝐢𝐧 𝐬𝐢𝐱 𝐦𝐨𝐧𝐭𝐡𝐬. 🔹 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐅𝐢𝐭 Guessing what users want is a waste of time. Predictive analytics showed us which features had a 𝟒𝟎% 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 before launch. The result? No wasted dev cycles, no misfires—just 𝐝𝐚𝐭𝐚-𝐛𝐚𝐜𝐤𝐞𝐝 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. If you’re still relying on past data to drive strategy, 𝐲𝐨𝐮’𝐫𝐞 𝐩𝐥𝐚𝐲𝐢𝐧𝐠 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲’𝐬 𝐠𝐚𝐦𝐞. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐥𝐨���𝐤𝐢𝐧𝐠 𝐛𝐚𝐜𝐤. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐰𝐡𝐚𝐭’𝐬 𝐧𝐞𝐱𝐭. #PredictiveAnalytics #MarketingStrategy #DataDriven #Growth

Unpopular opinion… If your clients aren’t on LinkedIn, you don’t need to be here. Seriously… If you’re in B2C selling hair products... Or your audience lives entirely on Instagram or TikTok… Don’t waste time. But if your audience is on LinkedIn? This platform is a goldmine… And you’re probably overcomplicating it. I learned this the hard way…. I was posting random tips, hoping “awareness” would somehow turn into leads. It didn’t. When I stopped guessing and built a focused system around…. Who I actually wanted to reach, everything changed. Here’s exactly how I’d do it again today: Step 1: Go deeper than just “who is my audience?” Most people stop at... “I target CEOs” or “I target marketing managers.” That’s surface level. You need to map the person, not the title. Ask: Who exactly are they? → Not just job title = industry, role, seniority, mindset. What’s their day actually like? → What meetings fill their calendar? → Who pressures them? → What decisions stress them? What’s the #1 problem that keeps them stuck? → Not a vague “they want more revenue.” → What’s the specific pain? What have they already tried that failed? → Knowing their failed attempts helps you position yourself as different. What do they secretly want? → Not just business wins. → Do they want to look good to their boss? → Do they want to save time? → Finally hit that promotion? Where do they spend attention? → Are they scrolling industry news? → Following niche creators? → Lurking in comment sections? Step 2: Build a precise list like a sniper… Once you know them deeply, translate it into exact filters on Sales Navigator. Here’s how… Industry: → Narrow to the specific verticals where your solution really hits hardest Company size: → Don’t target “everyone.” → Choose the size where you know you can deliver the most value. Seniority: → Are you talking to the decision maker? → Or the influencer who introduces you to the decision maker? Geography: → Remove markets you can’t or don’t want to serve. Step 3: Create content like a mirror… Most people post about themselves. You post about your audience’s reality. Write about: → The exact problems you uncovered in step 1 (make them feel seen) → The dream outcomes they secretly want → The mistakes you see people like them making → The lessons you’ve learned helping people in their shoes → The shifts in thinking they need to go from stuck → solved And show proof… Screenshots, mini case studies, behind-the-scenes of how you solve problems. Your content shouldn’t sound like marketing. It should feel like someone just opened your journal. Most overcomplicate LinkedIn... But when you strip it down, it’s simple… → Know them better than anyone else. → Talk about their world, not yours. → Show up daily in small, deliberate ways. → Build trust before you ever ask for anything. That’s it. P.S. Step 4-6 in pinned comments :)

Amazon dropped one of the biggest Sponsored Products updates we’ve seen in a long time (and personally was waiting for): ->audience targeting inside SP and the ability to create custom ones with AMC. Most advertisers are going to butcher this. They’ll pile audience bid boosts on top of existing ranking structures, placement modifiers, legacy bids — and then wonder why their campaigns nosedive. That’s how you torch your signals and bleed efficiency. Here’s the approach — the same structure we’re running across multiple brands: 1. Build your audiences inside AMC. This is the only place you’ll get truly clean data. Do not be lazy and use the ones you have available by default, they are mid to upper funnel audiences (which might work if that is what you wanna go after). But now you have access to AMC, so no excuse not to customize the audience based on your target. How to: Go to your ad console -> measurement and reports ->AMC → Use Cases → Audiences → pick the behaviour (ATC, PDP views, click-no-purchase, etc.) → create to Audience Hub. 2. Do not slap audience modifiers onto existing campaigns. If a campaign has a purpose — ranking, defence, etc — stacking audiences on top of it just corrupts the whole bidding logic. And if you’re already using placement modifiers, mixing them with audience modifiers is a guaranteed mess. 3. Create a separate SP campaign built only for the audience. Low base bid - start with half of the lowest suggested. Attach the AMC audience. Modifier applies only to that audience (at least 100% and increase this as needed). This isolates the traffic, preserves signal quality, and gives you a clean testing lane. The outcome across every brand using this structure has been identical: higher conversion rate, lower CPC, better margin. Same budget — just higher-quality traffic. The rule is straightforward: pick the audience that aligns with your objective. Don’t target everything. Fix the biggest gap in your funnel first. I’ve mapped out every audience, organized them by funnel stage, and included recommended starting points. Comment ME and share this post, and I’ll send you the file. #amazonad #amazonadvertising