Let AI handle the chores, and humans do the thinking: such should be the future of content marketing. In this piece, I try and debunk a few myths. Firstly, generative AI  can be creative — and often is. Secondly, AI doesn’t necessarily make us stupid; we don’t need it for that. And thirdly, becoming a prompting Guru isn’t necessarily the key to producing great content. The question of AI’s role in content marketing is actually more strategic than technical: it’s about why and for whom we create content. This is the major issue at stake for today’s and tomorrow’s marketers. In this presentation, I urge readers not to outsource their thinking to AI, and rather offload the chores of low-value tasks to machines. Unfortunately, it should be noted that they aren’t always doing a good job with that.

TL; DR

Let me introduce you to Ms Marie Bernard. This pretty young woman, somewhat artificial in appearance, exists only in Midjourney’s archives and on the website of “her” SEO agency. This supposed e-commerce expert found herself embroiled in a semantic mix-up that was both amusing and revealing.

Taking inspiration from one of my articles, this visionary author mixed up ‘snow globe’, an expression used by one of my expert interviewees as a metaphor, and ‘snowball effect’. Thank God, she inserted a link to Visionary Marketing so that I could correct that fatal mistake. Far from being trivial, this anecdote raises a few fundamental questions. Who is writing? For whom? How? And for what purpose? In fact, it even poses bigger questions such as “what is humans’ place in society, and what sort of society do we want for our children and children’s children?”

Content about generative AI is so ubiquitous that we have gone past information overload. AI content analysts are skirmishing via X (formerly Twitter) and LinkedIn posts, mainly on the technical front (this AI is better than that one), creativity (AI produces interesting ideas or rather, is dull and inferior to humans), and usage (“download my ultimate prompting guide!”). Yet all these debates (and sadly others that are less prevalent, like the poorly documented issue of energy consumption) fail to address other key questions: who are we creating for, why, and for whom do we work — or more broadly, what kind of society do we want in the future? 

AI, and in particular generative AI, have generated most of the noise on social media, blogs, newsletters, and chat around the pub. Traditional economy seems to be ignoring the phenomenon or treating it as incidental — a recurring habit when it comes to digital innovations, but online debates live on unabated.  

Whether and how we should use generative artificial intelligence is now a central question in our modern societies, and that’s understandable. Machines have been able to play around with text since the 1950s, but computing power and large-scale training on such a vast and decent dataset — despite criticisms — have never been so strong. In recent weeks, engineers in London have even shown how two AI bots can talk to one another. Even if it’s only a demo, we’ve known since the early 2000s that machines can buy and sell stock (algorithmic trading roughly amounts to 60-75% of total trading in the most developed markets, and this was already true back in 2006 when I worked in that field). So, why shouldn’t an AI known as “agentic” buy train tickets? 

Hence these legitimate questions. 

The fact that a programme — literally a “machine” in the sense of a computer — is capable of writing like humans, or nearly, is disconcerting. 

*[Machine] A mechanically, electrically, or electronically operated device for performing a task

first entry from Merriam-Webster

What’s even more unsettling is that humans often write more poorly than machines. This is what Loubna Ben Allal, a researcher at Huggingface and an expert in training generative AIs, describes in a video on the underscore channel, which is worth watching.

She explains how content is filtered during training sequences and, surprise, surprise, she says that good Ai-generated content is often better than bad human content. Sadly, poor human content is everywhere. 

Note that there are also texts, 100% AI-generated, aimed at proving that Loubna is right. 

If these mock texts are so disconcerting, it’s because language and the written word are indeed some of the fundamental characteristics of the human species. 

In the beginning was the word. Language is the operating system of human culture.

Yuval Harari — NYT March 2023

Yuval Harari, with a kind of reverse anthropomorphic twist, even calls it the “operating system of human culture”. Despite this idiosyncrasy, Harari is zeroing in on the real issue.

The real core problem isn’t technical, but deeply philosophical, especially when the most famous generative AI tools are led by a maverick who’s trying all he can to put us in a Spike Jonze film. Ultimately, philosophy could or should redefine how AI is trained, explain Michael Schrage and David Kiron of MIT Sloan Management Review.

The real problem with these generative tools isn’t technical, nor is it about creativity or even how well one uses the tool. It’s more fundamental, relating to the very essence of work and, more broadly, of human societies. Whatever human shortcomings and flaws there may be, and they are indeed numerous. 

This is all the more important, given discussions about new tools such as Manus, which promise even more autonomous intelligence capable of “agenticity”, a direction that appears to be a goal for many of the creators of these programmes.   

There’s no point in playing down generative AI, as I saw here and there, by predicting their demise (you don’t just eliminate tools that the whole world has made their own, no matter how imperfect), nor in overestimating their potential (there are simply too many tools and possible uses). 

Denying how astonishing these tools are is pointless.

Likewise, describing LLMs as “stochastic parrots”, is no longer relevant. It used to be apt, barely two years ago. Yet, that’s no longer the case. Safety nets exist, the biggest pitfalls (such as asking ChatGPT to prove that the Earth is flat) as former Apple Siri cofounder Luc Julia claimed recently in a Swiss daily are old hat. The right way forward is hybrid systems combining the power of LLMs with more conventional computing. It’s a matter of time before this merger is done and it might not even take too long. Whoever has witnessed the development of IT and the Web over the past 40 years knows it takes time to innovate. Time is of the essence.

Hence, even though the results we get today are still often disappointing, patchy, or downright wrong, GenAI models of 2025 hallucinate far less than they used to, provided you pay and pick your model carefully.

You may check for yourself with Perplexity.ai, which will answer your question on this subject while delivering links (sometimes off-target, so you’ll still have to cross-check that information). 

In short, four breakthroughs occurred from 2024 to 2025 in this field: 

Still, hallucinations remain common and won’t vanish soon. Again, it will take time before all control mechanisms are in place. Chain-of-thought is one example: it’s still a bit awkward, but it gives a flavour of future possibilities.

That said, even if I’m not a big fan of AGI (see the following article), generative AI challenges human skills and abilities and as a consequence of that, our very place within society. 

Essentially, there are three areas that need to be investigated. First, our capacity to be truly creative. Second, AI’s impact on our cognitive and intellectual abilities and finally, there’s the question of usage.

Obviously, one could wonder whether GenAI is creative or not. But above all, this very question challenges us, humans. Thus, the real question should read: are humans any more creative than GenAI?

The answer isn’t straightforward, even if that may come as a surprise. One could argue that GenAI texts are good or bad, depending on one’s point of view. Yet, one shouldn’t discount that texts produced by humans aren’t always better. And that’s what’s disturbing. As we mentioned above, Loubna Ben Allal calls into question the notion that “human = good, synthetic = bad”. 

The same applies to creativity. Alan Turing, in his 1950 piece Computing Machinery and Intelligence, had already invalidated a number of objections to the idea that a machine could be innovative. One of these objections claimed: “A machine can’t create.”

Creativity is also, and above all, about combinations, de-combinations and recombination. A bit like a puzzle if you wish. One cherry picks from others’, or even one’s work, sometimes unconsciously, and recombine from this to build a new story, a new blog, a new project. Even artists aren’t necessarily all that ‘creative’ in the sense of making something new entirely from scratch. They often rely on self-references. Tinguely with his zanyish machines aka antimuseums, Monet and his views of Rouen and his infinite variations on water lilies, Soulages with his black paintings, Rothko with his ubiquitous RED. Series are an integral part of Art, and one of the main creative mechanisms. 

Jonathan Gibbs in Randall even states that Young British Artists, as all artists, can at best come up with four genuinely original ideas in their entire career, the ones we’ll remember them for. 

‘The way it works is that you’re only going to be remembered for four things.’

