Imagine a world ruled by robots, where artificial intelligence (AI) is used to wield commercial influence and drive our daily decisions. Imagine if your every move was predictable, your behaviours easily anticipated, your world reduced to a set of probabilities. This might sound like the stuff of science fiction, but this isn’t a dreamed up dystopian reality — in fact, it’s the world we’re living in today.
But machine learning isn’t the stuff of nightmares conjured up in futuristic Hollywood thrillers — in fact, it’s something with which most of us are very familiar, thanks to its prolific use in online shopping portals and video streaming services like Netflix.
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The “machine” in question here isn’t quite the kind we might imagine though, but rather a clever algorithm used to monitor consumer’s behavioural patterns, mining important data so as to anticipate purchasing decisions and offer relevant recommendations likely to have shoppers reaching deeper into their wallets.
But while for years machine learning has been the territory of a limited few, it’s now fast becoming an imperative for businesses looking to understand and maximise the impact of their carefully monitored marketing spend.
‘Businesses currently employing AI systems are able to improve conversion rates by 60% to 80%’
Where once we might have been amazed by Amazon.com’s ability to offer us a list of similar options to the one we just lined up in our shopping cart, today’s consumers have come to expect it, and are becoming increasingly reliant on artificial intelligence to do the thinking for them.
As AI starts to filter into our day-to-day lives, influencing everything from the music we listen to, to the coffees we order, it’s becoming difficult for more traditional businesses to compete with their artificially assisted counterparts, who are better able to capitalise on shrinking attention spans and widespread time poverty.
Businesses currently employing AI systems have proven able to improve their conversion rates by between 60% and 80%, simply by tapping into human behavioural traits that predict relevant associations.
By serving consumers information likely to resonate more strongly, these companies are able to offer their clientele greater perceived value, while at the same time significantly improving their own.
Just take this common house hunting scenario as an example. A would-be homeowner is searching for a new property with three bedrooms and a sea view in a designated area.
While property portal A requires them to trawl through endless lists of vaguely relevant options, AI-driven portal B quickly gathers relevant information, tracking their behaviour and clicks to learn more about what they want, filtering results in real time to produce tailored outcomes. Now which do you think makes the sale?
Of course, AI has numerous applications beyond the realm of recommendations. It’s being used to power chatbots, making consumer interactions increasingly cost and time-efficient.
It’s at play on our smartphones, which adapt to our speech patterns so as to anticipate our conversational nuances. Not to mention the likes of Amazon’s Alexa or Apple’s virtual assistant, Siri, which render even the act of typing unnecessary.
Sadly, the costs of such technology are still prohibitive for businesses short on budget, requiring teams of actuaries and IT whizzes to bring them to fruition. But don’t expect this status quo to remain in place for very long.
As with the advent of any technology, AI is likely to become less of a niche offering in the years to come, with improved algorithms meaning the introduction of more broadly applicable machine learning systems, which will allow smaller businesses to tap into existing intelligence.
Much like Shopify, these advanced networks will enable any enterprise to introduce smarter tools into their offering, plugging into an established AI framework with a few clicks of a button.
Until then, it’s vital that businesses remain mindful of the capabilities of AI, and the potential implications for their bottom line.
Where once they might have been forced to question whether they could afford to introduce machine learning into their offering, the ask will soon be whether they can afford not to, as intuitive websites, apps and retail portals begin to reap the lion’s share of consumer spend.
Simply put, the future is here. The machines have risen. Traditional commerce is all but obsolete. The time has come for businesses to adapt or die.
Vian Chinner is the founder of SA startup Xineoh, which specialises in the application of mathematical modelling and machine learning to ad technology.
