In a short period of time, social media has grown into an enormous and hyper-dense digital entity containing a staggering amount of information on preferences, activities, political affiliations, intentions, relationships, and just about anything you care to mention. And while the complexity for an individual user hasn’t increased that much, the challenge for any person or business looking to use social media as a scannable resource certainly has.
At the same time, machine learning has come a long way, and today we have systems capable of interacting with social media in various ways — learning from it, catering to it, and directly interacting with it. Let’s take a look at the main ways in which machine learning is impacting social networks:
Brand monitoring tools are gauging public opinion
Since the court of public opinion moved to social media, brands everywhere have been understandably concerned about how they come across online. The right endorsement from the right person at the right time can quadruple sales and engender immense goodwill, while passionate disparagement in terrible circumstances can sink a brand for good.
But with so much traffic flooding through social media feeds at any given time, how can a brand keep track of what people think? That’s what brand monitoring tools are for. Tools such as Brands Eye and BuzzSumo can alert you when your brand name is mentioned on any social network, and provide you with information about the context and the sentiment of the post.
They can’t always get sentiment right (posts can be sarcastic or ironic, or simply written in ways that machines can’t yet interpret), but they get it right quite often, and machine learning is ensuring that they get better at it over time. One day it should be possible to get an accurate at-a-glance real-time social media view of how a brand is being received.
Programmatic advertising is automating PPC efficiency
Programmatic ad networks have been around for a while, taking the complexity out of PPC bidding by using automated systems to make all the decisions based on set parameters. They can factor in various contextual elements (such as viewer age, history, interests, browser, etc.) then use them to compare bids and decide which ads should be selected to fill gaps.
Today, we’re seeing this kind of smart advertising moving into the social media world. Twitter has been trialing an in-stream programmatic ad service for months now, but the biggest success story in social media advertising is Facebook advertising. Owing to the incredible amount of data accessible through Facebook, it’s a dream for advertisers who want to get very granular with their parameters.
Manual advertising work is still needed online, however — programmatic networks have their issues with spam, bots, and questionable decisions — but once those issues have been ironed out, machine learning will ensure that automated systems can deliver ad materials far more effectively than people ever could. At some point, they may be able to make convincing ad materials (they already can in some instances, as we’ll see next).
Chatbots are making social media channels actionable
Chatbots aren’t the low-quality distractions of, say, 10 years ago. They’ve become practically viable and found their way into customer service platforms across the world. And most recently, they’ve started infiltrating the social media world to great effect. No, I’m not talking about the cryptic threat of Russian bots — I’m talking about conversational commerce, or the practice of bringing shopping options directly into social media chats.
For the typical phone user, this is a significant boon. Instead of browsing a brand’s website to look for new products, they can enable a Facebook messenger chatbot and ask it questions about what’s in stock, what’s discounted, what’s new, and much more. They can look at product photos, read reviews, and even place orders without leaving their chat. And machine learning drives the personalization element, picking up user preferences and tailoring answers to match. This is so valuable because digital communication is vital for building brand loyalty.
But chatbots can’t only aid the front-end of the e-commerce process: they can also aid the back-end through supporting store owners (well, Shopify store owners only — if you’ve ever thought about an online business for sale, you might want to make it a Shopify store for this reason alone). The Kit chatbot allows store admins to check stock and do all the things that a shopper might want to do, but it also allows them to change prices, add or remove products, change themes, and even create automated ads that tweak templates and put them live. It can then change ad settings over time to optimize performance.
As noted, machine learning is already doing some interesting things with social networks — parsing their data, optimizing their ads, and synching them with ecommerce systems — but they pale in significance to what will come in the next 5-10 years. As technology develops, we’ll see AI achieve some amazing things in the social media world.
About the Autor
Victoria Greene is an ecommerce marketing expert and freelance writer who’s eager to see smart systems continue to change the world of tech. You can read more of her work at her blog Victoria Ecommerce.