What Marketers Need to Know About Machine Learning for Social Listening 2.0


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For years, the smartest brands have used social listening tools to keep tabs on their brand image and audience. But today, that’s not enough, which is why companies want to get to the “why” behind these metrics.

New social media analytics tools, built on powerful machine learning and AI technologies, dive deep into the nuances of consumer sentiment and purchase intent in order to surface new trends and audience affinities as they evolve in real time, or even before they happen.

Crimson Hexagon is an enterprise social media analytics company providing insights for brand strategy and market research, and at #SMWNYC, Mitch Brooks (Sr. Director, New Products, Crimson Hexagon) explained how some of today’s cutting-edge brands are using these tools to stay ahead of the competition.

In this video, Mitch discussed one aspect of technology that is predicting the outcomes of likely scenarios using machine learning by analyzing visuals.

Understanding the processing power of machine learning

As Mitch mentioned in the video above, machines are dependent on processing power, but for scalable tasks such as evaluating customer interactions, optimizing campaigns, and providing insights for learning, machines can undoubtedly out-perform a team of humans. However, humans are still better at understanding language, interpreting words based on context, tone, and what we know about the person saying them. So, how do we strike a balance?

Doug Conely explains in “Machine learning and the customer journey” that:

“Machines can quantitatively evaluate the consumer journey of millions of individual customers more quickly, efficiently, and effectively than any human planning team could ever hope to replicate, producing valuable insights into consumer behavior at a depth and level of detail that was never previously possible. These insights, in turn, can then be used by the machine to automatically deliver relevant messaging to consumers at a frequency appropriate to their stage of the consumer journey.”

While machine learning is often an invisible layer of technology we use everyday, for many people and organizations, it can be intimidating to adopt it as everyday use for automation and optimization.

As the technology continues to improve, and more case studies are published about the success of machine learning, this barrier for entry of many organizations will hopefully vanish, and allow more marketers to make smarter, faster, more efficient decisions with their insights and data.

Machine Learning 101: Structured data vs. Unstructured data

Crimson Hexagon’s “How Does Machine Learning Improve Social Media Analysis?” blog post is a terrific resource for both newcomers of machine learning, as well as industry experts who might want a fresh perspective.

Crimson Hexagon defines machine learning (in a social media analysis capacity) as “an analysis tool the ability to learn exactly what you’re looking for in social media posts, and categorize posts based on that training.” Social media is unstructured data, which means it’s variable and complex, making it much more difficult to sort, categorize and analyze.

On the other hand, structured data refers to the kind of data that is organized and displayed in a database with rows and columns, such as sales numbers and business addresses that one can easily sort.

Understanding these concepts are essential for today’s marketers to take the next steps of social listening with the help of machine learning. The perfect balance of manual input and artificial intelligence might not be evident from Day 1, but marketers who experiment, do their research, and utilize available tools will be the ones who can extract valuable insights from the trillions of data points across social media and the web.

Further analysis and commentary from Mitch’s #SMWNYC talk

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SMW Staff

SMW News, Social Media Week

@socialmediaweek

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