Gibbs, Jonathan. Randall or The Painted Grape

And Gibbs is right. If artists give in to reproducing their own ideas, that’s also because it’s what people are asking for. That’s why, for instance, minimal music these days — once dubbed repetitive and lately rebranded ‘neoclassical’ (Max Richter, Nils Frahm, Nicklas Paschburg, GrandBrothers…) — is so successful. It’s principally because it’s based on a never-ending repetition of fairly similar musical patterns. And I won’t even mention popular — as in ‘pop’ inclusive of jazz — music, which is even more standardised (check rhythm changes if you don’t believe me). 

Thus, the question of whether machines are more or less creative than humans is anything but trivial.

The next question is whether we end up being stupid from the misuse of these thinking machines (as one of my friends put it to me, “These tools are extremely addictive”). This question echoes what Nicholas Carr wrote a few years ago in The Atlantic: “Is Google Making Us Stupid”?

In that piece**, he argued that even though he wasn’t raised in the digital age and learned to read “normally” in books, he ended up using search engines and found that they made him lazy, encouraging minimal effort rather than combing through documents for hours before forming an opinion.

** yet another AI-written piece, by the way. I only inserted the link out of mischief. Our dear readers will find the Atlantic link by themselves using old-fashioned search engines or Perplexity.ai. 

With generative AI, all that Carr described is blown out of proportion. Perplexity.ai is the epitome of this issue. Instead of using search engines, one enters a prompt, and hey presto! Perplexity will gather the answers, summarise them and provide a list of links. The latter are not always relevant, but on average, they’re not that bad either. This process isn’t really less effective than wading through a so-called SERP (Search Engine Results Page) of questionable relevance or provenance, many of which results were written by ‘SEO experts’ to trick the very same search engine (i.e. Google, see this post for details).

Some years ago, those SEO experts had such low quality texts made by hand, often in low-wage countries, and now they create them almost entirely with LLMs (it’s estimated that about 19% of Google’s top 20 results are AI-generated). By the way, those people in low-wage countries must have been made redundant but who cares about poor people struggling to make a living. This is a dog-eat-dog kind of world, is it not?

As to the question of whether generative AI is making us stupid, it’s a bit disingenuous, just as it was for Google. But what’s certain is it could be making us lazy (again, just like Google, especially since they introduced position zero). 

Getting direct answers to our questions means we lose the habit of digging for them ourselves, and above all, we lose your critical thinking abilities. But it’s not that simple. 

Concluding that AI alone is responsible for dumbing down the world — assuming that’s even happening — would be going a bit far. Intellectual laziness and lack of critical thinking aren’t new, and if you want to see evidence of that, I recommend you browse the site of the Reboot Foundation.

Our third angle is usage quality of AI tools. Is it a real problem?

There’s indeed a misconception about the usage of these tools by the population. Their usage is certainly widespread and it happened in a flash. That’s for certain. Now, whether most users are wielding these tools properly is another kettle of fish. Believing we’ve all become prompt experts overnight is spurious. I’m not seeing that happening in the field. 

For starters, we have a massive digital skills deficit. How can people who struggle to remember a password or sign a PDF form could instantly be able to use generative AI effectively?

I see far too much straightforward copy-pasting in class and elsewhere. Also, few people, as I noticed in the course of my training sessions (thousands of people and students), are able to take the necessary step back to refine the content produced by these algorithms — even when encouraged to do so.

However much I regret this isn’t relevant. That’s why Steve Yegge is right: generative AI won’t help beginners nor average employees become brilliant, but it will help experts get rid of them altogether. Getting started in business won’t be easy in the coming years.

Moreover, the generative AI scene is so hectic and unstable that even experts are losing track of which model is most effective. Almost every day, there’s some headline-grabbing announcement overshadowing yesterday’s. 

And the ‘experts’ keep dishing out their analyses and forecasts. Some foresee the demise of generative AI (but that’s total nonsense), while others predict that GenAI will on the contrary be an all-out revolution (which is equally silly). 

The truth is, as we can see in the field, that we are in a learning curve, which isn’t too different from what we’ve been through with other digital innovations in the past.

As a system grows more complex, Kathy Sierra showed, you end up losing your grip, and a user who once felt in full control of the tool finds he or she loses that control and is going backward dramatically.

More recently, Maurizio Bisogni described the fluctuation of knowledge in ChatGPT over time in relation to what he calls the Dunning-Kruger effect, a psychological phenomenon identified by David Dunning and Justin Kruger in 1999 in their paper: “We lack competence and we don’t know it: how difficulties in recognising our own incompetence can lead us to overestimate our abilities.” 

This shows we have a tendency to overrate our abilities when we have too little information. A warning we might well direct at many of the analysts clogging up our social timelines with their views on the subject.

Conversely, the most expert people tend to underestimate their competence. This is something we also know as the ‘impostor syndrome’. Perhaps I suffer from the latter myself, because I hate the term ‘expert’. Even though I’ve been working in digital marketing for over 30 years, started my career in AI nearly 40 years ago, and have been documenting these topics for decades, I still feel I don’t know much. It seems only natural and necessary, given how volatile and complex this environment is.

Yet I see too many experts, some of whom are even behind some of these discoveries, such as Jeffrey Hinton, one of the discoverers of neural networks and winner of a Nobel Prize, who understand nothing about generative artificial intelligence, despite the fact that it is based on these very neural networks. In a BBC video, Hinton looks at ChatGPT and concludes that these machines can reprogramme themselves. In the long term, this is undoubtedly true. But it is not yet the case. I’ll come back to that later. So we need to have a certain humility when it comes to these subjects.

With all due respect for Hinton’s outstanding achievements in machine learning, he shows a nearly childlike ignorance when he claims neural networks can feel emotions. […] Emotions are so complex, a bridge between thought and will, a gateway to shared understanding between people and the world. By saying the machine can experience feelings, Hinton shows that he doesn’t understand what he’s built.

Robert M. Burnside – Robo Robert on Substack (2024)

I’m not writing this to diminish the great talents of the renowned British-Canadian scientist, Jeffrey Hinton, a Nobel Prize winner in the field of neural networks, but to illustrate how siloed these areas can be and how no one can honestly claim complete understanding of the field of AI, if there is any such thing. As for LinkedIn influencers’ rush opinions…

Truth be told, regarding usage, I don’t believe that a science of prompting — which I see more as a practice of common sense, trial and error — is essential. What I find vital, rather, is taking a step back, thinking carefully, applying reason, and sharpening one’s critical thinking skills. 

Besides, returning to prompts, I had already guessed we’d see prompt generators appear. And here they are, because that kind of interface is cumbersome and awkward. Prompting is powerful, but lengthy and tedious, requiring voice dictation abilities (which most people don’t have) or quick and accurate touch-typing, which is basically only for those who learned on an uncompromising typewriter, and/or how to touch-type without looking at the keys, like yours truly. 

To be honest, I create all my Midjourney prompts on Claude or ChatGPT because I find the exercise quite tedious and slow, and LLMs are best placed to tailor a prompt for another generative AI in the required style. 

Some folks even bet that chatbots will chat to each other in a language only they can understand (see this video. Careful! It’s not a product but a demo made during a hackathon). 

In short, usage doesn’t strike me as a major problem, even if most users’ results are far from great — even when guided. 

Let’s rule out a couple of areas at once. Ecological issues to start with. Apart from a few ritualistic mentions and greenwashing initiatives, not much appears to be on the menu in that area. As someone who’s been strongly committed to environmental concerns for ages, I’m deeply saddened about that. I will have to bite the bullet, nobody cares about that. And the current T**mpmania isn’t going to help.

