Marketing Technology Trends

This week's obsession: What happens to email marketing when AI manages the inbox? Does that mean email marketing as we know it is dead? 💀 Email has been the GOAT for years. I've probably told all of you at least once: Email has been the only place where people—not algorithms—are in charge. It’s been the only place where people *choose* to hear from you, instead of standing in the center of the Marketing Square, shaking a tin cup at the Fickle Algo Gods for a few puny shillings of attention. Email lets you show up in an inbox like, hey, 'sup. I’ve loved that for us. I’ve loved the high bar it has created for serious marketers. But now that’s all changing. The robots are in the inbox now, too. Or they will be. Soon—if not already—email messages are filtered, summarized, collapsed, prioritized, pasteurized, pulverized, purified, processed, and (I'm out of P words) before a human ever sees them. A human is summoned only according to certain set parameters. (Another P word! There it is!) For marketing, that means the holy grail of permission no longer guarantees visibility. Deliverability doesn’t guarantee it, either. It’s not enough to make sure all your T’s are crossed & I’s are dotted so you land in the primary inbox, high-fiving all the other emails that dropped in alongside you. YOU MADE IT is no longer the goal. The new goal is neither access nor deliverability. It’s being *chosen*. >>> The old way: deliverability was king. >>> The new way: selection is king. It means a person plucking your fresh email, new & telling AI: "Yo. Save those for just me, bestie." Your relationship with the recipient is the difference between being seen… and being skipped. For years, we optimized for deliverability—how to get in. Now we need to optimize for selection—how to be chosen. That changes the work, right? 👉 From messaging → making meaning Not just delivering info, but helping your reader make sense of it. 👉 From broadcasting messages → corresponding with people Pillow over the face of “dear audience.” More “hey this is for you” energy. 👉 From scale-first → story-first Less sending for the sake of sending; more personality and point of view. The only email strategy that actually works from here on out is being someone your reader would genuinely miss if you stopped showing up. That’s a relationship problem, a writing challenge, and—if you’re willing to invest the time—an opportunity. When speed becomes cheap, judgment carries a premium. That’s true in marketing strategy. It’s true in content. And now…it’s especially true in email. We need to show up differently when AI is triaging the inbox. Do this: Pull up your last 3 email sends. Look yourself squarely in the eye and answer with unflinching honesty: Would an AI assistant surface this for my reader? Would my reader save it for their own human eyes? Is this genuinely worth their time? That, my friends, is the new bar. And actually… I kind of love this new bar for us, too. You?

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

𝐀𝐈’𝐬 𝐧𝐞𝐰 𝐫𝐨𝐥𝐞 𝐢𝐧 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐑𝐎𝐈. AI has already proven to be a valuable tool in increasing #advertising efficiency, helping brands plan better, work smarter and save time. But can AI also drive measurable impact and increase #marketing effectiveness? Nielsen has recently measured over 50,000 #AI powered brand campaigns on YouTube and over 1 million performance campaigns on Google to uncover ROAS and sales effectiveness. The results from those MMMs showed that Google AI-powered advertising solutions consistently outperformed manual campaigns in both #ROAS and sales effectiveness: 🚀 Google AI-powered video campaigns on YouTube deliver 17% higher ROAS than manual campaigns. 💪 Google AI-powered VRC for Efficient Reach + VVC delivers 23% higher sales effectiveness than VRC for Efficient Reach alone. ✔️ Adding Google AI-powered Demand Gen to Search and Performance Max campaigns delivers 10% higher ROAS and 12% higher sales effectiveness than those without Demand Gen. It's already clear that #AI isn’t just a fad or a futuristic planning tool – it’s a performance driver in #advertising.

In marketing, choosing the right campaign strategy — such as whether to reach customers through SMS or email — is critical. These decisions shape how effectively brands connect with their audiences. In a recent tech blog, Klaviyo’s data science team shared how they used uplift modeling and counterfactual learning to help marketers deliver more personalized campaigns at scale. The team began with a simple but powerful insight. Instead of defining audience segments first and then randomizing within each group to test different strategies, it’s mathematically equivalent to randomizing treatments first and segmenting afterward. In practice, this means you can run a single randomized experiment — for example, comparing SMS versus email — across the entire audience, and later analyze how different subgroups responded to each treatment. Building on this foundation, the team applied uplift modeling to estimate how each recipient would respond under different treatments. The result is a system that predicts which customers are more likely to engage via SMS versus email — and automatically personalizes campaign delivery accordingly. The team ultimately turned this approach into a product feature, empowering marketers to design smarter, data-driven strategies with minimal manual testing. It’s a great example of how causal inference and machine learning can go beyond analysis — directly shaping how real-world marketing decisions are made. #DataScience #MachineLearning #UpliftModeling #CounterfactualLearning #Personalization #Marketing – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gBgBiTJj

