Marketing

I used to think charging less would get me more clients. After my trip to the US I realised it just made them trust me less. when i was cheap, clients questioned everything. "why this approach?" "can we try something else?" "i'm not sure about this." so when i raised my rates, they trusted my decisions completely. same work. different psychology. so here's what i've basically realized about pricing: when someone sees a low price, their brain doesn't think "great deal." it thinks "what's the catch?" they start looking for problems. inexperience. desperation. corners being cut. low prices trigger fear of loss, not excitement about savings. but when they see premium pricing, something else happens. "if they can charge this much, they must deliver results." "other people are paying this, so the value must be there." "the risk of not solving this problem costs way more than the investment." premium pricing signals confidence in your work. think about it. rolex doesn't make better watches from a functionality standpoint. but the price tells you everything about what owning one means. same thing with services. a premium project isn't necessarily 10x better in execution. but the price signals experience, systems, proven results. and here's the shift that changed everything for me: i stopped anchoring clients to the price and started anchoring them to the outcome. not "this costs X" but "this will generate Y for your business, and the investment is X." when they're thinking about ROI, the price becomes secondary. your pricing isn't just a number. it's a signal to the market about who you are and what you deliver.

In a MAJOR ruling for European copyright law, the Munich Regional Court has sided with Germany’s music rights society GEMA against OpenAI, finding that the company’s ChatGPT model unlawfully used copyrighted song lyrics in its training and responses. The decision, issued this morning, marks the first major European court judgment holding an AI company liable for using protected works without a licence. I got into AI through being Director of Legal Affairs and Regulatory Compliance in IMRO, the Irish counterpart of GEMA - and I know the people in GEMA - so this is very interesting to me. The case centred on GEMA’s allegation that OpenAI trained ChatGPT on its repertoire of German song lyrics, allowing the chatbot to reproduce works by artists such as Helene Fischer and Herbert Grönemeyer. The court agreed, concluding that the model’s ability to reproduce lyrics word for word demonstrated that the works had been used in training. It ruled that OpenAI is liable for copyright infringement and prohibited ChatGPT from reproducing lyrics from GEMA-represented artists unless a licence is obtained. The court also held that the European Union’s Text and Data Mining exceptions cannot shield generative AI systems that “memorise” and reproduce copyrighted material. This reasoning undermines one of the primary legal defences AI developers have relied upon in Europe. While damages will be determined in a separate proceeding, the court’s finding of liability alone sets a powerful precedent. OpenAI has announced plans to appeal. The 42nd Civil Chamber of the Munich Regional Court had indicated its position in September, when it observed that the model’s outputs could not be explained without training on copyrighted material. The final judgment confirmed that assessment. For the wider AI sector, the ruling suggests that AI companies operating in the European Union may need explicit licences for any copyrighted content used in model training or risk litigation. The decision also has regulatory implications. It aligns with growing momentum within the EU to enforce transparency and rights-holder protections under the AI Act and the Copyright in the Digital Single Market Directive. The GEMA v OpenAI ruling diverges sharply from Bartz v Anthropic in the United States. In Bartz, Judge Alsup found that AI training on copyrighted material could qualify as fair use, meaning no licence is required when the use is deemed transformative and non-substitutive. He viewed training as an analytical process that teaches the model general patterns rather than reproducing expression. The Munich court took the opposite view, holding that using protected works in AI training without permission constitutes reproduction requiring a licence. This illustrates the growing divide between the U.S. model, where fair use can exempt AI developers from licensing duties, and the European approach, which treats copyright as an enforceable economic right demanding prior authorisation.

TSMC posted a $440 million loss at its Arizona factory. American engineers called it "rigid, brutal, prison-like." Taiwanese managers complained about "lack of dedication and obedience." TSMC’s CEO Morris Chang saw this coming. "A very expensive exercise in futility," he called America's chip push. Taiwan doesn't just make chips. It breathes them. Three decades of alignment created something money can't buy. In Arizona, Americans clock out after shifts. In Taiwan, engineers sleep in the fab. In Arizona, decisions need consensus. In Taiwan, orders flow down. In Arizona, it's a job. In Taiwan, it's national service. Chang knew this at 55 when he started TSMC. The playbook worked because a nation aligned behind it: 1. Bet everything on survival Apple wanted impossible chips. Chang bet $9 billion in 2010 - half TSMC's cash. 6,000 people. 11 months. Round the clock. Because missing Apple meant Taiwan missing its future. 2. Never compete with customers Intel Corporation controlled everything. TSMC said: "We will never compete with our customers." When Nvidia shares five-year roadmaps, thousands protect them like state secrets. 3. Make enemies share factories Nvidia and AMD share production lines at TSMC. Works only when factory workers see both companies' success as Taiwan's success. 4. Turn precision into DNA TSMC's latest machines hit tin droplets 50,000 times per second. In Taiwan, this precision extends everywhere - emails, meetings, weekends. Not policy. Culture. 5. Compound for decades Every supplier grew with TSMC. Every university shaped curricula around them. Chang: "You cannot replicate this with subsidies. You cannot legislate dedication." 6. See the future through customers When Qualcomm fled IBM for TSMC in the late '90s, Chang knew IBM was doomed. Intel built walls. TSMC built bridges. TAKEAWAY: 2007: Intel rejected iPhone chip. Too low margin. Cost them mobile. Then AI. Then everything. Intel's real problem wasn't saying no to Apple. It was believing one company could do it all. Meanwhile, a 55-year-old built something stronger: a nation aligned around making everyone else successful. Today: Every ChatGPT query. Every iPhone. Every Nvidia chip. All TSMC. Not because Taiwan has the best engineers. Because Taiwan made engineering excellence a cultural value. And culture, unlike factories, can't be copy-pasted. — Want the full story of how TSMC became Nvidia's $1 trillion secret weapon? I went deep on the untold details: https://lnkd.in/epuWHu8B P.S. All research links, the audio clip, and the full archive are in the first comment below 👇