Bubble threats are real too, as Ed Zitron keeps hammering. Yet, the history of innovation has always shown that when some tech stuff is needed and the whole world is using it, money will always be found and invested. When there’s a will…

Web rot is probably a good avenue for our quest. I predicted it as soon as generative AI first emerged and GPT-3 was launched in 2020. Back then, I forecasted during a Pushengage webinar that the Web would be flooded with SEO content no longer created by humans but by machines. The latter deliver both quantitatively and qualitatively better (according to the standards of these ‘SEO experts’) than the armies of content creators from low-wage countries paid to boost webpage rankings through back-linking. 

As it happened link-building requests died out instantly in 2023 and were replaced by proposals for AI-generated content creation. I saw them crop up on Visionary Marketing immediately, and the change was savage. SEO content became more professionalised and multiplied at a frantic pace, as The Verge showed in its 2023 investigation of synthetic content farms. 

The result today is conspicuous. 

What was foreseeable has indeed happened. It took five years. So much for those who talk about an overnight revolution. Even for something as simple as replacing human content writers in low-wage countries with LLMs that churn out copy at high speed with a few basic instructions, it still took five years. As for the rest, we may have to wait a bit. By the way, we’ve laid off hundreds of impoverished people unless they’ve retrained for AI-based content, which is likely but not proven.

That’s the genuine underlying problem. And it’s why I created humansubstance.com with some friends.

A group of stubborn bloggers who decided to write with their hands and their brains, not with machines. Like this 4,000-plus-word article that I could very well have churned out in three seconds using ChatGPT — assuming ChatGPT can count words and by Jove it can’t.

Because there’s the hitch: we do need artificial intelligence to take out the rubbish and count words, fix our grammar, punctuation, and spelling mistakes. 

But we don’t need it to think in our place. And if the Web is rotting, or ‘enshittifying’, to borrow Cory Doctorow’s term, that doesn’t necessarily mean the end of real content marketing (genuine content, not SEO fodder).

It may not happen on the Web and this is sad news for Sir Tim Berners-Lee. Quality content will always find a way to be shared. If not on the Web, then somewhere else. Perhaps my vision is somewhat naïve, but I’ll own that. I’m inclined to believe good things can still and always happen. Let’s assume I’m wrong; at least I will die happy.  

I also see plenty of players, analysts, and professionals around me who think, search, dig deep, and document beyond the surface. They don’t buy into the big headlines from generative AI evangelists, who increasingly come across like transhumanists, to quote Jean-Gabriel Ganascia. 

Make no mistake, I have nothing against generative AI. I just want it to take out the bins instead of trying to think in my place. And when I see some of the results from these tools, I’m not convinced the game is over yet. 

I use them a lot for preparing my lectures (most of which aimed at training students to keep enough distance to interpret these tools’ results rationally rather than emotionally), to summarise my articles for my students’ presentations. 

But I’m always the one doing the thinking, and all I want from these tools is to take out the bins and turn my most relevant punchlines into PowerPoint. 

Why? Because copying out your own words into PowerPoint is basically a chore. And that’s why, for my keynotes I refrain from using slides. Those addicted to PowerPoint can still download the slides from my blog if they wish.

Finally, at the heart of this debate about AI’s role in content marketing lies a big confusion about the automation of creative processes, which aren’t continuous. It’s an illusion to think you can simply press a button to get a result. Sure, you get some sort of result, but which one and what value does it have? For an SEO content producer (I can’t get down to call them ‘authors’, sorry) it’s probably a thousand times better and faster than what a human being could write. But automating such tasks for true authors, those who write with their brains and for their readers, not a Google bot, gives you the impression you’re saving time, whereas reality is often radically different. Randall Munroe illustrates this brilliantly in his schematic about coding. And it’s even more apt for content marketing. 

And all that AI SEO copy for what outcome? More efficiency? Neil Patel shows otherwise in the following chart. 

So, to wrap up this article, I urge you never to relinquish your capacity to ponder nor your critical thinking skills. Certainly, humans are prone to error. Sometimes they’re even worse than LLMs, as Kevin Roose demonstrated in the New York Times. And that’s the real tragedy. 

Even if general artificial intelligence is probably an overstatement (we can’t define it anyway), insisting the opposite — that all humans are brilliant — is an even bigger mistake.

But despite these flaws, it was us, humans, who built these machines. It’s our job to use them for the better, not for the worse. It’s up to you to do the thinking and let AI do the chores. That’s what ought to be.

The post Chores to AI, Thinking to Humans appeared first on Marketing and Innovation.

Isn’t the notion of “disruption “, aka disruptive innovation, used and abused by analysts and technology experts? And by dint of abuse, aren’t we in the process of deluding ourselves? At a time when some are fretting about the volatility of the business generated by ‘unicorns’ or even centaurs, it is perhaps worth asking whether we have not entered an innovation bubble, yet accentuated by that of generative AI, marked by the correction of technology values and a return to more traditional values. Yet it may be too early to find out about the reality of such disruptive innovations. Here are my thoughts about the subject with a few references to sources and books I found interesting.

While some have been claiming that we are living in a bubble of innovation (here, here and here for instance and here and here with AI), it has to be said that not everyone always agrees. Especially with the advent of the so-called GenAI revolution.

In this piece, she explains what role disruptive innovation is playing in contemporary markets. She argues that disruption is not just a fad, but something more profound.

Forget all about unicorns, here come the centaurs! Bessemer Venture Partners – State of the Cloud 2022The hype surrounding disruptive innovation is overwhelming. Here is what I found here and there:

Despite this, and the spectacular performance of some companies that have established themselves in just a few years to the point of throwing a spanner in the works of well established markets and provoking defensive reactions, some observers maintain that we are facing an innovation bubble.

And these same observers point out that the expectations they have of these disruptive innovations are not in proportion to what they could deliver.

And when expectations exceed what innovation can deliver, disappointment occurs. As described by Gartner in its “Hype Cycle” with what the US analyst group calls “The trough of disillusionment“.

“The reality is that business disruption is not a fad. It is not a set of buzzwords you need to use in planning meetings, and it is not a way of positioning a brand in the marketplace. ,” explains Joanne Jacobs.

In her view, the only real break is the one that results from the convergence of three elements:

It is the combination of these three ingredients that she believes makes disruption a reality or a fiction.

We could, however, add a few important ingredients to this recipe:

In recent years, we have seen the emergence of new entrants in markets that were thought to be stable and saturated:

“AirBnB entered the top ranks in terms of hotel market capitalisation and Uber represented the world’s fastest-growing car rental with driver service,” Joanne wrote in 2015. These businesses were seen as a real challenge to such established markets. 

But what is the situation today? There was a clear wake up call in the high-tech industry. No more valuations without sound business results underpinning. Well, maybe.

So the question arises, is disruptive innovation overhyped? Vaclav Smil, the famous author of “Numbers Don’t Lie” answers bluntly that we are mistaken. According to him, the most innovative period in human history was the… 1880s ![/caption]

According to the worshippers of the e-world, the late 20th century and the two opening decades of the 21st century brought us an unprecedented number of profound inventions. But that is a categorical misunderstanding, as most recent advances have been variations on two older fundamental discoveries: microprocessors […] and exploiting radio waves, part of the electromagnetic spectrum.

According to Smil, and others, we may be living in a context that reflects a flowering of innovations, an accumulation of gadgets that are more or less important or distracting, but in which we are unaware of the importance of the underlying innovations.

To mimic smil’s deliberately cursory demonstration, we marvel at our little computer phones, but fail to take into account the importance of the work of Nikola Tesla to whom we owe the industrialisation of alternating current. Tesla died in debt and lonely, but without him, none of these gadgets would exist!

Many of the innovations we use are undoubtedly incredible – and I never cease to marvel at those communication tools we wield. Yet, does that mean that all these contraptions are truly disruptive?

