Marketing

Amazon's $68 billion ad machine now has access to 190 million Netflix viewers. Here's what it means for advertisers. Amazon's ad business makes $68 billion a year. Now advertisers can target right audiences on Netflix through expanded targeting capabilities via Amazon DSP. Starting Q2 2026, brands buying ads on Netflix through Amazon's platform can now use Amazon's shopping data to target their 190+ million viewers. Think about what this means. Amazon knows what a huge chunk of U.S. households buy, browse, and search for. Netflix knows what they watch. That data is now being combined for targeting. So a skincare brand can target someone who searched for serums on Amazon - while they're watching a show on Netflix. Here's why this matters: → Netflix made $1.5 billion from ads in 2025 and is targeting $3 billion this year  → Early tests are already beating previous benchmarks → A large share of new signups now choose the ad-supported plan. Till now, streaming ads were about showing up in front of millions and hoping it works. This changes that. Now brands can connect what people watch to what they actually buy. For anyone running ads, this is worth paying attention to. Shopping and streaming just became one ecosystem. How do you think this will change the way brands plan their ad budgets?

WOW - Claude Code is an analytics beast. I just had CC (via Google Workspace MCP) analyze mountains of my inbound emails from 2024 and 2026 to find patterns of business pitches, buyer options, and sales strategies. I reviewed everything and added my behind-the-scenes intel for each piece. 📌 Save this post. My takeaways: 1. AI is the premium differentiator 👉 In 2024, companies pitched pretty generic sounding "data tools" or storage upsells. 2026, AI features were the main premium unlock 💡 AI business insight: every company can offer AI features or products (whether you should is another question) 2. "Build and create" over "read and consume" 👉 2024 premiums were all about receiving or reading or getting: read this exclusive article, watch this MasterClass, attend this event. 2026 premiums are about producing. Seeing a trend in words like write, edit, dictate, create, deploy, build 💡 AI business insight: people are overwhelmed with info & want more agency, control, and externalization 3. Team and enterprise scaling 👉 The main pitch for products (especially AI products) shifted from "get your personal edge" to "multiply this across your org" 💡 AI business insight: people feel like superusers & realize non-adopters are pulling the average down. They want to raise the system. Executives, this is a system year 4. Buyer also becomes the monetizer 👉 Similar to point 2, but seeing a lot more paths toward monetization being pitched, not just tools to consume. A lot more “hey, you can use us to make even more money” messaging 💡 AI business insight: people are worried about revenue paths & want to diversify. AI messaging is moving away from productivity toward top-line growth (took them long enough!) 5. Compliance and trust as premium features 👉 Lots of companies highlighting compliance, trust, security, or even running trust summits 💡 AI business insight: decision makers still need to check certain IT boxes. Trust in AI is not guaranteed in B2B GTM 6. Human time 👉 Claude actually found the opposite in its analysis (ie that in 2024, people were pitching human time and elite access a lot more & 2026 was more about access to 24/7 AI twins). I'm not confident that's happening across the board. Might vary by industry or stage? Still seeing a lot of office hour offerings from startups, more FDE-sounding language 💡 AI business insight: if your industry has always been built on human access & relationships, double down for your highest value customers. Run more AI avatar tests in market for 24/7 brand access 🔮 My main prediction: the "free tier" of all these pitches will be shockingly capable. It has to be. Raw AI access is racing to free. So if free tier has access to the greatest models or content (maybe not fully unlimited SOTA yet, especially in image/video/3D), then premium becomes more about autonomy (high-quality AI that works while you sleep), orchestration (complex systems and integrations), growth enablement, & trustworthiness

