Marketing

We're about to see an onslaught of consulting and IT services firms going big on working with AI platforms to deploy agents in the enterprise. And if you don’t understand why it’s happening, it’s an opportunity to reset your understanding of how the real world works. The real world will need a ton of help actually getting agents going in the enterprise. Companies deal with significant legacy tech stacks they need to modernize, data in tons of fragmented tools, knowledge that isn’t captured or digitized, and change management needed to actually utilize agents effectively. And they have to do all this while still running their business day-to-day, unlike startups, who can generally just design their organizations from the ground up to deploy agents into new workflows designed for them. This is why there is so much opportunity for companies (software or services) to actually deploy agents in specific domains and workflows. This remains a big opportunity for both existing services providers but also tons of new services startups as well. Every new technology wave produces a new era of consulting firms that can deliver on that technology. We're seeing this a ton at Box, both in partnering with new forms of technology consultancies as well as existing systems integrators that are building out all new agentic practice areas to help enterprises work with their unstructured data and agents. These service providers will have the benefit of being able to work across multiple data platforms, as well as see common practices that work or fail within an industry. This knowledge ends up being incredibly valuable right now, especially given how fast things are changing. A corollary to this is also that the forward deployed engineer (FDE) model is going to be alive and well for a long time because companies will want to have their vendor actually help drive the change management and implementation for their new workflows. There’s no shortcut to getting this work done for the enterprise, and the vendors are going to have to do a lot of this or risk low adoption. All of this type of work is going to be in high demand for quite some time, and it's incidentally another example of jobs that aren’t actually going away.

Amazon just pulled off the biggest power shift in advertising since Google took over search, and most marketers are sleeping on it.   Netflix, Spotify, and Roku have opened their ad inventory to Amazon's DSP, putting purchase data from over 300 million shoppers directly behind your streaming campaigns.   I've been in digital marketing for over two decades, and I haven't seen a consolidation moment like this since the early days of Google Ads.   You will learn:   — Why Netflix, Spotify, and Roku collapsed their walled gardens and handed Amazon control of streaming ads  — How Amazon's first-party purchase data outperforms Google and Facebook targeting in a cookieless world  — The way programmatic advertising works inside Amazon DSP and why one remote click can close a sale  — How to know if your brand is ready for Amazon DSP and what to build toward if you're not there yet

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.

While running Amazon ads and the Amazon business in general, the north star business metric for me has always been TACOS which is the Total Advertising Cost of Sales. Not ROAS or ACOS TACOS is basically all your ad spends as a percentage of revenue. The revenue includes both ads revenue and organic revenue. But more often than not, most Amazon teams focus only on ad revenue and ad spends, forgetting the most important part-organic revenue Very few brands would even be measuring what their organic revenue is on the platform at a keyword level. It is extremely important to take all steps that will increase organic visibility and organic sales in the platform. In fact, Amazon Pi Search Performance report gives you the SOV that you have at a keyword level for SP ads, SB ads as well as organic The lead indicator of profitability in the platform are mainly 2 things a) Increase in organic SOV in all generic keywords b) Increase in branded searches Increase in branded searches is more often than not decided by what you do outside the platform. Executing good campaigns on ATL and really good clutter breaking Meta performance campaigns often does the trick here But increasing organic SOV in generic keywords is often a result of what happens on the platform. In Amazon, whatever you do on ads also directly influences organic results. Eg. If you bid and rank top of Search on SP ads for certain keywords and your conversion rates are better than the category on those keywords, Amazon will also start ranking you on top organically for those keywords That is why I have often told that Amazon is a compounding channel and can be run profitably at scale because it rewards good performance with better organic visibility. Because of this, if you could have organic sales and directionally estimate TACOS ( not ACOS) at a keyword level, you could make a lot of optimizations on your ads as well as overall content which would benefit the business Eg: Lets say “mixer grinder 750 watt” is a keyword that I am spending money on ads. I know the ad spends, ACOS and ad driven sales on this keyword. But not how many organic sales I am getting from that keyword and is that improving with time. And since I don’t know the organic sales, I also would not know TACOS for the keyword Ideally I would want both organic SOV as well as organic sales increase for this keyword. Without it, profitability would be very difficult. Amazon doesn’t expose true organic-sales revenue at the keyword level, so any TACoS by keyword metric has to be derived The rest of the post is there in the link in the first comment. Do read and share how you do keyword level tracking of organic sales and keyword level TACOS and how you use the results

The Voice Stack is improving rapidly. Systems that interact with users via speaking and listening will drive many new applications. Over the past year, I’ve been working closely with DeepLearning.AI, AI Fund, and several collaborators on voice-based applications, and I will share best practices I’ve learned in this and future posts. Foundation models that are trained to directly input, and often also directly generate, audio have contributed to this growth, but they are only part of the story. OpenAI’s RealTime API makes it easy for developers to write prompts to develop systems that deliver voice-in, voice-out experiences. This is great for building quick-and-dirty prototypes, and it also works well for low-stakes conversations where making an occasional mistake is okay. I encourage you to try it! However, compared to text-based generation, it is still hard to control the output of voice-in voice-out models. In contrast to directly generating audio, when we use an LLM to generate text, we have many tools for building guardrails, and we can double-check the output before showing it to users. We can also use sophisticated agentic reasoning workflows to compute high-quality outputs. Before a customer-service agent shows a user the message, “Sure, I’m happy to issue a refund,” we can make sure that (i) issuing the refund is consistent with our business policy and (ii) we will call the API to issue the refund (and not just promise a refund without issuing it). In contrast, the tools to prevent a voice-in, voice-out model from making such mistakes are much less mature. In my experience, the reasoning capability of voice models also seems inferior to text-based models, and they give less sophisticated answers. (Perhaps this is because voice responses have to be more brief, leaving less room for chain-of-thought reasoning to get to a more thoughtful answer.) When building applications where I need a more control over the output, I use agentic workflows to reason at length about the user’s input. In voice applications, this means I end up using a pipeline that includes speech-to-text (STT) to transcribe the user’s words, then processes the text using one or more LLM calls, and finally returns an audio response to the user via TTS (text-to-speech). This, where the reasoning is done in text, allows for more accurate responses. However, this process introduces latency, and users of voice applications are very sensitive to latency. When DeepLearning.AI worked with RealAvatar (an AI Fund portfolio company led by Jeff Daniel) to build an avatar of me, we found that getting TTS to generate a voice that sounded like me was not very hard, but getting it to respond to questions using words similar to those I would choose was. Even after much tuning, it remains a work in progress. You can play with it at https://lnkd.in/gcZ66yGM [At length limit. Full text, including latency reduction technique: https://lnkd.in/gjzjiVwx ]

