This post was originally featured in the Social Media Week Blog on February 3rd, 2014
Today’s companies realize the importance of social listening for crisis management, lead generation, product development, customer service, real-time marketing, and more, but lots are struggling to find the best way to filter, manage, and react to the flood of brand mentions and relevant industry-related conversations.
Many have turned to sophisticated social listening software solutions that can understand and pinpoint thousands, sometimes even millions, of positive and negative social media conversations in real-time across multiple languages. They are well beyond the abilities and efficiency of any human social media team — but how much can social listening technology really accomplish, and without human assistance, at what level of accuracy?
Let’s Start at the Beginning: How the “Machine” Works
Social Media Listening technology uses NLP (Natural Language Processing) — computer systems that process language in terms of its meaning and analyze language patterns to understand social media conversations. NLP offers valuable insights by tracking sentiment — the tone of a social media post, tweet, LinkedIn update, etc. — and tags that post as positive, negative, or neutral.
For instance, NLP gives you the opportunity to manage your reputation by seeking out negative mentions of your brand and then gives you the ability to engage with those who posted the comments. At the same time, you can monitor all mentions of your competitors, providing you with valuable insights into their businesses and brand reputations. For market research, it gives you both a high-level global snapshot of the sentiment around your marketing campaigns, new products, brand perception, etc., as well as the ability to drill down for more detail and context.
But Don’t Worry; Robots Won’t Be Taking Over the World… At Least Not in the Beginning
Although computers can analyze and classify much more data at a much quicker rate than humans and with more consistent results, they can’t do it all on their own. They need a little human insight and management, which is something that every organization needs to recognize when setting up a social listening program.
The Machine needs a Man (or Woman) for 2 key reasons:
1. Just as human speech is always evolving, so does NLP. Machine-learning algorithms learn how humans classify data – and copy it. Humans activate the learning system and classify data manually, and then machine-learning copies your classification process. Eventually, with periodic human correction and adjustment, the machine can classify automatically. But, as speech evolves, humans need to go in and do a bit of tweaking.
2. The internet is a hive of sarcasm, snark, acronyms, and made-up words and phrases that machines have trouble understanding. Human assistance is needed here. Humans have a complete database of knowledge and context experience that allows them to understand the true meaning of each message.
The “Machines” are learning how to be smarter and more efficient than humans… but they need humans to get them there.
Does that sound a little unnerving? Well, let’s put it this way, this technology will save you both time and work — but only after you initially dedicate time and work to customizing it…a well-deserved trade.
Just like hiring a great human team and training them to be the best team for your particular needs, leveraging the optimal social listening solution for your brand’s needs and training it, will allow you to accomplish infinitely more.
It just takes a little teamwork.
