Why artificial intelligence is set to automate marketing

Machine learning is combining with big data to bring a new level of automation and sophistication into marketing and advertising. We find out how


Virtual assistance

Another common uses for AI technology is in intelligent virtual assistants. Alaska Airlines and the US Army, for example, use intelligent agents to answer Web queries, although these systems are not self-learning.

One company that has bought true AI to online services is Australian-based Cognea (formerly MyCyberTwin), which was acquired by IBM’s Watson group in May this year.

Read more: IBM Watson analytics preps the data so you don’t have to

Indeed, Watson is one of the technologies leading the charge to bring AI into marketing, focusing on a concept called cognitive computing, which sees Watson striving to understand the context within which interactions take place.

Interactive experience leader for IBM Australia, Ian Wong, says Watson is being used in a marketing capacity to analyse both client needs and their personality types to make more tailored offers. IBM is currently implementing this technology with clients in fields as diverse as financial services, mining and retail, including with outdoor retail specialist, The North Face.

“Let’s say I want to go on a trek in Patagonia,” Wong says. “What kind of gear will I want? What North Face will do is assemble the right merchandise – so all the gear that I will need – and understand my price-point sensitivity through analytics, and then make that offer to me.

“It is an adviser, but also takes that targeted advice and turns it into an offer.”

Wong says Watson is also able to take data from a consumer’s social media feed, such as their Twitter account.

“That physiological profile can be fed into Watson to say ‘this is the kind of person Ian is’, so this is the right camping gear for him,” he says. “What we are seeing here is a feedback loop that allows organisations first to understand the behaviour of their customers.

“You need a computer that you don’t program, but that learns to find the right optimisation moment to really drive that mutual benefit forward.”

Other examples of AI in action can be found within the largest online marketers, such as Amazon and eBay, which are constantly analysing online behaviour to make more targeted offers to individuals.

“That is what machine learning does,” says Dr TIberio Caetano, chief scientist at data analytics specialist, Ambiata. “What it does is look at results of past experiments and then analyses the patterns that differentially make people choose because of the offer.

“It is this notion of ‘I want to estimate what would have happened hadn’t I done what I did’.”

Getting the right data

Dr Caetano cautions, however, that AI is no silver bullet. Too often, organisations fail to use scientific principles when designing their marketing programs.

He says one of the most common is failing to exclude consumers who would have bought their product or service regardless of the program, or failing to take into account external factors such as weather, when measuring the success of campaigns.

“The tragedy is the bricks-and-mortar – banks, insurance companies and telcos, even retailers, where most, if not all, of the marketing activity is being driven, is not properly advised from a scientific standpoint,” Caetano says. “Therefore the measurement is wrong, and the learnings are wrong.

“What you need to do is proper experimental design. That is the first step in a marketing campaign.”

While AI has taken great leaps forward in recent years, there is still some way to go before it becomes ubiquitous in our lives.

According to AI specialist at NICTA, Toby Walsh, the technology has great potential across the whole width of marketing, stating from analysing data and machine learning across big or little data.

But he says there is significant debate in the AI community as to the role that computers will and should play, as there are still many tasks where computers struggle.

“Anyone who has interacted with Google Translate will know that computers still have a pretty poor understanding of text,” Walsh says.

“There is a lot of debate in AI as to how we can as a society engineer it so that people end up with better lives and more leisure time and more freedom to do interesting things. And that is a conversation for society as a whole.”

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