As we head into 2026, one thing is crystal clear: Brand experience is no longer a layer of marketing...it is the brand. In a world saturated with content, algorithms and AI-generated sameness, the brands that are winning are doing something beautifully human: they’re building worlds you can step into. Physical space is no longer just retail, it’s the most powerful conduit for storytelling across digital, social and culture. Here are my top 5 brand experience trends shaping 2026 👇 1. The Store as the Source of Truth The physical store is becoming the anchor for all brand communications. Not a rollout endpoint, but the origin. Campaigns are now designed store-first, with every other touchpoint (social, e-comm, PR, creators) orbiting the physical expression. The best spaces don’t just reflect the brand, they generate content, community and credibility in real time. 2. Connected Storytelling (No More Copy + Paste) Consumers can smell disconnected campaigns a mile off. The winning brands are telling one story, expressed differently across touchpoints. Same narrative, different formats. Retail doesn’t mirror social; it interprets it. Experiences don’t repeat campaigns; they deepen them. This is joined-up thinking with intent; not assets rolled out, but meaning built up. 3. Experience Over Scale Big isn’t always better. The most impactful activations I’m seeing are focused, tactical and emotionally precise. Smaller footprints, clearer calls to action, stronger memory. Think: fewer people, deeper engagement. Presence over impressions. Brands are optimising for how it feels to be there, not just how it looks online. 4. Participation Is the New Premium Luxury isn’t access; it’s involvement. Workshops, rituals, performances, personalisation, live moments. The brands leading the way are designing experiences that ask people to do something, not just observe. Because participation creates memory, and memory creates loyalty. 5. Retail as Cultural Infrastructure The most progressive brands are treating physical retail like cultural programming. Collaborations, dinners, clubs, talks, performances, community moments. Stores are no longer just commercial spaces; they’re platforms for relevance. When done right, commerce becomes a byproduct of belonging. The Bigger Shift We’re moving from brand campaigns to brand ecosystems. From seasonal drops to living narratives. From selling products to staging worlds. In 2026, the brands that cut through won’t be the loudest, they’ll be the most coherent. The most human. The most considered. The future of brand experience isn’t about doing more. It’s about connecting better. 👉 Do you agree? What are you seeing emerge as the biggest retail trend for 2026? Let me know in the comments. ________________ *Hi, I am Tim Nash. I help global brands build connected campaigns that resonate across every touchpoint. 🚀 #BrandExperience #FutureOfRetail #ConnectedStorytelling #ExperientialMarketing #RetailTrends

While everyone's focused on AI replacing jobs, here's what top CMOs are actually excited about I recently sat down with a group of marketing leaders to discuss AI’s role in our industry. And let me tell you what’s happening is unlike anything I’ve seen in the past 20 years. We’re not just talking about automation anymore. AI is changing how we think, create, and connect with customers in ways that seemed impossible just a few years ago. Here are the numbers that stopped me in my tracks: →AI chatbots will handle 85% of customer interactions that’s a 4X increase from today. →30% of marketing tasks will be automated, freeing teams to focus on strategy and creativity. →97 million new AI-enabled jobs will emerge globally proving that AI isn’t just replacing jobs, it’s creating them. The most exciting part isn’t automation it’s how AI is helping marketers make better decisions. Predicting customer behavior before they even know what they want Example: Netflix and Amazon don’t just suggest what you might like—they anticipate your next move based on tiny behavioral cues. AI-driven marketing will soon do the same, predicting which product a customer will buy, when, and why. Creating hyper-personalized content at scale Example: The Coca-Cola Company has already experimented with AI-generated ad creatives tailored to individual consumers. Imagine campaigns that adjust in real time, responding to each person’s preferences, location, and mood. Understanding customer emotions in real time Example: Brands like SEPHORA and Spotify are leveraging emotion AI to tailor experiences based on a user’s mood. Soon, AI will help brands respond to sentiment shifts, adjusting messaging before customers even voice concerns. The companies that embrace AI today will have a head start over their competitors. But this isn’t just about tech it’s about how we use it to enhance creativity, storytelling, and human connection. Marketing has always been about understanding people. AI just gives us better tools to do that at scale. how will you use AI to make marketing more human?

For a long time, MarTech kept solving the same problem by adding more tools. More dashboards, more features, more “connect everything to everything”. Somewhere along the way, stacks became heavier not smarter. Most brands don’t actually have a loyalty or retention problem. They have an attention problem. Customers don’t leave because your product suddenly became bad. They leave because your brand slowly fades out of their daily life. No friction, no drama...Just… absence. What’s coming next in MarTech isn’t another wave of shiny features. It’s a shift in how systems are built. Less campaign thinking, more "always-on" systems that react to people, not calendars. Less rigid segmentation, more fluid understanding of where someone is, right now. AI is going to accelerate this, but not in the way most decks describe it. It won’t magically make marketing better. But it will make bad marketing very obvious. If your strategy is noise, AI gives you more noise. If your strategy is relevance, AI finally makes that scalable. Loyalty is also about to be redefined. Not as points or tiers, but as a byproduct of being useful, showing up at the right time and not wasting people’s attention. The best brands in the next few years won’t feel like they’re “doing marketing”. They’ll feel present, familiar. Almost natural in a customer’s life.