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

10 Ways to Use ChatGPT to Improve Your Copy: (With Simple Copy-and-Paste Examples) 1) Trimming Down Goal: Condense your copy for clarity and impact. Focus on: Complex sentences Redundant phrases Long paragraphs Example prompt: "Trim down this [phrase/sentence/paragraph] of my copy." 2) Finding Word Alternatives Goal: Find better synonyms for certain words to enhance readability and engagement. Look to replace: Fillers Jargon Clichés Adverbs Buzzwords Example prompt: "Provide [adjective] alternatives for the word [word] in this copy." 3) Doing Research Goal: Gather detailed information about your target audience to tailor your copy. Consider: Likes Habits Values Dislikes Interests Behaviors Challenges Pain points Aspirations Demographics Example prompt: "Create an ideal customer profile for [target audience]." 4) Generating Ideas Goal: Brainstorm multiple copy elements to keep your content fresh and engaging. Do this for: CTAs Stories Leads Angles Headlines Example prompt: "Generate multiple [element] ideas for this copy." 5) Fixing Errors Goal: Identify and correct any errors in your copy to maintain professionalism. Check for: Spelling mistakes Grammatical errors Punctuation issues Example prompt: "Check this copy for any [type] errors and suggest corrections." 6) Improving CTAs Goal: Make your call-to-actions more compelling and click-worthy. Play around with: Benefits Urgency Scarcity Objections Power words Example prompt: "Give me [number] variations for this CTA: [original CTA]." 7) Studying Competitors Goal: Gain insights from your competitors' copy to improve your own. Analyze their: CTAs USPs Offers Leads Hooks Headlines Example prompt: "Provide a breakdown of [competitor]'s latest [ad/email/sales page]." 8) Nailing the Voice Goal: Refine the tone and voice of your copy to align with your brand and audience. Consider: Target audience Brand guidelines Advertising channel Example prompt: "Make this copy [adjectives] to suit [target audience]." 9) Addressing Objections Goal: Anticipate and address potential customer objections to increase conversion rates. These could be about: Price Quality Usability Durability Compatibility Example prompt: "Analyze this copy to find and address potential objections." 10) A/B Testing Goal: Create variations of your copy's elements to determine what works best. Try different: CTAs Hooks Angles Closings Headlines Headings Frameworks Example prompt: "Generate variations of this [element] for A/B testing: [original element]."

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.

In today's digital age, leveraging celebrity brand ambassadors has become a popular strategy for businesses, including startups. As someone who's been a brand ambassador for various companies over the years and dabbled in startups myself, I've seen firsthand the ups & downs of this approach. People often ask if it's always beneficial to have a celebrity endorse your products or services. I’ll break it down to the most important things to consider. Visibility - Celebrities bring a massive following, offering increased visibility & reach to a wider audience that may have been difficult to engage otherwise. This exposure could enhance brand recognition & create positive associations in consumers' minds. Credibility -  The right kind of celebrity could inject a dose of credibility into your brand. Consumers may in turn perceive your product as reliable, particularly important for startups aiming to build a solid reputation & carve out a slice of the market. Engagement - Some celebrities are able to forge personal connections with their community. By aligning your startup with a celebrity, you may be tapping into that emotional connection & that community may be more likely to show interest in your brand. Costs - Engaging a celebrity ambassador comes at a price. Even if you opt for an equity-based deal, you still need to allocate valuable resources to amplify the association, potentially diverting funds from other key areas of requirement. Authenticity - The alignment between the celebrity & your product must seem genuine. If the partnership feels like a misfit or forced, the results can be counter productive. Today's consumers are evolved & can sense inauthenticity from a distance. Sustenance - While celebrities can generate a buzz in the short term, building interest & loyalty requires consistent effort & a solid value offering that goes beyond the celebrity association. Your product still needs to deliver exceptional value beyond the initial buzz.. Relevance - Ensure the celebrity aligns with the startup's target audience, values & offerings. The endorsement should make sense within the startup's brand identity & goals. Budget - Assess whether the startup can afford the associated costs, especially including the ongoing marketing efforts. Do not assume that bringing a celebrity on board itself is going to win you the war. It’s just a head start. Long-Term Strategy - A well-crafted partnership should naturally integrate into your overall marketing & branding strategy & solidify your position & bring sustained growth. Timing - Most importantly, remember, spending so much in early stages, or early dilution in equity can have long-term consequences, so ask yourself if you’re really ready at this stage. Ultimately, the decision to engage a celebrity brand ambassador should be based on your unique circumstances & goals. Hopefully this will help some make an informed decision. #BrandAmbassadors #CelebrityEndorsements #InfluencerMarketing