Mark Ritson: Machine learning may be the future but it’s still pretty dumb

Scott Galloway, the entirely fantastic NYU marketing professor who presents as if he has just imbibed a combination of uppers and downers all in one gulp, gives a great talk where he compares the winners and losers of the last 12 months. He then looks for patterns and themes in the two lists as a vehicle to pull out insights and develop predictions for the year ahead.

One of his many observations is the growing power of ‘algorithm businesses’. By his definition Galloway sees these companies as being able to “implicitly gather data from their consumers and then automatically update the consumer experience”. He sees the most powerful algorithm-driven businesses as those that have the most receptors in the market and move fastest to update and improve their offer based on what these receptors relay back to HQ.

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In traditional 20th-century case studies, big businesses usually grew too large to listen to consumers or pivot to meet their changing needs. Algorithm businesses don’t face this growth threat; rather, as their scale increases, so too does the number of receptors in the market and thus their ability to evolve and improve even further.

Every time you use Google, for example, it apparently gets 0.000000003% better. No surprise then that Google joins Facebook, Salesforce, Amazon, TripAdvisor, Priceline, Spotify and Uber among Galloway’s list of the top algorithm-driven businesses. Or that, of the 13 companies to beat the average performance of the S&P 500 consistently, each and every year for the last five years, seven of them were algorithm-driven businesses.

Although Galloway’s postulations are relatively new, his fundamental point is as old as marketing itself. Boil down the elemental advantage of marketing and it’s not communications or positioning or even pricing. It’s way simpler than that. The biggest gifts that good marketing bestows upon an organisation are the abilities to first see the world from the consumer’s point of view and then make appropriate changes to accommodate this perspective in future products or services.

AI versus insight

It does not sound much. You listen to the customer. Then you change what you do in response to the lessons learned from listening. There, I just summarised about 85% of the marketing concept. Simple.

But just because something is simple, does not make it easy. Try actually getting a big company to put down their products and technology and financial projections for even a minute to recognise that all the money comes from the customer. In my career – if I can sound like a wanker for just a second – nothing has stunned me more than my ability to disarm senior executives with the message that we should listen more to customers.

Algorithms should cut out the weak link in the whole understanding-and-responding-to-the-consumer bit.

After many years working for a company it’s too easy for a manager to go native and start thinking customers see the business the way they do. Even marketers, who are meant to always use the wet rag of research to douse the flames of corporate bullshit, have been known to lose the plot and start believing that their consumers want more than a toothpaste/beer/coffee and that they want peace/liberation/community instead from their brands.

And even when you stay market-oriented, the challenge of turning a big corporate ship around to deliver on what customers actually want is equally confounding. Many a marketing director drives home each evening knowing what the target customer wants, certain that his company can deliver on this but also convinced that the bureaucracy and politics and finance that run his company mean that there is no chance that this delivery will ever actually occur.

It usually takes a slightly deranged CEO like Elon Musk or the late, great Sir Simon Marks from M&S to be able to take an insight and, with a dismissive wave of a hand, make the insight a directive that becomes a reality. Usually consumer insight remains with the front line staff and organisational ability to drive change stays with the C-suite – and never the twain shall meet.

Seen in this light, it’s clear why Galloway’s algorithms make so much sense and, apparently, so much money for the companies behind them. Algorithms cut out the weak link in the whole understanding-and-responding-to-the-consumer bit – they remove us, the humans, from the process. Amazon does not have to know why the new Tom Cruise movie sells better with a picture of his upper torso, versus the one of just his face. It just knows that this is the case from the thousands of sales it has already made and not made this week and instantly changes how the product appears to the 98,000 shoppers who will view it in the next hour.

READ MORE: Rise of the machines: Are robots after your job?

There’s no moron from finance questioning whether the research is ‘accurate’. No merchandiser with a long-running hatred for marketing who won’t change the Cruise packaging because his movies ‘usually do better with just the face’. They are washed away in a sea of terabytes and predictive modelling that takes barely a flash of time to understand, implement and move on.

Limits of machine learning

But is there a bit of hype here too? I get the power of algorithm learning. I see it as the modern epitome of market-orientation. I get the correlation with oversized business performance too. I just don’t see any of this first-hand. I am a consumer of many of the companies that Galloway lionises as ultimate algorithm businesses and yet they just don’t seem that smart to me.

Spotify is meant to be pretty clever. It knows what songs I have saved, what I play most often, what my mates play most often and what other people who have the same vaguely 1980s, rubbish taste in music as me play most often. This should make for a pretty convincing ability to recommend some excellent musical additions to my playlists that I was unaware of.

Even after I click on Amazon’s ‘recommended for you’ tab I just get a list of stuff that I have already bought.

And Spotify has a go. It creates ‘daily mixes’, which combine a set of my own chosen tracks with some ‘new discoveries’ based on what the algorithm says I will enjoy. So along with my own tracks (Van Morrison, Billy Joel, Robert Palmer and a bit of Jackson Browne) today I got late Paul Simon (shit), Boz Scaggs (shit) and Toto – you know the one about Africa and Kilimanjaro and the Serengenti-i-i (very shit).

It did serve me Warren Zevon and ‘Werewolves of London’, which I had not heard in a decade, and this made me happy. But it’s hardly the stuff of dystopian nightmares or the business engine of the future that Galloway suggests we might now expect.

A visitor to my house, armed with only with the recent experience of three very good bottles of red wine while I blasted him with cigarette smoke and offensively loud tunes from my outdoor speaker, could have – would have – done much better in the suggestions department.

The improvement is not apparent

Similarly, signing into Amazon should, after about 60 grand worth of purchases over the years, make it know everything about me and then some. But even after I click on the ‘recommended for you’ tab I just get a list of stuff that I have already bought. Basically, baby clothes, a carafe for my coffee maker, and then more baby clothes. I don’t need any of this because I just bought a barn load of clothes for the young Ritson mutant last week and the coffee maker is already gone because it was crap.

Now I appreciate all this is very hard to discern if you’re Amazon just selling me stuff online, but I expected something better or a bit more left-field from the Dark King of Algorithms than a reproduction of what I’ve already ordered. Nestled in my Amazon top 10 list of baby leggings and coffee mugs I expect one completely unexpected, immediately desirable thing that I am strangely drawn to. An exotic musical instrument that only I can play or perhaps a piece of clothing I would never have considered but now realise will make me look fantastic. But there is nothing. Just stuff I already own in different colours.

I should pause here and note that I may not actually be aware of the algorithms around me. I say that because I famously got pissed once and accosted the CEO of a very large airline that I was a patron of. He was very patient with me as I explained to him how bad his airline once was and how, when I had first flown them many years before he took over, the cabin crew never had my first-choice meal in business class.

But, I blustered on, they had improved in recent years. So much so that I could not recall any incident where my preferred food was not available. I tried to ascribe this improvement to his leadership but he stopped me short, explained that I had simply risen up the ranks of his airline’s loyalty program and was now visited first by the air crew to ensure I got what I wanted, and the bloke behind me got the dodgy chicken.

My point is that consumers – even those of us who masquerade as marketers in the daylight hours – rarely spot the wheels and cogs that surround us when we buy from a well-run company. Perhaps the algorithms really are as good as Galloway says. Maybe the likes of Spotify and Amazon are getting ever better with each click of my mouse. I’m just waiting for that improvement to become apparent and for that Toto song to get out of my head.