We analyzed 4 million recruiting emails sent through Gem. Most get opened. But only 22.6% get replies. Half those replies are "thanks, but no thanks." We dug into what actually works. Here are 8 factors that drive REAL responses: 1. Strategic timing beats everything else - 8am gets 68% open rates. 4pm hits 67.3%. 10am lands at 67% - Most recruiters blast at 9am when inboxes are flooded - Avoiding peak times alone can boost your opens by 7-10% 2. Weekend outreach is criminally underused - Saturday/Sunday emails get ≥66% open rates consistently - Why? Empty inboxes. Zero competition. Candidates actually have time - Yet few recruiters send on weekends. Their loss is your gain 3. Keep messages between 101-150 words - Shorter feels spammy. Longer gets skimmed - You need exactly 10 sentences to nail the essentials - Every word beyond 150 drops performance 4. Generic templates kill response rates - Generic templates: 22% reply rate - Personalized outreach: 47% increased response rate - Even adding name + company to subject lines boosts opens by 5% 5. Subject lines need 3-9 words - Include company name + job title for highest opens - "Senior Engineer Role at [Company]" beats clever wordplay - 11+ words can work if genuinely intriguing, but why risk it? 6. The 4-stage sequence is optimal - One-off emails are dead. Send exactly 4 follow-up messages - You'll see 68% higher "interested" rates with proper sequencing - After stage 4, engagement completely flatlines. Stop there 7. Get the hiring manager involved - Having the hiring manager send ONE follow-up boosts reply rates by 50%+ - Yet most recruiters don't use this tactic - Weekend advantage: Minimal competition for attention 8. Leadership involvement is a cheat code - Role-specific timing (tech vs non-tech) matters - Technical roles: 3 of 4 best send times are weekends - Engineers check email differently than salespeople. Adjust accordingly TAKEAWAY: These aren't opinions. This is what 4 million emails tell us. Most recruiting teams are stuck in 2019 playbooks wondering why their reply rates won't budge. Meanwhile, recruiters who implement these 8 factors see dramatically better results. The data is right there. The patterns are clear. The only question is: will you actually change how you operate? Or will you keep sending the same tired emails at 9am on Tuesday? Your call.

Six weeks ago, I went underground. Not off the grid. Just deep into the private Discord servers where sneakerheads spot fakes before they hit the market. The Slack channels where CMOs trade budget hacks they’d never tweet. The WhatsApp threads where collectors swap intel like it’s insider trading. I was lurking. Reverse-engineering how trust gets built in dark social. It seems like increasingly, we're seeing public feeds are for performance. And private chats are for proof. Back in 2010, Bitly found 69% of social shares happened in DMs and emails. Today, it’s closer to 90%. These spaces aren't controlled by algorithms, they're ruled by humans. Want in? Here’s how AI can help you: 1. Find the watering holes without wasting 100 hours: Tools like SparkToro reveal where your audience actually talks and track how those spaces shift over time. 2. Decode the language in minutes, not months: Drop top conversations into Microsoft Copilot or Google Gemini and ask: “What slang, inside jokes, or recurring complaints stand out here?” A skincare brand did this and found its audience was skeptical of clinical claims—so they pivoted to raw, unfiltered before-and-afters. 3. Pre-test content before you post: Use Perplexity to analyze which links get shared most in those communities. Run your hooks through ChatGPT and ask: “Would this grab attention in a thread full of X jargon?” Last month, a supplement brand nailed this. They scanned 500-plus Reddit, Inc. threads on workout fatigue, discovered that everyone hated the term biohacking, and switched their messaging to old-school muscle science. Engagement tripled. Your move this week: 1) Pick one niche community, whether it’s Discord, Slack, or a tight-knit Substack. 2) Use AI to extract three insider phrases and identify one unaddressed gripe. 3) Draft content that speaks their language, not yours. High impact means going beyond being data-driven to being community-fluent. And fluency starts with listening smarter. AI can help. #hicm #DarkSocial #SocialListening #AI