Reading this article from 2015 today as the clouds gather over tech stocks and others are towering up is interesting. Where do we stand on “disruption” at a time when, as Smil has it, there are many more important things than that in the economy.

In 2015, “[…]A whole range of disruptor companies from DropBox and SurveyMonkey, to the secretive Palantir Technologies and audacious SpaceX”, were redefining the way organisations were communicating, researching and developing products, Joanne explained.

Even then (2015 seems a long time ago) she was rejecting the idea that we were living in an “innovation bubble”. At the very least, she recognised “a bubble of disruption hyperbole”.

30 years after the development of the commercial Web, we have the necessary hindsight to see what has really changed under the impetus of these ‘disruptive’ companies.

And I have miwed feeling about the result. Both naysayers and proponents of disruptive innovation mays disagree with me, though. Regardless, I have looked at both sides of the equation, and the pros and cons of that so-called disruption.

According to Eric Van Susteren (Momentive’s head of Brand Content strategy), there are 5 pieces of evidence that this disruption exists:

Better still, McKinsey proved to us back in 2019 that the pace of disruption was accelerating in its report “navigating in a world of disruption“:

But what will be left of all this in a few years’ time? Now that Covid has been forgotten, what has become of the ‘great resignation’? Will the wave of wealthy urbanites fleeing to the country last? We saw flock to the Pyrenees a few years ago and now they are all returning home to make a living. Nonetheless, only time will tell.

And how will the exiles in the far-flung suburbs survive such long commutes? Already, businesses are massively scaling down WFH policies. I’ve be a fan of remote working for 35 years, but I’m not sure it is made for everyone in the same way.

And what about the AI boom, noted by McKinsey in its report. In short, how can we tell the difference between disruption and non-disruption? How can we avoid, to use Joanne’s expression, this “hyperbole of disruption”?

Some readers may think that “all this doesn’t matter, it’s all theory, what matters is what happens in the field”. There is truth in that. It doesn’t matter that disruptive innovation is all the rage, the proof will be in the bacon and even in eating the bacon.

Let’s mention Web 3, for instance. Pundits are telling us that the blockchain is nothing but a lie and, ultimately, nothing more than a glorified spreadsheet.

It’s […] not surprising that whenever “blockchain” has been experimented with in a traditional setting, it has either been thrown in the bin or turned into a private permissioned database that is nothing more than an Excel spreadsheet or a misleadingly named database

Others, no less knowledgeable, tell us that this is a major change and a fundamental breakthrough.

Web3 is not vapourware, it’s a vision that encompasses different principles based on practices and technologies that enable new applications.

Gartner, as far as they are concerned, issued a midlle-of-the-road statement about Web3.

Admittedly, blockchain is only one of the components of Web3, but not the least. So who should we believe? How can a layman navigate this world of “disruption”, to paraphrase MacKinsey?

What if we were living in a workd which has become too complicated for us to understand?

I’ve wondered a few times lately whether the world has really become complicated or whether it’s engineers and marketers that are messing things about so much that noone can understand anything anymore? Unless it’s Google — and AI soon — which made us stupid?

It reminded me of a lecture by the late Bernard Stiegler, who sadly passed away in 2020. His in-depth thoughts on the proletarianisation of our hyper connected society [see his presentation in French] hit the nail on the head (his diagnosis was more impressive than the solution he suggested).

We have lost the ability to understand the world around us

And once again, it’s Vaclav Smil who sets the record straight. The Manitoba hermit joins the French philosopher in a slightly different way.

He begins by pointing out the extent to which ‘experts’, during the outbreak of Covid, by dint of hyperspecialisation, had been unable to help us deal with the pandemic. According to the Canadian thinker, there is an underlying explanation for the fact that it has taken so long for so clever experts to agree on something as simple as wearing a facemask.

[…] explanations of this comprehension deficit go beyond the fact that the sweep of our knowledge encourages specialization, whose obverse is an increasingly shallow understanding—even ignorance—of the basics.

Smil’s sentence rings true, but not only to describe our ignorance of the world in its main components. Proletarianisation also strikes experts in innovation, the Web, digital and the economy, and in the analysis of so-called ‘disruptive’ technologies.

I don’t know whether Roubini is right or whether, on the contrary, the aficionados of Web3 or AI (or of the next high-tech fad) will win the battle. They probably don’t even have a clue themselves. And innovation takes such a long time that it’s possible that we’ll have to wait a decade or two before we know the whole story.

And so it is with ‘disruptive innovation’, and I used inverted commas quite intentionally. If someone had asked me – back when I created internet-banking.com – whether the Internet would ‘disrupt’ the banking sector, I would probably have said yes. And there’s a good reason for this: this selfulfilling prophecy was giving me job. Truth be told, I sincerely thought so.

Nearly 30 years on, my feelings are rather mixed. Almost all the neo-banks have been taken over by big financial institutions, and even if new ones have been created, we’re all keeping our fingers crossed for feare they crash before we’ve had time to withdraw our money (it’s already happend twice with me, once with ING and once with banque directe. Now my business is with Shine but after a year, Socgen got rid of them and this is a bad omen) .

Despite the above, Chris Skinner was predicting bank closures in droves nearly 10 years ago. And reality has finally caught up with many of today’s banking institutions, even in more traditional and stable markets like France.

‘Disruption’ did happen, but not in the way we expected originally.

If there has been disruption in the banking sector, and this is only the beginning, it is because of the impact of automation, a universal movement that affects all professions and that is linked to a natural trend in our societies since the miraculous 1880s, to put it in Smil’s words. Wherever a machine can do the job, it will. After all, human beings cost money, and they always complain that working is hard.

The answer to the question – is this technology disruptive or not – is not trivial, and is above all a matter of judgement and time.

The only thing that is certain is that the proletarianisation (in the meaning that we are losing the ability to understand how things work) that we are experiencing is not going to help us decode the technological innovations of the years to come. And that includes Generative AI in the first place.

The post Is disruptive innovation overhyped? appeared first on Marketing and Innovation.

Growth hacking can often be perceived as toxic, but you can sit back and relax, it is possible to practise ethical growth hacking but it requires time and energy, growth hacking expert Frederic Canevet explained to Visionary Marketing. In a nutshell, it may be a little harder than you think, but it is well worth the effort. Fred, who ate his own dogfood to sell his bestseller on the subject, tells us everything we should know about whte hat growth hacking.

Frédéric Canevet: There are two tale-telling cases that illustrate the controversial practices of growth hacking perfectly:

FC: Yes, it’s feasible but it requires time and effort.

FC: Our approach has to evolve considerably in the face of today’s economic challenges. In a tense economic climate, we can no longer afford traditional marketing with its long-term plans. This is precisely what inspired growth hacking in Silicon Valley, where startups had to, as the time-honoured slogan went, “live or die“. In a world tending towards the end of consumerism, at least in Europe, the challenge is to do more with less.

There are three levels of growth hacking.

FC: Sending mass unsolicited messages in LinkedIn serves no purpose. Instead, effectiveness lies in forging true connections. The strategy I adopt lies in the daily publication of high added value content, demonstrating real expertise. It’s not an aggressive sales approach, but rather inbound marketing based on trust.

In fact, email spam is a no no. You send 10,000 emails and what you get is a 0.5% open rate and a slightly lower click rate. All in all, as you sent tens of thousands of messages, you may get some sort of result. But very soon, all this is bound to dwindle. Not to mention how damaging all this could be to your reputation.

As for ‘black hat’, I absolutely forbid it. In particular, fake accounts attached to the name of a company, solely for the purpose of recovering data from lists of people and companies that follow the page of the target company. This is illegal, because it’s identity theft. The same applies to those individuals who seek to recover the email addresses or telephone numbers of people with whom they have no relationship. I refuse to do that, especially as it often involves personal data.

