54 Fascinating and Incredible YouTube Statistics
By Kit SmithJan 17
Research analysts reveal how the most innovative brands are using consumer intelligence to transform decision-making.
Published August 25th 2017
The world of marketing has changed drastically from where it was ten years ago, and emerging technologies have had an enormous impact on its development.
With buzzwords such as machine learning, AI, big data and VR dominating conversations on the future of marketing and customer experience, we’ve reached out to Azeem Azhar, product entrepreneur, tech expert and curator of one of the tech’s most loved weekly newsletters, Exponential View, to hear about his thoughts on this topic.
So, hop on for a conversation on all things marketing, digital trends, tech lessons and much more.
Early exposure to consumer technology, and amusement with a Binatone video game console in late 70s, remained with me throughout my career journey.
I got my first computer, a ZX81, in 1981 and programmed throughout my teenage years.
I graduated in 1994 with a degree in Philosophy, Politics and Economics from Oxford University which wasn’t (then) very quantitative.
My first job after graduation was at The Guardian, where I covered gadgets and tech and launched one of the first newspaper websites.
While working with The Guardian and The Economist during the dot-com bubble I was exposed to the idea of founders coming together and building products. It had an aura of excitement that drew me in.
Whatever project I moved onto next, one constant theme followed me: I wanted to know how to offer the best possible information out of the gargantuan sea of information.
That’s what inspired me to start PeerIndex in 2010, a startup that applied machine learning to large-scale social media graphs to make predictions about web users. Brandwatch acquired Peerindex at the end of 2014.
It’s almost impossible to keep up to date with the pace of change.
But that said, I sometimes feel I am reasonably informed. That comes from a range of filters on Twitter and Medium.
My Twitter network is broad – I follow about 3,000 people. I try to have a diverse group of people interested in topics that I want to get abreast of. These include machine learning, AI, startups, blockchain, economics, Brexit, U.S. politics, renewable energy, business models, electric vehicles, venture capital, gene editing, neuroscience, etc.
I’ll typically rely on aggregation and signals filters like Nuzzel and RightRelevance to help filter the best content.
There is too much to read directly, so I rely on a handful of trusted friends to help me find things of interest. If you deliberately position yourself in different cliques in the network it gives you a fair handle of the best stuff.
But that said, you also need to give yourself space to think about what is happening. Otherwise it becomes a litany of noise. So I typically have a couple of hours a week (when I write Exponential View newsletter) when I reflect on what I am seeing and how that supports or contradicts my analytical framework.
Human with AI tools.
Andrew Ng once tweeted “Anything that a normal person can do in < 1 second, we can now automate with AI”. Redundancies and manual tasks are being automated leaving much more deep work for marketers to do. With nuanced data at hand, they are increasingly able to earn “social depth”.
This, coupled with predictive capabilities, allows marketers to know their targets better than ever before. It will be up to humans to decide how to act on the insights, and how far to go to reach the customer without being intrusive.
That latter point is super important: marketing is currently about persuading humans. And humans’ attention is precious and needs to be treated as such. So I’m not a fan of using AI systems to automatically post content onto social networks. That pushes the burden of filtering onto the human. That is wrong. Use AI to help you find the right messaging, but don’t use it to overwhelm my capacity for attention and judgement.
Phew! Where to begin? I know where. Look, the Cluetrain Manifesto got it right 18 years ago.
Markets are conversations. Companies are participants. In fact, since Cluetrain, I think we’re getting comfortable with something even tougher for traditional firms to understand. Your product experience is your marketing.
If you look at new economy firms, be they Amazon, Uber, Airbnb, Google or Facebook, the marketing is really done by the product. They eschew the existing channel and go direct to consumer.
It’s even happening in product categories we previously thought were immune to digitization like consumer packaged goods.
Dollar Shave Club is a great example: subscription-razor blades based on very close buyer understanding. All this means that insight and getting very close to your customer matters more than ever before.
I’ll add something else: a huge amount of advertising (and with it marcomms) has been connected to the oil economy. Nearly 13% of all advertising spend in the US is on the automotive sector. They are at risk as we decarbonize personal transport and move to post-ownership transportation-as-a-service using electric vehicles.
This transformation will butcher demand for traditional car advertising. Neocar manufacturers will be direct-to-consumer (like Tesla). The rest of the demand may well be mobility-as-a-service (like Lyft or Uber).
In reality it can only move in one way: which is to get more diverse.
There are any number of reasons for this. The academic research is pretty solid that diverse teams perform better. There is even a growing body of evidence that startups with female CEOs are more capital efficient than those with male CEOs.
Economics tends to trump politics. So that evidence will only get more widespread, more people will have good experiences of working in diverse teams which will in turn promote diversity.
I’m pretty certain that distributed ledgers (i.e. blockchains) will have a significant impact on the tech industry.
However, I also recognize that these fundamental changes to infrastructure take a long time. The Internet didn’t overtake the landline phone system by number of users in the U.S. until around 2011.
And distributed ledgers are a lower level technology so I would expect them to take in the order of a decade to start having a significant impact.
Data has become one of the crucial assets to almost all businesses, online or offline.
Collecting big quantities of data is just the beginning, the trick is in knowing what pieces of data are important for particular campaign or business.
There’s not only a gargantuan amount of data out there, but also a diversity in types of data. Is it behavioral? Does it give you facts about your customers’ geolocation or their opinions and needs? What really matters to your business?
Big data and advanced analytics is taking supply chain to the next level as it allows for more detailed prediction and recommendations that help cut costs and redundant nodes within the supply chain network.
Following the right kind of data can make companies more efficient and hopefully increase capacity for firms to innovate and serve their customers better.
First, if you’re a business leader, you have to keep your eyes and mind open for what’s coming next.
Businesses that don’t accept the fact that AI will affect their industry won’t exist in 5-10 years.
The equation is simple: AI improves the product, increases profits while collecting the data that helps improve AI. It’s a magical loop. Businesses that don’t take advantage will be left behind.
Second, get close to your customers, try to get inside their heads, as your research is probably wrong.
In the early 1980s, AT&T consulted McKinsey to figure out if cell phones would take over in early 21st century. The conclusion was that the total market would be about 900,000 devices—they thought people would find them too heavy, unreliable and expensive.
As we now know, they were wrong, because they were not close to the consumer.
Third, learning how to do something new is not failing, and this is particularly relevant for startup founders.
Some of today’s most successful startups are hard to recognize in their pre-pivot form.
Before Instagram, there was Burbn—a clunky app with too many features. Before Slack, there was a multi-player online community Glitch. Do your homework, and take a leap into a different direction if necessary.
Thank you to Azeem Azhar for sharing his thoughts with us.