AI in Ecommerce Marketing

Both AI and neuromarketing are playing transformative roles in the world of advertising, reshaping strategies and enhancing the effectiveness of campaigns. What do you think about this Ad? Here's how they contribute: Personalization: AI algorithms analyze vast amounts of data to understand individual preferences, behaviors, and demographics. This information allows advertisers to create highly personalized and targeted campaigns, delivering content that is more likely to resonate with specific audiences. Predictive Analytics: AI can predict consumer behavior and trends based on historical data. Advertisers leverage predictive analytics to identify potential customers, optimize ad placements, and allocate resources more effectively. Chatbots and Virtual Assistants: AI-powered chatbots and virtual assistants provide personalized interactions with consumers. They can answer queries, recommend products, and guide users through the purchasing process, enhancing customer engagement. Content Creation and Optimization: AI tools can generate and optimize content for advertising. From writing ad copy to creating visuals, AI algorithms analyze data to determine what elements are most effective in capturing audience attention and driving conversions. Programmatic Advertising: AI-driven programmatic advertising automates the buying of ad space in real-time. This allows advertisers to target specific audiences across various channels and optimize campaigns for better performance. Emotion Analysis: Neuromarketing, particularly through the use of neuroimaging techniques, helps advertisers understand how consumers emotionally respond to advertisements. This insight enables the creation of emotionally resonant content that has a stronger impact on the audience. Eye-Tracking Technology: Neuromarketing studies often involve eye-tracking technology to understand where individuals focus their attention in an advertisement. Advertisers can use this information to design layouts that draw attention to key elements. Neurofeedback for Ad Testing: Neuromarketing techniques, such as neurofeedback, are used to assess the neurological responses of individuals to advertisements. This data helps in refining and optimizing campaigns by understanding which elements evoke positive or negative reactions. Voice and Visual Search Optimization: AI is integral in optimizing advertising for voice and visual search. As more consumers use voice-activated devices and visual search tools, advertisers need to adapt their strategies to be discoverable through these mediums. Dynamic Pricing and Offers: AI algorithms can analyze market conditions, demand, and competitor pricing to dynamically adjust product prices or offers. This dynamic pricing strategy can be implemented in real-time to maximize revenue. #ai #marketing #technology #innovation via @ marketing.scientist

Struggling to get SEO content that actually ranks from ChatGPT? Here’s the problem: You’re asking it to write. But you should be asking it to think with you. As SEO's, we need more than just 1,500 words with a few keywords stuffed in. We need structure. Strategy. Search intent. Clarity. So here’s the prompt that leveled up my process: “You’re a senior SEO strategist with 10+ years of experience. Give me brutally honest feedback—like a mentor would. Don’t rewrite. Just evaluate based on search intent, keyword alignment, clarity, content gaps, and user value. Let me know if anything is unclear. Take your time—I’d rather get thoughtful feedback than fast feedback.” Why does this prompt work for SEO content: You define the role and expertise You share keywords and search intent context You avoid rewrites, forcing strategic thinking You create a loop for feedback and iteration You focus on depth, not just deliverables This approach turned ChatGPT from a basic writing tool into a second pair of expert SEO eyes. Want better AI support for your SEO workflow? Start with better prompts. Got one you use? Share it in the comments—I’d love to steal… I mean, learn from it. Follow : Sushil Singh #SEOtips #ChatGPTPrompt #ContentStrategy #AIforSEO #PromptEngineering #DigitalMarketing #sushil_singh #seoexpert #learnseo