The ABCs of Greenwashing 🌍 Greenwashing weakens trust and slows down meaningful progress. When companies present overstated or unverified claims, it creates confusion across markets, misleads stakeholders, and reduces pressure for real change. The cost is not only reputational, it also undermines the credibility of sustainability efforts more broadly. As sustainability becomes a business priority, the risk of misleading communication continues to increase. The pressure to report progress has led to claims that are not always backed by substance. Recognizing the signals of greenwashing is essential to ensure integrity in reporting, communication, and strategy. The ABCs of Greenwashing is a practical reference that outlines common red flags, from vague wording and selective data to unverifiable targets and weak transparency. These signs often appear in sustainability reports, websites, product labels, and corporate campaigns. There is a growing demand for better sustainability communication. However, clarity must come with accuracy. Narratives that focus on ambition without showing results raise concerns. Authentic communication requires alignment between commitments, measurable progress, and public disclosures. Expectations are shifting. Stakeholders, regulators, and investors expect more than general statements. Claims must be supported by credible data, meaningful metrics, and consistent reporting. The absence of independent verification or full scope analysis is no longer seen as acceptable. Regulatory frameworks are evolving to address this. New directives and standards are increasing pressure on companies to validate their statements with clear evidence. This shift will affect how sustainability is communicated, measured, and governed across sectors. Avoiding greenwashing requires clear internal structures, cross functional accountability, and regular review of communication practices. Sustainability performance must be integrated into operations, not added as a marketing layer. This is not a communication issue alone. It is a strategic and operational matter. Claims must reflect business decisions, investment priorities, and outcomes that can be tracked over time. The ABCs of Greenwashing is a reminder of the need for precision, transparency, and consistency. Improving the quality of sustainability communication is essential for building trust, reducing risk, and advancing long term business goals. #sustainability #sustainable #business #esg #greenwashing 

Website traffic was a valuable metric correlated to growth. Now it may be a vanity metric, not correlated to growth. Search has been disrupted. Visits to your website are declining. So, marketers - what now? The search landscape was already shifting (I talked about this at INBOUND last year). Now, the change is accelerating dramatically: - AI Overviews appear in 43% of Google searches – when they do, organic CTR drops by nearly 35%. - Google’s AI Mode and audio AI overviews are coming – they will cause clicks to collapse further. - More buyers are using LLMs to find information, ChatGPT search in Europe grew 3.7x in six months. So, what should marketers do? And how can AI help? 1. Be everywhere and diversify your channels The days of relying solely on Google search are way over. You need to show up on YouTube, LinkedIn, Instagram, podcasts, and in niche communities. The good news? AI makes multi-channel, multi-format content creation scalable – even for small teams. 2. Be specific with context In the past, broad informational content was the way to rank in Google. Today, buyers expect results deeply relevant to them, whether they’re on Google, LLMs, or Reddit. You need specific content that reflects your expertise and resonates with your buyers. 3. Optimize for conversion, not clicks Traffic was once the lever you could pull. Now, conversion is where the opportunity lies. AI enables you to deliver personal messages that drive better conversion. Don’t ask, “How do we get more blog visits?” Ask, “How do we convert more prospects into customers across all channels?” The changes in search are sending shockwaves across marketing teams and media companies everywhere. The era of traffic-based marketing is ending. But a new era full of opportunity is just beginning. Super exciting times for marketers to reinvent the playbook!

We talk a lot about how brands can connect to women. But here’s where I think the conversation goes wrong: Women are not one group of like-minded consumers. The category of “women” comprises 4 billion people with different preferences, professions, purchasing habits, and personal lives. So how can brands connect with women? Authenticity. I'm talking about the kind of authenticity that comes from truly understanding, representing, and serving the people your brand reaches. Why does this matter? Let's look at the numbers first:  • Women are overseeing $32 trillion in spending globally.  • By 2028, 75% of discretionary spending will be controlled by women. These aren't just statistics—they're a wake-up call for brands trying to connect with women. Brands historically miss the mark when they focus on women as "consumers," rather than as people. Take Dove's work with the CROWN Act, a movement and legislation aimed at prohibiting race-based hair discrimination in workplaces and schools. By bringing attention to how women of color—particularly Black women—have historically been told how to wear their hair at work, Dove drove meaningful change that extended far beyond marketing. The result for Dove (and its parent company Unilever) hasn't just been products sold, but actual legislative change—all because they stood for something that impacts the day-to-day life of their consumers. The key to the consumer paradigm: You cannot effectively serve women if you don't represent them at every level of your organization. Women continue to hold relatively few leadership positions in industries primarily serving women. The fashion and beauty industries, for example, are dominated by male leadership. When brands get it right, it shows. A few examples? FERRAGAMO appointed a female CEO back in 1960—long before it was trending—and that commitment to women in leadership has been woven into their DNA ever since. It’s not a campaign. It’s who they are. Or formula company Bobbie, which doesn’t just have consumers, they have devoted brand ambassadors, families, and loyal subscribers. True representation isn't about optics—it's about women making decisions at all levels—from product development to marketing to the C-suite. Maybe we need to retire the word "consumer" altogether. Because if we're talking about real, authentic connections, shouldn't we instead be focusing on people as human beings. It's no longer about thinking what you “should” create to get them to buy—it's about genuinely making that woman’s life better because you know exactly who she is. And your company’s leadership reflects that. 