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 

The most expensive mistake in business is assuming your customers will never change. Last year, something shifted in Indian retail. Gen Z (377 million) overtook millennials (356 million) to become our largest consumer group, influencing $40-45 billion worth of apparel and footwear purchases. But they're not shopping at the stores we built for them. [Et Retail] Brands watched their growth collapse in just 12 months. → ZARA fell from 40% to 8% growth, [Et Retail] → Levi Strauss & Co. crashed from 54% to 4% growth [Et Retail] → H&M dropped from 40% to 11% growth [Et Retail] Here's why the growth has slowed down: 📌 Gen Z discovered new brands like Freakins and Bonkers Corner, offering trendy clothes at ₹500-800 📌 They chose self-expression over brand loyalty 📌 70% of their shopping moved online, heavily influenced by Instagram 📌 They demanded inclusive sizing (XS to XXL) and unisex options that legacy brands ignored Take FREAKINS, which clocked ₹25 crore in FY2023, or Bonkers.corner, clocked ₹100 crore. [The Economic Times] [Et Retail] These brands understood what Gen Z wanted: crop tops, baggy clothes, Korean pants, and oversized tees at prices that let them experiment with three different outfits daily. Body positivity isn't a marketing campaign for this generation. It's how they think. When they couldn't find the sizes or styles they wanted at premium stores priced at ₹1,200-1,500, they simply went elsewhere. Myntra saw the shift and launched FWD with ₹500 price points. The result was explosive: 100% year-on-year growth and 16 million Gen Z users, who now represent one in three e-lifestyle shoppers. [Et Retail] Legacy brands bet that Gen Z would "grow up" and pay premium prices. Instead, 377 million young Indians chose values over logos. The most expensive mistake in business? Assuming your customers will never change. What changes in your customer base have surprised you recently?

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

Is THIS the best ad campaign ever? In 2015, Sport England challenged ad agency FCB Global to close the 2 million strong gender gap by getting women more active. The agency used the insight that women often feel 'fear of judgement' in exercise, to create the campaign 'This Girl Can'. The campaign is a rallying cry to women to get active in THEIR own way by replacing fear with a 'don't give a damn' attitude. This is shown with bold copywriting, relatable casting, REAL moments (the make-up smudged under the eyes, normal jiggling bodies, menopausal sweat, period cramps, tampon string hanging out your pants) and a true sense of female camaraderie. Since it's launch: - 3 million women were inspired to exercise as a direct result of seeing the campaign - 1000+ social media mentions each day - 37m views across social media - 500,000 active members in the This Girl Can community - Cannes Lions award The campaign is evidence that advertising can make great impact and drive change in many little corners of the world. THIS is the result of a clear brief, unifying insight and - in this case - a dedicated female creative team who truly 'understand' their audience. But more than that, it's the result of a LONG-TERM campaign that has been running for almost decade, and continues to re-engage the audience in various different ways, globally. I think there is such a short-term mindset in advertising nowadays. Mainly due to the fast-paced nature of social media, the need to 'go viral' and the economic need for performance marketing tactics to generate cashflow. But without the longer-term brand campaigns, we are missing the ability to build strong narratives and make REAL change in the world. And with that, stronger brand salience, brand love and LEGACY. This is an element of advertising that I fell in love with years ago. And an element that I see really defining which brands stand the test of time, an which fall apart years down the line.

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 ]

Just because "google" shows up in attribution doesn't mean it's what driving buyers to buy. Ask "how did you hear about us" in a free-text required field upon conversion and you'll get the real stuff: -Social media (LinkedIn, Tik Tok, Reddit, Instagram, etc.) -Podcasts (owned, earned, paid) -Communities (Slack, discord, private groups, etc.) -Referrals / Word of Mouth (colleagues, friends, investors, etc.) -3rd party events (e.g. I saw your CEO speak in Belgium last summer) ^^These insights will RARELY or NEVER show up in attribution software. Most B2B companies never ask this question. And most B2B companies don't actually know what's creating their demand. #attribution #revenue #sales #marketing #b2b p.s. This is not meant to be a replacement to digital touchpoint based attribution. It's a different measurement strategy used for a different purpose - to know what buyers report as the most *impactful* touches. p.p.s. Self reported attribution is a *directional* insight that you get directly from customers. Many marketing activities will not get measured by touchpoint based digital attribution and we need another strategy to measure these - podcast, social media, connected TV, Out-of-Home (OOH), referrals, influencer marketing, word of mouth, etc. p.p.p.s. Most companies don't get value from self-reported attribution because they don't use it properly. Require it for all declared intent submissions. Copy it from the lead/contact to opportunity object. Track the results against qualified pipeline and revenue, not just "leads". p.p.p.p.s. Self-reported attribution is 1 of 6 different measurement strategies we use at Passetto to analyze the impact of all GTM Investments. A one-size fits all approach of using touchpoint based digital attribution to measure all Marketing, Sales, and SDR investments is a losing strategy.