Consumer research strategies have evolved alongside AI and social media technology. Companies used to rely on focus groups and surveys to gauge consumer attitudes. Still, as brand-relevant conversations have shifted to online platforms, a new wave of consumer behavior data has become available. And with AI’s ability to analyze large data sets, companies can gain actionable consumer insights from this data.
Sentiment analysis, an AI-based social listening tool that assesses the emotional tone of online content, has dramatically changed consumer research capabilities, and savvy brands realize the benefits of this fast-evolving technology.
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What is Sentiment Analysis?
Sentiment analysis involves using AI technology such as natural language processing and image analysis to discern the attitudes and opinions expressed in text and visual content.
The text or images analyzed could come from posts on social media platforms such as Facebook, Twitter, and Instagram or other online content such as customer reviews and comments sections.
Benefits of Sentiment Analysis Tools:
Once content is gathered and analyzed. Sentiment analysis tools provide intuitive data visualizations to help companies recognize underlying trends. For example, NetBase Quid AI uses sentiment analysis to assign Net Sentiment ratings ranging from -100 to 100. These ratings can be assigned to audience segments organized by demographic characteristics such as age, gender, and location or organized by shared interests.
Using this audience segmenting approach, brands can learn how fans of a particular musician, film franchise, or sports team feel about their product, information that could lead to beneficial marketing partnerships. Or they can learn how Gen Z consumers feel about their brand in relation to Gen X consumers or compare consumer attitudes in big cities and small towns.
Because sentiment analysis enables brands to quantify emotional responses and opinions, they can engage in more fine-grained analysis. Rather than simply understanding that some consumers like their brand or product while others don’t, companies can compare detailed sentiment ratings that measure both enthusiasm and positive or negative valence of the emotions expressed.
A New Era of Consumer Research
Equipped with these more fine-grained analytical capabilities, brands that use sentiment analysis can develop more focused, targeted marketing campaigns and product development strategies.
For example, using image analysis, a beverage company could learn that their product was often enjoyed at a particular type of sporting event. Most posts will not tag a brand, but image analysis does not require that a brand is tagged to recognize that its product or logos appear in a photograph. The AI is able to sort through posts on social media and analyze posts that include brand-relevant images.
Companies can also use sentiment analysis to understand better how consumers feel about competitors. Rather than focus only on posts in which their own product or brand is featured, companies can direct these powerful analytic tools to posts that feature the products or logos of a competing brand. Doing so allows companies to understand market gaps and to identify strengths that they should emphasize.
If it turns out that a certain demographic tends to post about a competitor’s product, then messaging may need to be adjusted to target that demographic. Or a product could be revamped to address the consumer’s needs better. Recognizing why consumers are turning to competitors allows a company to improve its own products to compete proactively better.
Final Verdict:
Finally, sentiment analysis can help brands identify the causes that their customers care about. For example, if a target audience or existing customer base tends to post positive content about environmental causes. In that case, a brand could emphasize its commitment to sustainability in its advertising and product development.
Ultimately, the goal of sentiment analysis is to understand the consumer’s perspective better and listen to their preferences and dislikes as expressed naturally rather than in the context of a questionnaire or focus group discussion.
Improvements in AI technology and the rise of social media have made this more nuanced approach to consumer research possible. As these technologies improve, brands with sentiment analysis capabilities will only gain deeper consumer insights and more effectively meet consumers’ needs.
