Marketers, you might be starting to get a bit fed up of seeing the acronym “AI” in the headlines of your go-to industry publications.
We get it. It’s everywhere. But we’re going to keep talking about it.
Because AI is disruptive. It’s changing how the majority of industries across the globe operate. And it’s even threatening to make certain job roles obsolete.
Is ‘ecommerce marketer’ one of those jobs? Not quite.
But it is inevitable that things will change: much of it for the better (think less admin, more creativity).
Human vs machine – which wins?
In this post, we’ll begin by exploring the decisions that marketers can trust a machine to make, before taking a look at those which—now more than ever—need the human touch.
As we know, the “I” in “AI” stands for “intelligence”. And it’s true: machines can be intelligent. Very intelligent. But in a different way to human beings.
Machines are able to process information much quicker than us, and automate laborious tasks that would take any homosapien hours.
This puts them in a strong position to take on the following ecommerce marketing tasks:
AI-powered algorithms, through unsupervised and responsive learning, can independently process large sets of data and spot patterns and/or similarities amongst customers that a marketer might easily miss.
It’s almost pointless for a data scientist or marketer to spend time manually going through large data sets if a computer can do it much much faster.
Segmentation and taste profiling
Through clustering processes, an unsupervised learning algorithm can then group customers together – based on their shared traits – to create very focused segments.
An algorithm can also use all of the various data sets available to create (or refine) a single customer view (i.e. a consolidation of all of the information available on a customer into a single record).
This in turn can be used to execute accurate taste profiling and predictive marketing; for example, knowing that customer A is most likely to be interested in category [X] (let’s say ‘trainers’, and therefore sending a message featuring a new trainer launch to boost engagement.
When it comes to cross-channel campaigns, the frequency, discount and channel can all be decided via a machine-learning algorithm using a feedback loop (which basically means perpetually learning from past actions).
Monitoring (and optimising) campaign performance
The number of messages being sent to each customer will also fall under the “machine” category, as an artificial intelligence model…