Leveraging Value-Based Consumer Segments Using Social Media Data
Social media has proven to be a valuable way to segment and micro target customers, yet most social analytics has concentrated on positive versus negative sentiment instead of focusing on the nuance that often is more predictive. For example, research has shown that an individual’s personality traits can be inferred from their relative use of pronouns and articles. By going beyond word counts and looking at the overall structure of people’s speech, we can infer the values and deeper motivations of consumers. This information can offer more forward-looking, predictive insights on what customers will care about in the future versus simply looking at what they have liked or disliked in the past. For example, one can look at past data and see that millennials were interested in things like YOLO, Snapchat, tiny houses, or craft beer and invest more in brand-building connected to those trends. Or marketing researchers can look at the broader personality trait (Do they seek out new experiences? De-emphasize material possessions? Live for each moment with little regard for the future?) that might provide meaning to trends and help predict their future actions.
In this talk, Ravi Iyer will show how text analytics can be used to uncover deeper, more predictive customer motivations to enable brands to increase influence, loyalty and engagement.