Modern social listening tools and the ubiquity of social media have revolutionized the nature of consumer research. With customers routinely posting about brands and products online, companies have access to more data on consumer behavior and consumer attitudes without having to conduct surveys or focus groups. And with the evolution of powerful AI and machine learning, companies now have the tools to analyze this data to gain precise, actionable consumer insights.
While traditional data points such as who is buying a product and when remain relevant, data analytics and modern AI technology are enabling companies to achieve another level of consumer insight, analyzing more data, and doing so with more precision. Read on to learn about two techniques for generating these next-level consumer insights: sentiment analysis and audience segmentation.
Sentiment Analysis
The technology at the heart of many of the most revolutionary developments in consumer research in recent years is sentiment analysis, a powerful social listening tool that utilizes AI to analyze large amounts of online content, including social media posts and online reviews, to determine the emotional content of posts related to a certain topic. There are seemingly endless applications for sentiment analysis, but the key for any of these applications is the consumer insight that comes from quantifying emotional connection to brands.
For example, a food or beverage company could use sentiment analysis to comb through large datasets of mentions of their brand on social media platforms such as Twitter and Instagram, looking for trends in positive or negative sentiment. Maybe a specific flavor or promotion is receiving particularly high sentiment ratings, while a newly introduced flavor or advertisement is falling flat or receiving negative attention online. Locking into these sentiments is key for planning future product development and marketing strategies.
Sentiment analysis is crucially different from a focus group or survey in that it can process large amounts of data that would be infeasible to analyze manually, and this data is generated by natural, unprompted expressions of consumer attitudes rather than forced responses to questions. This means the consumer insights a brand can gain better track natural consumer behavior and are better attuned to the public conversation around a product or campaign.
Furthermore, consumer research companies offer users sentiment analysis features that simplify complex information into actionable, easily visualizable data. For example, NetBase Quid allows companies to track Net Sentiment and Passion Intensity ratings, which offer a score from -100 to a 100 based both on the strength of the positive or negative emotion expressed and the intensity with which it is conveyed in either text or image-based content.
Audience Segmentation
While general consumer sentiment on a brand, its products, and its promotions is invaluable, companies can utilize this data to create additional valuable data points that lead to actionable consumer insights. This is the goal of audience segmentation, which involves grouping consumers according to shared sentiment to identify market gaps.
For example, a brand can create segments of its own customers based on shared interests. Fans of a particular brand of clothing might also be passionate about environmentalism. This could lead the brand to emphasize sustainability in future products and campaigns, targeting both their current consumers who are passionate about this cause and other consumers who share their customers’ passions in this area but may not yet be aware of the brand.
Gaining Consumer Insights from Big Data
Â
While big data is often touted as a cure-all for research, data is not valuable in a vacuum. When it comes to consumer research, the ability to gather and analyze large amounts of data online is a potential game-changer, but only for those companies that can recognize the consumer insights in this mass of data. Using sentiment analysis and audience segmentation, brands don’t just rely on a vague notion of big data; they actively identify the next-level consumer insights to be found in the data available.