Northern Light CEO C. David Seuss presented a virtual session at The Market Research Event (TMRE) Digital Week on June 24, about the value of new, AI-driven tools for “decision-oriented analysis” of social media posts to help set and refine an organization’s product marketing strategy.

Seuss’ talk, entitled “Using Machine Learning to Make Social Media Marketing Decisions,” focused on analyzing Twitter – the most text content-rich social media platform – for the specific purpose of gleaning business insights valuable to marketing professionals.  Among the important questions such analysis can provide answers to include:

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• Which hashtags are related to my topic, and how important are each of the related hashtags?
• What author, audiences, and keywords should I buy advertising against?
• Which Twitter authors should I try to influence?
• What can I learn about my competitors’ business strategies from the hashtags they use in their social media posts?

“Assessing simple co-occurrence of Twitter hashtags is insufficient, and often downright misleading, for marketers of complex products,” Seuss asserted in his presentation.  “Understanding the context of the social media conversation is vital to derive a truly meaningful analysis of hashtag and keyword overlaps.”

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Seuss explained that using AI and machine learning techniques to measure the semantic similarity of hashtags leads to far more accurate analysis that gets at the importance, from a business perspective, of seemingly related terms.  In addition, Seuss noted that the best way to quantitatively measure the importance of related hashtags is not simply to count the number of posts in which they appear, but rather to gauge “net impressions”: count the number of followers of each post with the co-occurring hashtags being evaluated, and then take out the overlap.

Seuss illustrated his points with examples from the pharmaceutical, insurance, and other industries drawn from Northern Light Social Analytics, Northern Light’s AI-based tool that enables social media analysis for research and planning.

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