FC. To illustrate the effectiveness of the approach I recommend, let’s quote some figures from the company I work for, Eloquant: over 20% of the 1,200 people who signed up for our interviews with customer relationship experts this year came from LinkedIn, out of an industry of around 15,000 professionals. Our aim is to unite this community, establish our legitimacy, and then convert the members of this community into visitors of our various events such as webinars or our “All for Customers” trade show in Paris.

The omnichannel approach is becoming essential as traditional channels become saturated. The numbers are not adding up: the rate of participation in webinars has fallen from 35% 3-4 years ago to around 25% today, 30% at the most. The rate of viewing replays has also fallen, from 15% to 12%. These figures show that it is no longer viable to rely on a single channel. Omnichannel is of the essence, and face-to-face meetings especially, that are more effective than ever.

FC. Personalisation is vital. For birthday wishes, we take a two-step approach: an initial message automated by my assistant, followed by personal interaction on my part.

And I send the messages one by one.

AI won’t replace humans, but professionals who master AI will outperform those who don’t. Our experience with Smart Tribune illustrates this principle: during a joint event, we decided to pull together and appeal to our respective networks and approach each potential participant individually. Success depended on pre-existing relationships and established personal links.

This then led to a white paper written jointly with Apizee and Smart Tribune [note: in French only], based on an OpinionWay survey of 1,000 interviewees. Our partners used AI to kickstart the writing of this project. While AI was impressive at the start, it was quite obvious after a while that all this was more artificial than intelligent: the formatting was bland, transitions were artificial. All that was typical ChatGPT gibberish. We had to substantially correct all these initial sections.

AI remains a valuable tool, particularly for copywriting. I use a custom GPT to generate drafts of posts about my events. The result, while not exceptional, provides us with a straw man, which can then be adapted and personalised.

FC. There has been a significant drop in SEO traffic on Google these past few months. Well-established blogs have been massively hit by the arrival of AI and the automatic generation of content. As Google is struggling to distinguish authentic content, it then started to favour more specialised and industry-specific sites.

I’ve noticed that many content creators and bloggers I know have given up or scaled down their online writing. Personally, while blogging used to be my number one priority, it has now slipped into second place. I now prefer to concentrate on LinkedIn.

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AI is redefining retail for good, bringing in the kind of automation and professionalism once implemented in the manufacturing industry. In this case, it’s mostly revolving around data-driven marketing decisions and in-store retail media capabilities. As shown by Axians, a VINCI group company, AI isn’t a mere toy for undergraduate students who are failing their tests and need better inspiration. It’s a robust, state of the art high-tech engine for growth and better in-store management. Yet, as often with technology, there are two sides of the same coin. The other one is more ominous, though, depicting a future of retail where layoffs will continue to rise, mostly for those retailers who missed that boat of AI-driven customisation. Here is the account of our discussion with Hugo Rocha Gonçalves, Axians’ head of Smart RetAIl, at Tech for Retail 2024. 

Hugo Gonçalves. We developed the Smart retAIl concept to address the main challenges that the retail industry is facing today. There is a strong need to better understand in-store consumer behaviour, profile and shopping habits. We provide this knowledge to improve store efficiency, and to enable data-driven decision-making.

H.G. We are using AI and computer vision to accomplish this. 

H.G. This is precisely what it is. And with this heat map, we can also understand what products people are looking at, how much time they spend. With AI we are taking this to a new level. This new level includes product tasting and testing. Two good examples are chocolate tasting, where we need to understand through computer vision when a customer is tasting something, which is very important in chocolate stores, and perfume stores. With this technology we can detect if the customer is testing the perfume and then understand if he or she will buy it or not afterwards.

H.G. Indeed. It used to be very tedious work to have someone watching hours and hours of video, trying to understand customer behaviour, customisation, and buying habits. Now we have AI that can process 24 hours of video, covering all the opening hours of a given store. We can process all this data and obtain valuable insights as well as data enriched by AI and computer vision.

H.G. This entire process demonstrates the beauty of machine learning and AI. No need to resort to supplementary intrusive devices in the stores. We are using existing in-store CCTV cameras. We subsequently apply AI image processing, frame by frame, on the existing footage. The data is recognised and categorised by the AI automatically. The resulting data provides a lot of KPIs like passerby/buyer qualification, hot and cold zones identification, as I said already. We’re also interfacing with other information systems such as CRM, ERP, or point of sale systems. Doing so we are able to match our data with the sales data.

H.G. Indeed, the technology is also helping us in that direction. All the innovation and sophistication lie in the AI processing the image. With the evolution we’ve experienced in computer vision, we no longer need specific hardware to do this. We simply need AI to help us with good machine learning and AI models to process it.

H.G. This system has demanded a great deal of knowledge and experience. We have a large group of data scientists at Axians. It’s also important to mention that this solution originated from an AI program launched by VINCI Group called the Leonard program (editor’s note: named after Leonardo da Vinci). This program focuses on solving real challenges we face as citizens in our daily interactions. It’s aimed at using AI to solve real challenges. One of these challenges involves using human expertise and knowledge in conjunction with AI. Her me we are talking of a different kind of AI (coupled with computer vision), not generative AI. 

H.G. Typically, we have a learning curve for these types of systems. We train the model using what we call manual labelling. Manual labelling helps the model understand what a person is. There are already modules that assist us. We don’t need to start from scratch. We have existing models, open models that identify a human in a shop and their interactions. On top of this, we use not only our retail clients for assistance (they help us with the training of the model), but also to understand and label the data correctly. It’s important to note that ours is not an unsupervised process. Here we are talking of supervised AI image processing. Supervised learning ensures the correct labelling of data and effective leverage of AI capabilities.

H.G. Beforehand a lot of preparatory work was required. We have extensive experience developing AI solutions, especially in computer vision, data processing, AI processing, and data quality. This represents at least two or three years of intensive work, collaborating, testing and trying to understand how to move forward. Whenever the packaged solution doesn’t suffice, we propose POCs to our clients. Such POCs help us reduce overhead related to testing. For example, we are currently testing queue times AI management. From experience, we’ve found that normally when customers are buying something, they won’t wait more than 10 minutes. If the wait exceeds 10 minutes, they’ll leave the queue and give up on their purchase. We’re addressing issues such as these by providing data driven insights.

H.G. We have launched a POC in Italy. We’re assisting a large retail client over there. This retailer had realised it was losing sales and that their conversion rate was decreasing because their staff wasn’t supporting their customers, even though that was part of their onboarding training. 

The end gain was significant. They’ve reduced queue time by 50% and increased sales in some stores by 12 to 15% due to this implementation.

It was sufficient to break even and they are now challenging us with new use cases, including some very complex AI problems.

H.G. It depends greatly on the size of the stores. It’s not a one-size-fits-all solution, but we can say that recovering the cost of the investment in the platform typically takes between 6 to 12 months. 

H.G. Regarding queuing times, we have another example in Portugal involving high-tech retail solutions. The main issue was the identification of the most profitable areas within the store. When selling technology hardware like smartphones, etc., hot zones are of the utmost importance. They are areas  where consumers spend extra time, allowing retailers to sell media space to vendors. This what is known as in-store retail media. In this particular case in Portugal, we achieved great results with a retailer who started to monetise the hot zones in its stores. This wouldn’t have been possible with our platform. Now they know which areas provide more return on investment and can charge more for product placement in these zones. We’re still in the early stages with this client, a major retailer in Portugal. Already, the return in euros is between four and five figures per store.

