AI in Ecommerce Marketing

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

OpenAI just launched Instant Checkout. I’ve spent an hour in their merchant docs and product feed spec, and I think I see the first play here: First, the basics: It's live now with Etsy, Shopify's coming soon. If you're on either, you're auto-enrolled. Everyone else has to build a product feed, enable their Stripe-based checkout (ACP), and submit a form. Now, for the strategy: Everyone's first instinct will be to just map their Google Shopping feed over. I think that's a mistake. After reading the spec, it's clear that this isn't a search index, it's moreso a conversational database. There are no keyword fields, no custom labels and as a result, your usual shopping query SEO won't work 1:1. Winning in ChatGPT Shopping at this stage may well come down to data density. Think about how a user asks a question: "I need waterproof trail running shoes with good cushion for under $150 that will arrive by Friday." ChatGPT isn't matching keywords. It's breaking that query down across structured fields: [product_category], [description], [price], and [delivery_estimate]. The brand that wins is the one that has provided the most complete, accurate data for every single field. [material], [weight], [shipping], [return_window], [product_review_count] in order to capture all possible long tail queries and query fan-outs. Let's be honest: most brands won't do this. Their product data is a mess and they won't put in the work to fill out every optional field. The attrition will be massive. But that's the entire opportunity. This is a raw informational advantage for the companies willing to do the boring, detailed work of getting their data into the system. And I think your [description] field (with its 5,000-character limit btw) is now a really potent long-tail battleground. It's the source material for the AI's answer and it needs to be packed with tons of context. These are the three tactics I'll be watching: - Aiming for 100% field completion if its relevant, I'd bet every optional field is more chance to answer a question and fulfill a filter. - Treating your [description] like a knowledge base for your product. - Structuring everything to build your data moat: [material], [color], [size], [weight] etc. At it's core, this strategy is about feeding the e-commerce agent with the most comprehensive data possible, so that when a user describes what they need, your product appropriately shows up and is the only logical answer. The docs are at https://lnkd.in/eSPNZc7z, so go and build the most detailed feed you can. Happy hunting. 👀

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.

At the start of my career, pricing was often treated as an afterthought. Decisions were made based on instinct, outdated models, or by simply matching competitors. I witnessed how this approach consistently led to underperformance, weak positioning, and lost revenue opportunities. That experience shaped my belief that pricing is one of the most overlooked drivers of business growth. To solve this, we built the Predictive Sales Engine an AI-powered tool that brings clarity to pricing strategy. It analyzes actual market behavior to forecast revenue and sales volume at different price points. More importantly, it segments data to reveal how different audiences respond to pricing, allowing companies to set prices with precision and confidence. After working with hundreds of companies, the pattern is clear. When pricing aligns with how customers perceive value, businesses grow faster and more profitably. In a competitive market, using AI to guide pricing decisions is no longer a luxury. It’s a requirement for those aiming to lead rather than follow. #PricingStrategy #ArtificialIntelligence #PredictiveAnalytics #RevenueGrowth #ProductMarketing

Two forces are colliding in B2B go-to-market: the decline of the traditional playbook and the meteoric rise of AI. In 2006, we founded Marketo and I helped create that traditional playbook — the one built on MQLs, marketing automation, and tracking every click. For years, it worked brilliantly… until it didn't. Now, the “gum ball machine” approach to marketing (“budget in, MQLs out”) has become unsustainable. Buyers are burned out by relentless outreach, and trust is at an all-time low. It’s time to reframe marketing’s role in revenue and lean into brand-building as a long-term differentiator. At the same time, AI agents are reshaping how we work and buy. They’re handling repetitive tasks like qualifying leads and building campaigns, and helping us make purchases by filtering and summarizing information. In this world, experiences that can't be filtered or summarized will become marketing's new currency. These two trends are driving the most profound transformation in B2B marketing since the advent of marketing automation. And they work together. As AI finally delivers on the promise of “automation” in marketing automation, it will free us to focus on the strategic, creative work that truly moves the needle. Put another way, if AI can handle the "-ing" in marketing, then we can focus on the "market": understanding our buyers, crafting compelling narratives, and building memorable experiences. This shift is at the heart of my 11 predictions for how B2B will evolve in 2025 and beyond. Here's a sneak peek: 1. Companies will slowly break from their "gumball machine" MQL addiction 🍬 2. CMOs will work to reframe marketing's role in revenue 📈 3. Marketers will rebalance budgets toward brand 🌟 4. AI agents will gain early real traction in the enterprise 🤖 5. MOps teams will use AI to trade tactical tasks for strategic impact 👩💻 6. AI will start to replace junior sales roles but augment strategic sellers 🤝 7. Companies will adopt AI SDR agents — but automated cold prospecting will fall flat ❄️ 8. Seat-based pricing will give way to value-based models 💺 9. Agents will begin to transform how we buy — and how we go-to-market 🛍️ 10. Experiences, relationships, and original content will stand out as AI filters out traditional marketing 🎉 11. Marketing automation will be reimagined for the AI era 🚀 The full definitive article, shared in comments, dives into each prediction and what it means for you. Found this valuable? Please leave a comment or repost to let me know what you think and help drive visibility for these ideas! Do you agree or disagree? What are you seeing in your own business? 🙏 #B2BMarketing #MarketingAI #MarTech #CMO #MOps #SalesAI #MarketingAutomation #Predictions #Marketing2025

