Facebook’s hope with M is to utilize the popularity of Messenger, combined with the data resources of Facebook, to create the ultimate utility for mobile discovery, stepping in on Google’s long-held turf. While Google is still the dominant player in search, Facebook’s hoping that M might be able to keep users within Facebook’s walls longer – if you’re already on Messenger and you want to search for answers, M will enable you to do so without clicking out and switching to another app.
“We start capturing all of your intent for the things you want to do,” Marcus told Wired. “Intent often leads to buying something, or to a transaction, and that’s an opportunity for us to [make money] over time.”
If M can capitalize on the masses of data Facebook has in it’s databanks, as well as the added input of human trainers refining and updating the system in real-time, it may just be the ultimate assistant service. But how do you scale such a process?
The M process works like this:
Users will tap a button at the bottom of the Messenger screen to send a message to M
M’s AI software will analyze and assess the message to determine the nature of the query, then ask follow-up questions based on its algorithm and data matches
Once the query is clarified, M will go about completing the query – whether that’s a data-based response or an actual physical task - and will send an update when complete
In this process, users won’t know whether a human has intervened, or whether the process was fully automated – the task is completed by M, that’s all you need to know. In terms of physical tasks, M’s trainers have customer service backgrounds and are able to make judgement calls on more intricate tasks, where the software might struggle to comprehend. But as they do, they’re also able to input that data into the M system to better educate it on human response and signal interpretation. The more people use the service, the more they improve the AI, which, eventually, might be able to take over the whole process.
But not yet.
M has a dedicated team of trainers on contract roles, and Marcus anticipates that as the service expands, they may employ thousands of them, at a high cost. Given that, how can Facebook expect M to become a profitable offering?
Business, with a Capital ‘M’
The success and failure of M, of course, is reliant on its accuracy. The more accurate and functional the system is, the more it’ll be used, so getting it right early on is crucial. But if Facebook can get it right, if they can provide a functional assistance service via Messenger, then the potential revenue opportunities are significant.
“If, for instance, you have a lot of calls that have to be placed by people to cable companies, that’s a pretty good signal that their customers would actually like a better way to interact with the company and maybe they should have a presence inside of Messenger directly,” – David Marcus to Wired.
This is just one example of how M data could be used to power enhanced customer service opportunities via the Messenger platform, an area of focus for the next evolution of the service. So far, Facebook hasn’t pushed to monetize Messenger, ever wary of impacting user experience (with lessons learned from previous changes to Facebook proper, most notably to its infamous News Feed algorithm). But that’s soon set to change – they’ve already announced new eCommerce options for Messenger, including improved customer service and payment options for the platform. With M, Facebook aims to build a vastly improved data set on how people are using the platform and what queries people want responses to – imagine if, when setting up your next Facebook ad, Facebook’s system could tell you what questions people were most commonly asking about your niche, even your business specifically? The bigger the data set, the more Facebook will be able to assist brands to better utilize the platform for marketing and advertising purposes – if successful, M could change the way businesses use Facebook for outreach purposes.
But then, of course, that’s a big ‘if’.
Sink or Swim
Essentially, Facebook is banking on their ability to deliver with M. The program is starting small, with Messenger users in the Bay Area being the first to get access, and a slow roll-out planned from there on in. This will enable Facebook to scale their AI system in a more accurate and regionally-focussed way – it’s likely that each location will need its own, separate M servers and systems to help it account for localized variations and dialects, and local knowledge from the M trainers will also, no doubt, play a part. Taking a location-by-location approach also makes it easier for Facebook to drop the project if it’s not successful.
The crucial element in the initial stages of the M project will be accuracy and speed – if Facebook can provide a great service, that will increase user adoption and enable them to grow the functionality of the system. Given the breadth of Facebook’s data, and the focus they’re putting on training and refining their AI, M is possibly best-placed among the assistance services to see success and to be of most use. But it depends on how Messenger users see it, whether it’s seen as a cool new function or an intrusion on what’s considered a private space.
It’s one of the many challenges the project needs to confront, but the logic behind M is sound, the approach - a different take on the AI process - is solid. Now we just have to wait and see if M can win over the masses.