H.G. Absolutely. We’re living in a data-driven world. Decisions should all be made based on data. This platform provides extensive in-store data and enables many well-informed data-driven decisions. In the near future, retailers failing  to consider data-driven marketing and AI will have to layoff staff and make other last minute haphazard decisions. Our solution helps uncover KPIs and metrics that were previously hidden. Through data-driven approaches, we’re confident we can help reduce redundancies and facilitate better data-informed decisions.

H.G. It won’t be. There will be a major reconfiguration of stores. Luxury stores will continue to have staff assisting us with purchases. For everyday retail purchases, there will be a significant reduction in staff. 

In the future, retail will no longer be a labour-intensive industry

The future of retail will also be about extensive customisation. We’re already experiencing this level of customisation in streaming services that trace our personal and behavioural data very well. This means that each consumer will have its own bespoke catalogue tailored to his or her needs. To stay in business, retailers must possess in-depth knowledge of their customers. Moving forward, beyond this extensive level of customisation, a personalised care experience for each customer. 

The post AI in retail: shrinking queuing times today, headcount tomorrow appeared first on Marketing and Innovation.

Data-Driven AI is the future of customer experience, François Ajenstat told us at a recent interview. François is Chief Product Officer at Amplitude, the company behind a digital analytics platform aimed at helping B2B and B2C businesses build better products, websites and ecommerce experiences through behavioural data. François stressed the significance of data-driven AI within analytics but also delivered a clear warning: Don’t fall in love with what you have built! Focus on delivering second-to-none customer experiences instead. He emphasised that implementing chatbots without purpose isn’t beneficial, noting that too often, in this new world, businesses rush to add chatbots but it doesn’t make “anybody happier. No. People are still frustrated.”

François Ajenstat. While AI capabilities have existed for years through statistics and machine learning, generative AI has opened new possibilities. We’ve integrated this through “Ask Amplitude,” allowing natural language queries with visual responses. Thus, users can simply ask questions about their most engaged users and receive actionable insights.

Absolutely. And you’d get the answer in a flash and the system would suggest what actions you should take and how you should engage them. Alternatively, it could help you visualise the journey of those users.

F.A. Indeed, we focus on three different areas with a massive potential impact.

*Outliers (statistics): a data point on a graph or in a set of results that is very much bigger or smaller than the next nearest data point.

F.A. Every time new technologies emerge, it causes fear, uncertainty, and doubt about the jobs that are going to be eliminated.

Think of word processing. In the 50s and 60s, the only people who would type were office secretaries. That was a specialised job. When WordPerfect and Word came out to the market, that job got removed. But at the same time, it empowered millions and millions of other people to be able to perform new tasks by themselves. And that was extremely liberating.

It doesn’t eliminate the fact that some people are good writers and some people are not. You still need the core skills to know how to write properly.

And I think that in our jobs, whether you’re in marketing, engineering or product management, you still need to grasp the fundamentals to understand what is happening. But you can eliminate some of the more basic work and spend more time on the higher level.

F.A. Indeed, it does. Think of these new programming languages where you don’t have to learn all the basic hard-coded engineering. You are therefore facing a higher level of abstraction. The same goes with AI. It is merely providing a higher level of abstraction. It makes it possible for you to focus on building greater software versus knowing all the mechanics below it.

F.A. AI will become a core capability in all software, driving faster innovation and creativity. The focus on user experience becomes even more critical as expectations rise. Success depends on delivering value to users, regardless of the interface or platform.

User expectations are going to grow. And we will all have to compete more effectively or more aggressively on winning the rights to be able to serve those users.

I think that changes the equation a lot. We now have a higher responsibility to deliver better quality experiences.

But the core of all these experiences is data. We have to be able to collect more and more data to understand what’s working and what’s not working.

Just delivering a chat experience on a website doesn’t mean it’s a good experience.

Too often, in this new world, businesses rushed to add chatbots. Is anybody happier? No. People are still frustrated. But the real question now is “how do you continuously use that data to deliver better experiences?” To better understand your funnels and user journeys and drive customer retention.

That’s the $300-million question. If your website experience is clearly positioned but you are delivering your chat capability through a third-party interface, how do you actually differentiate?

All that matters is how much value you are delivering to your users.

Don’t fall in love with what you have built. Fall in love with your customers and this should guide you every single day.

F.A. Users want software that’s adaptable, continuously monitoring itself to drive the right outcomes. I think one of the keys to achieving that is the ability to express the metrics, the goals that you have. Because the software will never know what ‘improvement’ means.

Thus, if you were to say, ‘My goal is to increase signup conversions,’ then the software could look at the data and improve itself, change terms, add new buttons and new capabilities that will drive that outcome for you.

I think the world actually is shifting from websites to metrics and outcomes.

And that’s how AI can come through. There’s a lot of gibberish that comes out of AI because it doesn’t know your intent. It doesn’t know your domain. But if you’re able to start with the intent, then everything else makes a lot more sense.

We have a project in development right now where we analyse all the sessions on a given website. We’ll create screen recordings of everything. And from that, we will be able to infer which industry you’re in, where users are frustrated, how users are navigating your site. From that you can start ask AI to suggest changes for you.

F.A. We’re at an exciting inflection point, like the PC revolution or mobile transformation. AI is going to be a whole new world, but we’re in the very early days. And now’s the time to start dreaming, trying, experimenting.

My advice to everybody is to lean in. Don’t be afraid of it. Be the first ones to try and fail and test and really dream of what’s possible because there are incredible opportunities ahead of us.

Those who don’t adapt risk being disrupted by those who do.

The post Data-Driven AI Is the Future of Customer Experience appeared first on Marketing and Innovation.

Ever heard of cookie pop-ups? It’s true that it’s hard to escape them. Following the 2011 Cookie Directive, sites have finally complied. But rather than deleting cookies, they have installed cookie pop-ups, which throw annoying messages at you and prevent you from surfing the web. They’re useless, mostly because they don’t really improve data confidentiality. Their main purpose is for sites that track your data for advertising purposes to cover their tracks and pretend they’ve become virtuous. Here’s how to get rid of them.

You’re all familiar with those messages that warn you when you’re visiting a website stating that your data will be used, and that your consent is required to do so.

50 times a day you click, but not to get rid of cookies, but rather the cookie pop-ups.

Thanks to Statista, we can see that more than half (73%) of internet users in the United States are somewhat or completely unknowledgeable about cookies. Using this knowledge, websites take advantage by simply having a cookie pop-up, forcing users to click through to access the content.

So, what is there to do to reduce these repetitive, irritating messages? A content blocker sometimes won’t be enough to protect you from these cookie pop-ups. This list is not exhaustive and may evolve. Don’t hesitate to suggest additional solutions, which we’ll add to our benchmark.

Let’s start with this extension for Firefox. Developed by Alessio Capponi, the No Cookie Wall extension claims just 26 users.

Brave, one of our favorite browsers, has also taken to blocking cookie pop-ups. Here’s what Brave has to say about it in the release of the latest version of the Web3 browser: “You know those annoying cookie consent notifications that pop up every time you visit a new website?”.

Newer versions of Brave can hide them and, if possible, block them completely. Simply update to the latest version of Brave.” If you miss the prompt to block cookie consents the first time, you can visit brave://settings/shields/filters and easily enable/disable the EasyList-Cookie List option.

Safari (exclusive to Apple products) has an even more radical option: block all cookies. It’s so violent that when we activated it this morning, we lost much of the content of this post, which we had to recreate. Handle with care. Because it also deletes session cookies, which are essential if you want to remain connected to a site. This is my case here on WordPress. I’m there most of the day and I don’t want to log in again 20 times a day.

On Edge (Microsoft): Edge is Microsoft’s new browser, also available on Apple hardware. It’s very fast, extremely well designed, and includes Web3 subtleties such as a Wallet.