I spent 6 months testing AI SEO tactics for clients. One saw 2,300% AI traffic growth and now appears in 90+ AI overviews (vs zero before). Here's the exact 4-step framework we used: 1. Finding AI Opportunities: • Use Surfer AI Tracker/Ahrefs Brand Radar to see how often AI platforms mention your brand vs. your competitors • Plug in your brand and theirs, then filter for AI keywords they’re winning that you’re not • Those are the exact topics you need to target and steal 2. Content Strategy: •  Write directly and skip the fluff (AI hates filler content) •  Use conversational tone (how people actually speak vs keyword stuffing) •  Structure with clear H1/H2/H3 hierarchy •  Add TLDR summaries at the top of articles 3. Building Trust Signals: •  Claim and optimize Google Business, Yelp, LinkedIn profiles •  Get high-quality backlinks from relevant domains •  Include expert quotes and author bios with credentials •  Showcase certifications and awards prominently •  Add case studies with real data 4. Tracking Results: In GA4, go to Reports > Acquisition > Traffic Acquisition, add comparison filter for referral traffic, then use this regex: (.*gpt.*|.*chatgpt.*|.*openai.*|.*neeva.*|.*writesonic.*|.*nimble.*|.*outrider.*|.*perplexity.*|.*google.*bard.*|.*bard.*|.*edgeservices.*|.*gemini.*google.*) This shows exactly which AI platforms send you traffic.

On the surface, Performance Max(PMax) campaign looks rigid. However, there are 4 ways you can optimize your PMax campaign effectively. 1. Value rules In Search, Display, or YouTube campaigns, you can easily make bid adjustments at device, location, and audience segment levels. However, the Pmax campaign doesn't allow you to make them directly. However, it is possible, albeit with two constraints. - The campaign should be in value(ROAS) based bidding - We can only do a maximum of 50% bid reduction (on the negative side). There is no constant on the bid increase side, however.   We can optimize value rules based on three segments: Device, location, and audience. One good thing about the value rules is that you can cascade two conditions and set a rule, which is not available in other campaign types. For example. If you want to decrease your bid for a mobile device from Bangalore, you can do it with one value rule. 2. Product feed-based optimization This is the most critical optimization method for e-commerce brands. With the removal of Smart shopping ads, almost all the e-commerce brands use PMAx campaigns for shopping ads. Here, you can optimize the campaign's performance by removing and adding items in the feed. This can be done at 5 levels. 1. One individual product level (i.e.) at item ID level 2. Product category level. E.g., Home appliances Vs. Books 3. Product Brand level. E.g., Adidas Vs. Nike 4. Product type level. Basically, the subcategory level in a category. E.g., Running Shoes Vs. Formal shoes 5. Lastly, custom label level - Based on the customer label you put in the feed. Multiple strategies can be used here: AOV-based, Best-selling items-based, or simply ROAS-based. You just need to crunch the numbers and decide. 3. Adding negative keywords This is the most reliable and comparatively more straightforward method. App Marketers have been doing this in the UAC campaign for many years, and it works perfectly. You get 4 data points at the keywords level in PMax. - Impressions - Clicks - Conversions - Conversion Value Based on the optimization you want to do, you should create a keyword list and ask your account manager/Google support to get them excluded from the campaign. This way, you can modify the list later too. 4. Brand exclusions. Google rolled this out a few months back. You can give a meta instruction to the campaign to restrict ads in specific brands. Originally designed to help brands not bid on their partners and competitors, many brands are using it to remove their brand keyword targeting from PMax. But we should note that it may not block sub-brands, product collections, or abbreviated versions of your brand name. If you feel Google knows all your business nuances better than you do, you can skip all these and manage the campaign simply with bids and budget changes. That way, you get average results.

Programmatic ads in 2020: Slow and long term Programmatic ads in 2025: Delivering ROI in weeks. For context - Programmatic has always been seen as a tool for TOF campaigns, not direct response. It lacked the agility of platforms like Meta... Where you can put 1 dollar in today and see 1 dollar out tomorrow. Traditionally with programmatic, you needed months to gauge an impact. But recent advancements are transforming that process. Here’s what’s happening: 1️⃣ Better Attribution Brands can now use pixels and attribution models to directly track purchases from programmatic ads. This reduces the guesswork and makes reporting far more accurate. 2️⃣ Shorter Learning Cycles Instead of waiting six months to optimize campaigns, new algorithms can reduce the learning period to just 4-6 weeks. This means faster feedback, faster results, and lower upfront risk. 3️⃣ Direct Response Capabilities Programmatic is evolving to compete with platforms like Meta in terms of efficiency. Algorithms and tools are making it possible to run campaigns that function like direct response—turning ad spend into measurable ROI much faster than before. Takeaway for DTC brands: Programmatic is no longer just a long-term play. It’s becoming a viable channel for short-term, conversion-focused campaigns. Advancements in: ~Targeting ~Attribution ~Optimization Are all bridging the gap between programmatic and Meta’s ad engine. It’s just another reason why brands should be exploring programmatic as a competitive edge. The tools are better, the learning cycles are shorter, and the potential is massive. If you want to get started with programmatic ads for your brand, let's chat.

