Back in the day, Bebo, MySpace and then Facebook were fun tools to find events, share photos and chat with friends, but communicating with brands required picking up the phone, sending emails or visiting the company website to get the information you needed.
Nowadays, it couldn’t be more different. 95 percent of millennials expect to be able to engage with brands via Facebook, and 42 percent of brand marketers state that Facebook is critical to their day-to-day business.
And Facebook is just one of many social media marketing platforms where brands need to be ready and waiting to engage with users 24 hours per day. As social media has become more embedded in all aspects of consumers’ lives, social media management has evolved into an increasingly challenging but essential role.
With users leaving more than 1.5 million pieces of user-generated content on Facebook each day, companies are leaning on new technology to help them manage huge numbers of conversations in a time-effective manner. One of the most important new tech trends for social media managers is machine learning/artificial intelligence.
If we look back less than a decade, social media management was a part-time job normally designated to recent college graduates or interns. Daily management included growing networks as much as possible, posting regular photos and ads, liking comments and responding to the occasional question.
As social media platforms grew in numbers and popularity and smartphone technology and wireless internet advanced increasing engagement, many brands contracted digital agencies to manage social media marketing campaigns.
However, over the past six years, social media has blossomed into an essential customer-service and sales channel. Social media and chat applications have advanced to the extent that users no longer use email or pick up the phone to communicate with brands—they expect to be able to leave a comment on Instagram or Facebook and receive the same speedy, informative response they would using traditional channels.
While once used predominantly by younger demographics, social media is now used by people of all ages, with most users regularly logging on to two to five different social media accounts.
Many leading companies now hire for management-level positions such as community manager to engage with the community, curate social content that targets specific demographics and strategize social marketing campaigns based on past performance, analytics and emerging trends, all across a range of different platforms.
Luckily for swamped social media managers, new technology is allowing brands to automate a number of tasks using artificial intelligence. Chat bots are growing in popularity, and industry leaders like Facebook’s Mark Zuckerberg and Chris Messina predict that the technology will revolutionize sales and customer service within the next five years. But for now, machine learning and AI offer the most efficient means of engaging millions of social media users.
Recent studies show that consumers have more respect and loyalty for brands that respond to feedback in a timely and thorough manner. Bots or machine learning systems can learn to respond to common client questions and comments immediately using natural language, which frees up time for human managers to focus on strategy and content.
In the age of big data, brands now have access to more information than ever about their customer base. One of the main challenges of social media marketing is segmentation—targeting people with content that they are interested in based on their online activity and demographics.
Using a mix of big data analysis and machine learning, brands can effectively target users with messaging in a language they really understand and push offers, deals and ads that appeal to them across a range of channels.
According to a recent Business of Reviews study, 79 percent of 500 business owners interviewed feel that online reviews, comments and forum posts are important to the financial and reputational status of their brands. In the same study, 31 percent said finding ways to monitor and manage negative content is becoming more important to their customer-service and marketing strategy.
Conversation management is probably the most critical challenge for social media managers nowadays. Managers must vet malicious content from trolls and competitors aiming to ruin the experience for the community with obscene content and offensive messages.
Brands can quickly find themselves in public-relations nightmares if problems arise with products or hoax stories from trolls or competitors balloon across social media channels. During the recent Samsung Galaxy Note 7 scandal—in which the company had to recall all handsets after there were multiple reports of batteries exploding—the news spread quickly across the brand’s social media channels and ultimately affected its brand reputation, leading to a 30-point drop in its BrandIndex rating.
Curating the flow of conversations and content could soon be managed entirely by AI systems. Using filtering systems, AI systems are able to monitor millions of user comments across a range of platforms and note emerging crisis situations before they spiral out of control.
While ethical companies should not vet comments that are not offensive or inappropriate in an attempt to pull the wool over consumers’ eyes, they can release official statements, making them look transparent and reassuring consumers from an early stage. Machine learning systems and bots can also send personalized messages to customers who raise concern.
Effective social media management is a sign of a brand’s strength in the modern digital age, and social media followings and conversations have become telling metrics for their brand success.
Some argue that AI will lead to the loss of jobs and the lack of a human element for community management. But the reality is that machine learning and bot technology allows human teams to focus their energy on providing the best, more positive experience possible for individual users, in a safe, moderated environment by replicating and automating repetitive functions.