A very easy way to improve your Amazon ads efficiency by at least 10% Let’s say you’re spending ₹4–5 lakhs/month on Amazon ads. Your ACoS looks okay. Conversion rate seems fine. But your gut tells you—you’re still wasting some money on irrelevant traffic You’re not wrong At Atomberg, we had found that some of our Amazon spend was going toward search terms that had no business seeing our ads: - “cheap fan” -“rechargeable fan” - “usb fan under 1000” None of these users were in-market for a ₹3,000+ BLDC ceiling fan. But we were still showing up. And paying for those clicks. And it’s not just us. I’ve seen 6–7 brands' Amazon ad accounts across categories over the last few years—same problem, every single time The fix? N-gram analysis Takes less than an hour. You don’t need to be a performance marketing expert. But the results compound What’s N-gram analysis? It’s breaking down every search term into its word components—1-grams, 2-grams, 3-grams—and then identifying patterns that consistently drive waste… or conversion. Example: “cheap rechargeable fan for hostel room” turns into: 1-grams: cheap, rechargeable, fan, hostel, room 2-grams: rechargeable fan, hostel room 3-grams: fan for hostel, etc. When you do this across all your search terms, you start seeing the real picture. Why this matters more than just checking your search term report: Search terms ≠ keywords a) One keyword can trigger 100s of different queries. Some convert. Most don’t. You need to find the patterns. b) Waste is diluted across low-volume terms. Maybe “rechargeable fan for hostel” spent ₹300. You ignore it. But what if 12 other queries with “rechargeable” spent ₹6,000 in total with zero conversions? c) Long-tail is infinite. N-grams are finite. You can’t negate every bad search. But you can block the core terms—“cheap”, “usb”, “mini”—once and be done with it. d) It helps you scale campaigns too. You can find goldmine phrases like “white ceiling fan”, “silent BLDC fan”, “fan for living room”—with 5x+ ROAS. Those became exact match campaigns What you should do: a) Pull last 3 months of search term data b) Break them into unigrams, bigrams, trigrams c) Create a pivot with spend, orders, ROAS by N-gram d) Negate high-spend, low-conversion N-grams (e.g., “cheap”, “rechargeable”) e) Boost high-ROAS ones (e.g., “bldc”, “ceiling fan white”) f) Add exact match campaigns g) Rinse and repeat monthly Try it. Guaranteed to improve efficiency at whatever scale you are operating If you want to read an expanded version of the post, link is in the first comment

Haldiram understood something that no one else did: a product isn’t just what it tastes like—it’s how it makes people feel. And that’s where the magic began. Bhujia was common. Every corner of Rajasthan had someone selling it. But Haldiram didn’t just want to sell bhujia. He wanted it to mean something. So, he gave it a name that would stand out in the crowded bazaars. Not just any name—Dungar Sev, after Maharaja Dungar Singh of Bikaner. Think about it. A simple snack, suddenly infused with an air of royalty. What was once just fried sev became a symbol of status, a delicacy that carried the weight of a Maharaja’s name. The people of Bikaner didn’t just buy bhujia anymore. They bought Dungar Sev. And unknowingly, they bought into an idea—a brand. At the time, words like ‘branding’ and ‘marketing strategy’ weren’t common parlance in India. There were no MBAs, no advertising agencies plotting out product positioning. But Haldiram did what modern marketers today struggle to achieve: he gave an everyday product a unique identity and a powerful story. Naming the bhujia after royalty wasn’t just clever. It tapped into something deeply psychological—the human desire for exclusivity. People weren’t just eating a snack. They were consuming something elite, something tied to the grandeur of a kingdom. But Haldiram didn’t stop there. He understood something even more profound: consistency builds trust. As the demand grew, he ensured that no matter where his bhujia was sold, it tasted the same, had the same texture, and carried the same name. And just like that, an unorganized market started getting shaped by a singular force—brand recognition. An iconic Indian-born brand

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]