Unlock the power of AI in Google Ads: The Impact of AI on Google Ads Performance Understanding AI in Google Ads Before delving into its impact, let's first grasp the concept of AI within the realm of Google Ads. AI in Google Ads refers to the utilization of machine learning algorithms to automate and optimize various aspects of ad campaigns. From targeting the right audience to adjusting bids in real-time, AI empowers advertisers to streamline their advertising efforts and achieve better results. Enhanced Targeting Capabilities One of the primary ways AI enhances Google Ads performance is through its advanced targeting capabilities. Traditional advertising methods often rely on demographic data and basic user behavior to target audiences. However, AI takes targeting to a whole new level by analyzing vast amounts of data to identify patterns and preferences among users. Dynamic Ad Customization In addition to improved targeting, AI also facilitates dynamic ad customization, allowing advertisers to create highly personalized ad experiences for their audience. Through techniques like dynamic keyword insertion and ad variations, AI-driven ad campaigns adapt to individual user preferences and behaviors in real-time. Optimized Bidding Strategies Another area where AI significantly impacts Google Ads performance is in optimizing bidding strategies. Traditional bidding methods often require manual adjustments based on limited data and insights. However, AI-powered bidding algorithms continuously analyze performance data and adjust bids in real-time to maximize the ROI of ad campaigns. Improved Performance Insights Furthermore, AI provides advertisers with invaluable performance insights that enable them to make data-driven decisions and optimize their ad campaigns for better results. Through advanced analytics and predictive modeling, AI algorithms identify trends, patterns, and opportunities within ad campaigns, allowing advertisers to fine-tune their strategies for optimal performance. Summary The impact of AI on Google Ads performance cannot be overstated. From enhanced targeting capabilities to dynamic ad customization and optimized bidding strategies, AI-driven solutions revolutionize the way advertisers approach online advertising. By leveraging AI technologies, businesses can maximize the effectiveness of their Google Ads campaigns, drive better results, and ultimately achieve their marketing objectives in today's competitive digital landscape. #AIinGoogleAds #DigitalMarketing #ArtificialIntelligence #AdvertisingStrategies #OptimizationTechniques #OnlineAdvertising #MarketingInsights #GoogleAdsPerformance #AIRevolution

New Update: Amazon DSP campaign and creative APIs are now generally available. This is a build on many of the announcements from #unBoxed2024 What is it? This new feature allows users to create, read, and update their Amazon DSP campaigns, ad groups, targets, and creatives through a programmatic interface. How does it work? These APIs enable technology providers and advertisers to develop custom experiences within their own applications and seamlessly run Amazon DSP campaigns within existing workflows. The new APIs can be used in conjunction with existing audience and deal resources, providing a comprehensive toolkit for end-to-end campaign management. Users can now store Amazon DSP campaign data locally, simplify campaign and creative creation, and automate optimizations to maximize campaign performance. Why should I care? This update is a game-changer for Amazon DSP users. Here's why it matters: 1. Efficiency boost: Streamline your campaign and creative creation process, significantly reducing activation time for new campaigns. 2. Better data control: Store and manage Amazon DSP campaign data locally, giving you more control over your data and analytics. 3. Custom optimization: Automate optimizations across campaign, ad group, and targeting settings, allowing for data-driven decisions on bids and budgets. 4. Seamless integration: Easily integrate Amazon DSP into your existing tech stack, enabling you to track campaigns in your own tools and sync campaign metadata with your data storage solutions. 5. Performance improvement: Experiment with new audiences and quickly remove underperforming ones to maximize campaign performance. 6. Real-time adjustments: Automatically adjust bids and budgets in real-time, ensuring your campaigns are always performing at their best. Bottom line: Whether you're a large agency or tech partner looking to integrate Amazon DSP more deeply into your operations or an individual advertiser seeking to automate and optimize your campaigns, these new APIs offer exciting possibilities to enhance your advertising efforts on Amazon's platform. Want to check it out? You can learn more about these new features at the Amazon Ads website (https://lnkd.in/gESdMWhy). For those ready to dive in, check out the developer guide (https://lnkd.in/gQAPRdcs) and reference documentation (https://lnkd.in/gBV-BbVb) to start leveraging these powerful new APIs in your advertising strategy.