I’ve been working with some of the world’s biggest brands on their AI transformations. Here’s the biggest mistake everyone is making 👇 It’s not the models. It’s not hallucinations. It’s not the prompts. It’s the brand. As AI adoption accelerates, I keep seeing the same pattern: Every tool needs brand grounding. Every agency uploads their own version. Every team creates their own rules. Before long, there isn’t one brand. There are dozens of fragmented versions of it. And the brand doesn’t control any of them, nor is it learning and being feed real time data to do what AI can do best. Learn! This creates serious problems: Loss of control: Sensitive brand positioning and IP are scattered across third-party tools. Loss of consistency: What’s approved in one workflow is wrong in another. Updating becomes impossible. Loss of ownership: Whoever owns the brand data owns the power. And too often, that’s the agency or the platform—not the brand. Here’s the deeper issue most teams miss: In an agentic world, brand isn’t static. It should learn. Campaign wins and losses should continuously feed back into the system. That only works if there is a single source of brand truth. If there isn’t, the brand belongs to whoever configured the tool. This should worry every CMO. Because brand is no longer a PDF. In the agentic world it’s an agent. That’s why the next critical layer in the #martech stack is the Brand Agent. A Brand Agent is a centralized, owned agent that serves as the single source of truth for brand voice, guardrails, do’s and don’ts, and campaign learnings. Every other agent—creative, media, CRM, commerce—references it for grounding. No duplication. No drift. No agency-owned brand logic. The implementation will vary. The principle won’t. Own the Brand Agent. Govern it. Feed it real-time learning. Let everything else integrate to you. That’s how brands scale AI without losing themselves. How are you thinking about brand governance in an agent-first world?

If your competitors keep showing up in ChatGPT answers and you don’t, there’s a reason. And it’s fixable. Here’s the full 8 step process to figure out why and close the gap fast. Step 1: Identify your real AI competitors There are three groups to watch for. - The brands AI mentions next to you - The brands AI prefers instead of you - The brands your audience compares you to inside AI search Ahrefs Brand Radar gives you all three. Step 2: Define the entities that matter Add brand variations and domains for every competitor. Simple version: main brand and main domain. Full version: products, sub brands, alternate sites. Save this setup as a preset so you can refresh it quarterly. Step 3: Benchmark your AI visibility Track four numbers. - Mentions - Citations - Impressions - AI Share of Voice Then compare yourself against competitors across AI Overviews, ChatGPT, Perplexity and others. Document the numbers so you can measure progress over time. Step 4: Study how AI talks about you It is not enough to show up. How you appear matters. Check: - Volume of mentions - Placement inside responses - Depth of coverage - Positioning and framing - Sentiment - Credibility language Ahrefs research shows branded web mentions correlate most with showing up in AI Overviews. So this analysis tells you exactly what to reinforce. Step 5: Compare Share of Voice for your core topics List the topics where you want leadership status. Search each one in Brand Radar. Check your Share of Voice against competitors. - Note irrelevant topics to drop - Note extra competitors tied to the topic - Note unbranded opportunities you can win - Note attributes AI associates with the topic Step 6: Find top cited pages and patch content gaps Open the cited pages report for each competitor. Look for gaps in visibility, topics, formats and freshness. Are they writing about topics you ignore? Are they publishing formats you lack? Are they updating content more often? Create or refresh pages to close those gaps. Step 7: Compare brand mentions across the web AI often relies on third party content to talk about brands. Not your own site. Use the web pages report to collect competitor mentions. Export them, delete anything coming from their own websites, and line everything up in a spreadsheet. Where you see empty cells, those are missed opportunities. Then study how they earned those mentions. Look at content types, publishers, triggers, and distribution habits. Step 8: Turn everything into clear priorities Summarize the whole analysis into three buckets. Fix: visibility gaps. Build: content areas where competitors lead. Influence: publications and sites that shape AI answers in your niche. Run this monthly or quarterly. You will see exactly why competitors appear ahead of you, and you will know what to do next.

As businesses face increasing pressure to address climate change, eg the EU’s Carbon Border adjustment mechanism coming into force very shortly, AI and blockchain are emerging as key technologies in the drive towards sustainability. These innovations go beyond simple compliance—they offer new ways to promote transparency, accountability, and resilience. One example of this shift is how companies like Changeblock are reshaping the carbon credit market. Through the use of AI and blockchain, they’ve created a platform where carbon credits can be traded with greater confidence and reliability. This helps to solve long-standing issues of credibility and trust, turning carbon credits into a valuable tool for sustainability. Moreover, Changeblock’s Systems Monitoring Technology (SMT) integrates AI, IoT, and blockchain to provide real-time insights into sustainability projects across the globe—from biochar projects in Zambia to clean water efforts in Kenya. This enables businesses to meet environmental standards with precision and speed, ensuring that sustainability becomes an integral part of day-to-day operations. For businesses, the challenge is no longer about whether to embrace AI and blockchain, but how to leverage them to build a future where transparency and trust underpin success. The companies that adapt now will be those that thrive in the sustainable economy of tomorrow. For more information: https://lnkd.in/dAGaMZu3 #Sustainability #AI #Blockchain #BusinessLeadership #CarbonCredits #FutureGrowth