What’s more, it lets you try out GPT4 coupled with Bing (aka Bing AI. Nice, but…). So here’s the CookieBlock extension, straight from Zurich. CookieBlock is a browser extension that lets you automatically delete cookies that don’t respect your privacy.

Using advanced machine-learning technology, it classifies cookies into four distinct categories. These are “necessary”, “functional”, “analytical” and “advertising” cookies, which the user can then authorise or reject individually.

Unlike cookie pop-ups, which interrupt your browsing experience, CookieBlock only asks you to define your policy once and guarantees that the types of cookies you reject actually get deleted from your browser.

What’s more, CookieBlock works on all the websites you visit, and doesn’t depend on the site publisher’s goodwill. Thank God for that!

DuckDuckGo (the browser): the famous privacy-friendly American search engine is the one we’ve been using on a daily basis for many years. DuckDuckGo’s browser is a good starting point for getting rid of all these little beasts.

Chrome: Here I’ll pass, if you use Chrome, no need to pretend to hide cookies, you’re the product.

This overview of anti-cookie and anti-cookie pop-up devices is not intended to be exhaustive. There are many others. It’s a good starting point, however, for avoiding those unbearable and unnecessary messages.

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Is GenAI content marketing-friendly? Adobe organised a round-table discussion during their Experience Makers conference in Paris at the end of last year. The debate brought together a few digital experts. During this debate, I mentioned that there were limitations associated with GenAI image production and that they weren’t technical. Others contended that it was just a matter of prompt engineering. In my opinion, proper prompting may be recommended, but the limitations of GenAI image generation tools extend far beyond that. Such is my point, which I substantiate in this piece with insights derived from a two-year practice of such online tools while editing this very website.

This debate was organised by Adobe at the Louis Vuitton Foundation in Paris. The main topic was GenAI and its impact on content marketing. This discussion turned out to be an opportunity for me to take stock of a year’s experience of using generative AI to produce images for Visionary Marketing.

At first, as we discovered Midjourney and its clones, at the end of 2022 we all were very excited. And boy! Did we have fun producing images for all intents and purposes. Then came a moment when one needed to hold our horses. It was indeed high time to take a step back from it all to ponder over the use of GenAI with regard to content marketing. As I explained during the debate, it reminded me of these HDR filters I discovered when I started using Adobe Lightroom 12 years ago. At first, I resorted to them on almost a daily basis. Five years on, in hindsight, I removed all these HDR pictures.

Thus, here are a few thoughts on the use of these tools which, in my view, are more than ever, worth investigating. Yet, one should look at them in the context of the widespread use of GenAI tools by both Web users and the Media.

In fact, at Visionary Marketing, we went through several stages. In the beginning, we only used images from my personal stock. All the Visionary Marketing content writers had to go through this limited stock of images. These photos are personal, and therefore unique.

Yet a feeling of déjà vu soon also set in. And above all, we were often unable to describe certain concepts using those images. It makes sense since this stock doesn’t include all the possible metaphors one would require.

Stage 2 of our own discovery process consisted in adding stock photos to our content. This made it possible for us to bypass the above-mentioned limitation syndrome. However, it also made our illustrations look more commonplace. This could have been damaging in some cases. Fortunately, we were using Jumpstory, an image data bank that stood out from the rest.

Thus, we avoided this pitfall to some extent. It was good while it lasted, for Jumpstory went under this year. I wouldn’t be surprised if GenAI killed it there and then. 

From the end of 2022 onwards, this is stage 3, we started making more intensive use of generative AI to produce illustrations for our articles.

In all cases, whether it be the first, second or third stage, we’ve come to the same conclusion: using the same image source all the time leads to a feeling of repetition, fatigue and trivialisation.

At the end of the day, to successfully illustrate your content, you are recommended to opt for a mix of the different techniques.

Above all, as I explained during the Adobe debate, you have to be able to master the prompt so as to produce illustrations that are clearly differentiated from what other content publishers usually publish on the Net. As explained with the above example, this has been made easier as of mid 2024 when Midjourney started to implement the personalised mode.

The more abstract the prompt, the more eye-catching and the more different the image produced. That’s what makes you stand out from the crowd. This is rather counter-intuitive. Indeed, most self-proclaimed AI pundits on LinkedIn and elsewhere will be adamant that such prompts should be banned. What life has taught me, though, is that when the crowd produces A, producing non-A will make your work — and yourself — more distinctive.

Besides, advanced mastery of all available image tools (GenAI, Lightroom Classic, Photoshop, Illustrator, or all of them combined), means that you can retain total control over your pictures. Thus, you should be able to produce less commonplace pictures or illustrations for your content.

More than ever, marketing is not about getting things done. It’s about getting things done differently. Whether you resort to GenAI or not, you should always bear that in mind.

Last but not least, don’t hesitate to revisit your content to change illustrations that, with hindsight, seem too trivial, too stereotyped or too garish. Unless, of course, you like it that way.

The post GENAI and Content Marketing: Learning from experience appeared first on Marketing and Innovation.

The state of influencer marketing in Europe 2024 is a survey conducted by Kolsquare, a leading European influencer marketing agency. It provides a particularly interesting perspective on influencer marketing budgets, how influencer marketing is handled and its future trends. Besides, its comparison of Europe’s main markets for IM is clearly enlightening. It’s one of the first if not the first of its kind and it sheds light on the way that businesses are conducting marketing with Key Opinion Leaders, at least for business to consumers. One of the most striking takeaways from this study is the sheer size of the average European influencer marketing budget which is evaluated at a whacking €3.375 million annually. 

This 2024 European Influencer Marketing (IM) survey was conducted by Kolsquare and NewtonX. It involved 385 decision makers representing medium to large organisations across various sectors (Beauty and fashion, IT, SaaS and Telecommunications, Retail food and beverages, entertainment …). All respondents had more than two years of experience in influencer marketing. The sample is relatively large for that kind of B2B survey with five countries surveyed (France, Germany, Spain, Italy, and the United Kingdom) and approximately 80 respondents in each of these countries.

Thanks to this survey, we now have evidence that the influencer market is really significant with €3.375 million spent on influencer marketing by European businesses annually, and Germany topping the list at €5.74 million per annum. Micro influencers (10,000 to 100,000 followers) being the most popular partners for the surveyed European businesses. Respondents’ expectations on growth are very optimistic with 54% of them expecting to increase their influencer marketing budget next year. Unsurprisingly, the influencer marketing landscape has shifted towards three main platforms: Instagram, TikTok, and YouTube.

More than ever, influencer marketing is here to stay with 27% of respondents saying that it will become more important in the marketing mix. And even 6% stating that it will become the most important part of the overall marketing spend.

UK marketers seem less prone to spend huge chunks of their budgets on influencer marketing with a yearly average of £848,000 (still a whopping €1.02 million!) Brands are also declaring that they will become more selective in the influencers with whom they work (56%). Ethics is topping the list of preoccupations in Italy and France, but less in the UK and not that much at all in Germany.

Indeed, Germany is described by Kolsquare as “the big spender”, but not very keen on ethical considerations. Unlike the French and Italians, who said to be prioritising corporate ethics when selecting influencers.

This emphasises a significant shift in the market, whereas four or five years ago we were stressing the fact that ethics weren’t really on French influencer marketing managers’ priority list.

When it comes to social network usage by influencer marketers, the shift towards Instagram, TikTok and YouTube is significant. However, Facebook has not disappeared from the IM landscape completely, as it is still the platform of choice in the UK.

X has slipped down the ladder and further down one can find niche platforms such as Twitch, Pinterest, Snapchat and a flurry of Chinese platforms that are clearly less attractive to European marketers. What is surprising, though is that LinkedIn is definitely not part of this list, meaning that the survey is mostly geared towards Business to Consumer marketing. For all intents and purposes, one should emphasise once more that Business to Business amounts to approximately 80% of the production of wealth worldwide.