Has AI officially killed SEO? I recently saw Palki Sharma's video claiming 80% of top websites are losing traffic. And I won’t lie it's true for many. But does that mean SEO is dead? Not really. SEO isn’t dead. It’s evolving. Search has moved beyond 10 blue links. It’s shifting into: → AEO (Answer Engine Optimization) → GEO (Generative Engine Optimization) → And AI-powered SEO strategies that go beyond titles and meta descriptions. We’ve been testing this with our clients optimizing content to get cited by ChatGPT, Gemini, and even Perplexity And it works. Yes, the rules have changed. But the goal is the same: 📌 Show up when your audience is searching. 📌 Be the best answer in a world full of AI summaries. 📌 Use SEO to train the AI to recommend you. It’s no longer just about ranking on Google. It’s about being the default answer across every AI assistant. If you’re still doing SEO like it’s 2018, it’s time to adapt. The new playbook? → Learn AEO. → Master GEO. → Use AI to futureproof your visibility. Because the brands that get this now... Will be the ones everyone sees later. #SEO #AEO #GEO

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

Pro tip from a PPC expert: 🎯 ❌ No clear account structure = wasted budget ❌ No winning strategy = clicks don’t convert ❌ No optimization & tracking = flying blind Master these 3 pillars and turn campaigns into cash. 💸🚀 ✅ Structure your account for clarity ✅ Define a focused strategy for growth ✅ Optimize & track every click for insights Here’s a quick deep-dive into those three pillars—with a mini case to bring it to life: 1. Crystal-Clear Account Structure✅ What it is: Organizing campaigns → ad-groups → keywords so your ads serve the right message to the right audience. 👉 Why it matters: Keeps budgets separate, makes performance easy to diagnose, and prevents irrelevant traffic. 👉Example: A footwear brand splits its “Running Shoes” campaign into two ad-groups—“Men’s Running Shoes” and “Women’s Running Shoes”—each with tailored headlines and keywords. This way, female shoppers only see “Women’s Running Shoes” ads, boosting relevancy and Quality Score. 2. Focused Strategy✅ What it is: Defining clear goals (e.g., maximize ROAS, boost sign-ups) and matching bids, placements, and ad copy to those goals. 👉Why it matters: Stops you from spending on low-value clicks and aligns every dollar with your business objective. 👉Example: If your goal is to drive trial sign-ups, you bid aggressively on “free trial + [your product]” keywords and use ad copy like “Start Your Free 14-Day Trial Today,” rather than generic “buy now” language. 3. Continuous Optimization & Tracking ✅ What it is: Installing conversion tracking, monitoring key metrics (CTR, CPC, CPA, ROAS), and iterating—testing new headlines, adjusting bids, pausing under-performers. 👉Why it matters: Without data, you’re flying blind; with it, you can cut wasted spend and double down on winners. 👉 Example: After 2 weeks, the brand notices “Women’s Running Shoes” ads have a 3% CTR vs. “Men’s” at 1.2%. They shift more budget to the higher-CTR group and test a new headline (“Shop Top Women’s Running Styles”)—CTR jumps to 4%. ✅Bottom Line: Structure → Strategy → Optimization: nail these in order, and you turn random clicks into reliable revenue. Follow Kautilya Roshan for more insight 😊 #GoogleAds #PPC #DigitalMarketing #GrowthHacking