2nd February 2017
By Scott Logie, MD, Insight at REaD Group
During the recent, and really rather excellent, DMA event on the Future of Customer Engagement there was a throw away remark made by one of the speakers that really stood out for me. In amongst all the Dystopian nightmare futures; predictions of millions of job losses and the creation of new ones we can’t even imagine as yet; the replacement of call centres with chatbots and the running of our homes handed over to tiny lovable robots was the statement “one aspect of customer engagement we need to consider is that much of it will be between machine and machine”.
When we think about customer engagement, at least when I do, we tend to think about computers, or even Artificial Intelligence on the company side – creating triggers when someone drops onto a website or automating outbound comms, responding to live chat on-line or in a call centre. However, there is also the automation, or computerisation, of the customer side of things. With things like Siri, Cortana, Amazon’s Echo and Dot functionality and Google Now already common place there are times when the decision to engage with a website or brand may not be made by the end consumer but could well be made by a “machine”. As such, who – or indeed what – is the customer in this instance?
From a data perspective, this makes how we gather, hold, manage and then present data and information in the future very interesting. In the past a Single Customer View (SCV) was centred around a person, or maybe a household, based at a physical address. Over time this evolved to take into account e-mail address where we would often know an individual by an email address, and start to gather a lot of data against that email address without knowing who the person was, or where they lived. In many cases it might even be that we never actually find out what that person’s full name is but through e-mail we can build up a decent amount of knowledge and information about them and start to create a meaningful relationship.
In recent times that has evolved again. From the first time an individual drops onto a website we now tend to capture the IP address and/or the device ID of the device being used. If that doesn’t connect to one we already know we then create a new record in the SCV. Over time this device connects hopefully to an email address as we get initial registration and then, if needed, to a name and address as a transaction takes place. This does mean that we do already centre our SCV developments on machines.
To a certain extent it is about ensuring that we capture the most relevant data as early as possible and then connecting that data to a “person” as we learn more. So in many ways we have the technology capability already in place to deal with building a database, or single customer view, that creates customer engagement strategies between two devices. On one side are automated triggers and engagement streams already in place, on the other side devices that may or may not be operated by a human.
Back to the recent event. During an inspiring presentation by Jeremy Waite from IBM, he introduced us to Jibo, the next generation of home help style AI. Jibo responds to human instructions but also has enough intelligence to collate data and make decisions on its own behalf. As an example, Jeremy, who has been testing it out, recently got told by Jibo that he was running late for a meeting and didn’t have time to walk so Jibo had ordered him an Uber. While this is amusing and shows how AI is developing, it also raises quite an important question: Who are we marketing to here? The person who owns the robot or the robot itself? Does Jibo decide to use an Uber because the owner has trained it to select Uber as their taxi firm of choice or does Jibo select Uber because it recognises Uber from marketing messages received? To a certain extent it doesn’t matter, as AI develops the robots will increasingly make decisions on our behalf, marketing will need to also develop to “sell” to machines.
While we might be set to market machine to machine at the moment, and be able to gather and host the data required to do so, I suspect where we currently do fall short is in our ability to distinguish between machine and person in terms of how we are trying to influence and why. This will provide a real challenge for the future and one that might need to be solved quicker than we think.
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