When it comes to size, One can spot a good balance of macro, mega, micro and nano-influencers in marketers’ choices of partnerships. Whereas patterns are relatively similar across countries, micro influencers definitely top the list in almost all of them.

One-Shot Versus Long-term Influencer Marketing Collaborations

It seems that in Germany and France businesses prefer to work in the long-term with Key Opinion Leaders. However, the structure is rather similar in all countries with a three-tier pattern: approximately 30% of collaborations with long-term partners, 30% for a mix of new and existing influencers, and another 30% of new kids on the Instagram block. This pattern varies slightly according to countries.

Content Forms: How the Brands Collaborate With Opinion Leaders

The variety of content types that is offered by influencer marketing is noteworthy, with sponsored posts and influencer events as well as product reviews topping the list. However, there are differences according to countries with sponsored posts not being very popular in France (only 23% of respondents vs. 58% on average across all countries).

Approximately one third of businesses are keen on performing co-creation with influencers and even nearly 25% of respondents are conducting product creation with them.

This study is instrumental in showing how pivotal influencer marketing has become in B2C marketing. Barring a few variations, one can say that IM patterns are relatively similar across the five main European countries surveyed.

Spending levels are stellar, with German businesses being on a buying spree. One can only hope that IM will help them fight the current economic slump in Europe’s biggest economy. France and Italy are keen believers in IM too. The United Kingdom and Spain are lagging a bit behind, or can be considered more reasonable, it depends on the point of view.

The most important indicator (KPIs) for influencer marketers is not the number of followers but the quality of the engagement. And as it suits B2C, it’s even shifting towards sales and conversions.

Last but not least, there are a number of challenges to influencer marketing such as striking the right balance between influencer freedom and brand control. A major issue we have consistently highlighted for the past 20 years we have spent in that domain.

It’s a significant pain point in most European countries and especially in France, where brand control is tightening on influencers.

Measuring ROI and ROAS (Return on Ad Spend) is on top of Italy’s list of issues related to influencer marketing.

Apart from that, authenticity and the quality and tone of voice of influencer content is definitely what entices European brands to work with Key Opinion Leaders.

What is also most striking is the significance of ethics according to countries. The results seemed very counter-intuitive to me but reassuring, with Southern countries showing a lot of concern for ethics compared to Northern ones.

Kolsquare is Europe’s leading Influencer Marketing platform, a data-driven solution that allows brands to scale their KOL Marketing strategies and implement authentic partnerships with KOLs (Key Opinion Leaders). Kolsquare’s technology enables marketing professionals to easily identify the best Content Creators’ profiles by filtering their content and audience, and to build and manage their campaigns from A to Z, including measuring results and benchmarking performance against competitors. Kolsquare was founded by Quentin Bordage in 2018. 

The post Influencer Marketing: Average European Spend at €3.5m Annually appeared first on Marketing and Innovation.

The Cyber threat landscape in Europe is quite worrying. A recent survey by Cloudflare was conducted amongst 4,261 IT executives responsible for cybersecurity in Europe. 24% of the sample is made from small enterprises (150–999 employees), 24% from medium-sized businesses (1,000–2,500 employees) and 52% from large organisations (above 2,500 employees). All major European countries were surveyed by Cloudflare in their study entitled Shielding the future: Europe’s cyber threat landscape. The report paints a rather bleak picture but stresses that solutions exist… as long as leadership teams understand what new Cyber threat countermeasures like Zero Trust are about. All in all, this will require all management teams, not just IT, to better understand the ins and outs of such dangers.

The sample of this survey is quite comprehensive given its high profile and B2B orientation.

– All kinds of businesses are impacted by cyber threats with 72% of respondents reporting at least one incident in the last 24 months,

It seems that an increasingly dangerous cybersecurity landscape is causing more and more aggravation within organisations. The growing complexity of open networks with access to increasing amounts of money is too big a temptation for most cybercrooks to resist. Besides, the staggering complexity of IT, networking and especially cybersecurity solutions such as zero trust explain why there are so few companies that are ready to implement such solutions. However much sense they may make.

However much I hate the idea, it seems that too much openness of such systems isn’t making our lives easier.

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Content creation with artificial intelligence is already old hat as it’s been going on for a few years and, unfortunately, slop is now populating the Internet at an increasing pace. Yet, when I received this message from a good friend of mine last week regarding Google’s new app entitled NotebookLM, I was shellshocked. I tried it and tested it and felt immediately overwhelmed. Having slept over it for a few days, I’m just recovering, so here are my impressions. 

The other day, a friend of mine sent me a message about Google’s new NotebookLM AI application. As I always do that kind of thing, I tried and tested it immediately. The catchphrase for NotebookLM is “Think Smarter, Not Harder” and Google presents it as “The ultimate tool for understanding the information that matters most to you, built with Gemini 1.5”.

I wonder about that. Is it really a tool made to “understand information”? This sounds a bit dubious. 

The aim of NotebookLM is to turn a piece of text, a video, a web link into a conversational podcast. It is semi customisable and not quite finished. But it gives you an idea of what the future has in store for us, content creators. 

On the one hand, the technology is great and works fine, barring a few glitches, on the other hand, it’s a window on a very weird and dark future  (once again, it’s not the tool that is the problem but the people using the tool, as Bradbury remarked). 

For my initial test, I selected one of my English pieces about artificial intelligence (AGI). I copied and pasted my text into the window and hey presto! a few seconds later, a proper conversational podcast between an American man and woman was available. Here it is 

I must admit I couldn’t believe my ears when I heard this podcast generated from a mere piece of text. It was both brilliant and daunting. I immediately thought that anybody could produce an audio conversation out of anybody’s blog piece and I suppose that some of the laziest of content creators will do just that.

When I looked into the podcast in greater detail, I spotted that there were a few glitches here and there and especially the quote by Ray Bradbury which is definitely not taken from Fahrenheit 451. It’s clearly mentioned in my text. 

Man | 01:24.308

Woman | 01:28.691

Well… nope, sorry (and by the way, I hate your “yeahing” at me). This was taken from Bradbury’s 1974 letter to Brian Sibley. It’s clearly stated and the link to the source file is explicit. 

This very morning, I went back to the application and tried it once more. I inserted a YouTube video and it failed a couple of times. So I gave up and copied a web URL and it worked wonders. This time I used my fraud and AI piece with Fujitsu. 

https://visionarymarketing.com/wp-content/uploads/2024/10/fujitsu-fraud-in-retail.mp3

I tried to customise the podcast but I couldn’t change the American voices nor the tone of voice which isn’t consistent with mine. I tried to turn it into a more professional, less casual, tone of voice. This didn’t work as planned. But my instructions aimed at making the podcast more factual and to focus on the numbers were executed correctly by the AI. 

That said, when you listen to the entire podcast, especially towards the end you will realise that the AI is adding quite a lot of content to it and making its own commentary. 

This experiment raises quite a few questions.

As I mentioned already, I find this kind of application a little worrying. On the one hand, it is great to be able to produce a conversational podcast which is very engaging, very quickly. In hindsight, this is probably conducive to producing even more slop on the Internet, which will probably end up collapsing in on itself. It’s a matter of years if no one stops this nonsense.

I’m not sure that Google will maintain this application nor that it will even let you use it for very long. They have a track record of crucifying innovations.

It could be quite tempting to use NoteboookLM to produce conversational podcasts without any efforts. But I will never use because we have this 100% human content commitment on visionary marketing. I may end up being the last of the Mohicans in that concern but I intend to keep the upper hand in this content creation process. I’m the one doing the thinking here, not the tool I’m using to write it.

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