The search bar is dead. And most e-commerce platforms don’t even know it yet. After working closely with AI systems and recommendation engines, I’ve learned one thing: “Personalized shopping” was never truly personal. It was pattern matching. It was collaborative filtering. It was reactive logic pretending to be intelligence. Now we’re entering a different era. → From personalized to personal → From search-based discovery to proactive intelligence → From browsing endlessly to AI agents working for you This is agentic commerce. Traditional e-commerce makes you do the heavy lifting: Search → Filter → Scroll → Compare → Hope Agentic commerce flips the entire model: Describe what you want → AI delivers with context One of the most interesting examples I’ve seen is Glance. They are not building another shopping app. They’re building a contextual, agentic AI commerce layer powered by multiple specialised agents working together. Instead of one algorithm guessing what you like, Glance deploys multiple AI agents working for you in parallel: → Weather Agent analysing real-time climate and fabric suitability → Trends Agent tracking global shifts and micro-trends → Occasions Agent anticipating upcoming events → Physical Agent understanding your skin tone, undertones, and body type → Lifestyle Agent decoding your aesthetic preferences All coordinated by an orchestrator that synthesises everything into a unified styling strategy. That’s not basic personalization. That’s contextual intelligence. And the most powerful shift? You see yourself in the generated looks. Not stock visuals. Not generic models. You. Commerce becomes a conversation instead of a search box. From personalized to personal. AI agents working for you. Learning with every interaction. Refining your style instead of just tracking clicks. This is the rise of agentic commerce. #Glance #AICommerce #AgenticAI

Your next customer might not even be a human — but their AI agent. Which means the future of marketing won’t be just B2B or B2C. It’ll also be B2AI.     With the rise of agentic commerce, AI-driven agents will become increasingly autonomous and make purchase decisions on behalf of consumers.     In this new landscape, digital assistants will act as intermediaries between brands and consumers, and even increasingly from agent-to-agent. Marketers will need to speak the language of these platforms and optimize for what AI agents suggest and target.      And despite AI's immense capabilities, the true unlock is how we as human beings decide to work with AI. I see AI as a companion, partner, and collaborator. Just like in music, where it's rare for one person to write a successful song on their own — it’s a powerful cowriter not meant to replace your abilities, but enhance them.     In fact, I believe we are entering an era where we’ll have an incredible chance to blend art and science in marketing. One where human creativity is empowered and expanded by AI. Where intuition is informed by data.      If you think about it, it already is. Our intuition is deeply formed by all the knowledge and experiences we’ve accumulated over our life.     So true opportunity lies in combining AI’s speed and scale with human creativity, empathy, and cultural insight.     And, let’s remember that there are some things AI cannot do. People inherently crave connection, community, physical proximity, and even the imperfection inherent in human performance.      That’s what makes us human.      And that embrace and display of humanity is what will separate the brands that people love from the brands they simply know.     #AIinWork #DigitalMarketing #ArtificialIntelligence #GenAI

50% of Google searches now result in AI summaries. Businesses must optimise content accordingly.  Ranking first in Google is no longer the only goal. AI engines are quickly gaining in popularity for discovery.  (ChatGPT, Claude, Perplexity, Gemini) If you want your brand to be visible across the board,  You need to put energy into optimising for AI-driven search (AEO/GEO). Google rankings can still bring traffic.  But AI-driven rankings are shaping recommendations and buyer intent. This is what businesses need to dominate search in 2025 👇 ➡️ Step 1: Be Found (SEO)  ↳ Search engines still matter, but they’ve changed. You need to: ✅ Nail the fundamentals: fast site, schema, structured data. ✅ Build clusters around full topics, not just keywords. ✅ Own authority with consistent on-page optimisation and backlinks. This is a prerequisite to entering the conversation. ➡️ Step 2: Be Chosen (AEO)  ↳ AI Overviews are now the front door of discovery. To win, you must: ✅ Optimise for voice queries, FAQs, and instant answers. ✅ Structure content so LLMs can “lift” your answers directly. ✅ Prioritise clarity, short snippets, and schema-rich responses. If you aren’t AI’s first choice, you will be invisible. ➡️ Step 3: Be Cited (GEO)  ↳ Generative engines reward those they can reference. To earn citations: ✅ Create high-trust, insight-led content. ✅ Publish data, original thinking, and frameworks AI can reference. ✅ Use prompt-led publishing to match how people query LLMs. If the machines don’t cite you, humans won’t find you. ➡️ Step 4: Be Scaled (AIO) ↳ AI has changed the economics of content. Scale comes from: ✅ Automating content production without losing voice. ✅ Repurposing into multiple formats (text, video, carousel). ✅ Using AI for editorial planning, clustering, and coverage. This is how you compete on volume without burning resources. ➡️ Step 5: Be Dominant (SXO) ↳ Winning in 2025 isn’t about ranking. It’s about owning the ecosystem: ✅ Stack SEO + AEO + GEO + AIO into one system. ✅ Dominate a niche with topic-layering across search, AI, and platforms. ✅ Push traffic off platforms into owned channels (newsletter, community). Search in 2025 is no longer a single-channel game. SEO is the foundation. But... → AEO earns you visibility in AI overviews.  → GEO secures citations inside generative engines.  → AIO and SXO let you scale and own the ecosystem. The businesses that start adapting now will catch this rising tide.   The ones that don't risk losing traffic and customers. That's exactly why I'm building Searchable.  It's an Autonomous SEO & AEO Growth Engineer. Join the waitlist here 👇  https://lnkd.in/epgXyFmi Have you started optimising for AI search?  Share your experience in the comments. - - - - 📌 If you want a high-res PDF of this sheet: 1. Follow Chris Donnelly. 2. Like the post. 3. Repost to your network. 4. Subscribe to: https://lnkd.in/eUTCQTWb

What Got Lost In The AI Hype Last Week: Most iPhone and Samsung Galaxy users think their phones’ AI is useless, and less than half of users are willing to try the AI features. Anyone building AI products can learn from these mistakes. Here are my top takeaways. Neither brand has defined AI as a key differentiator that would drive consumers to switch. Only 17% of iPhone users and 10% of Samsung users would switch brands if the competitor developed superior AI features. Only 12% of iPhone users and 4% of Samsung users would be willing to pay for a subscription to AI services on their devices. That’s probably a big reason that Apple pushed some Siri updates into next year. It’s not driving customers to switch, there’s little risk of losing customers, and the path to monetization is unclear…so what’s Siri AI’s value driver? Both Apple and Samsung must change their AI product strategies and pivot toward value-centric features. AI products require a powerful motivator for people to try AI-powered features. 58% of iPhone and 53% of Samsung users haven’t tried the AI features on their phones. The technology, AI label, and marketing hype alone aren’t driving people to use AI. However, when AI is integrated seamlessly into features like writing tools (72% used it) and circle to search (82% used it), people are more likely to try it. Workflow integration is critical for adoption and monetization. Users have had Samsung’s AI features for longer, and 44% don’t use them because they don’t find them useful. 35% don’t trust them, and 30% are concerned about privacy. Utility, reliability, and trust must be designed into AI products. Productizing AI goes well beyond the models and surveys like this one provide insight into what’s working and where the most significant pitfalls are. 

Three quarters of companies have yet to generate value from #AI. But the good news is that AI leaders are showing the way forward in adopting valuable AI solutions at scale, here’s what they’re doing differently:   ✅ Focusing on core business processes and support functions   ✅ Setting more ambitious targets  ✅ Integrating AI in both cost and revenue generation efforts  ✅ Prioritizing people over technology and algorithms  ✅ Moving fast on #GenAI      Leaders making these moves are set up to generate more than 45% in cost reduction and 60% greater revenue growth than the 70%+ companies falling behind. This may appear daunting, but we can use their success as a playbook:   1. Set a bold strategic commitment from the top  2. Define a portfolio of initiatives that maximize AI’s value potential, following 3. BCG’s deploy, reshape, invent approach  4. Start implementation with 1-3 high ROI projects  5. Ensure the minimum viable infrastructure exists for these initiatives  6. Identify the critical gaps  7. Ensure that implementation focuses on people > technology and algorithms   8. Set responsible AI guardrails      Read our new report for a deeper dive into how companies can increase their AI maturity and implement it at scale: https://on.bcg.com/4eWHu5N