Since the early days of social listening much of the analysis has been focused on generating sentiment – the general overall attitude toward a brand or product. Labeled as “buzz” given its vague, abstract “thumbs up / thumbs down” nature, the issue with high-level sentiment has revolved around a lack of trust and inability to act on the information.
Earlier this year, Coca-Cola confirmed that simply determining whether “buzz” is actually positive or negative with keyword tools is a challenge. When comparing human to machine sentiment measurement they found, “When we say it’s positive, the machine about 21% of the time says it’s negative. That can cause some problems in our understanding.” This lack of trust and the vague measurement of whether something is likeable is largely futile since it’s typically not insightful nor actionable since it does not answer why.
However, as technology has advanced, allowing companies to get broader and deeper into social data, it has facilitated the evolution of sentiment to a dimension of specificity to help brands gain detailed understanding of the specific aspects and features that drive consumer sentiment, demand, decisions and ultimately purchases.
Beyond the “Buzz”
Having the ability to process the entire open social universe, well beyond the big four social networks, allows for analysis of deeper, more specific social discussions that revolve around product features, attributes and benefits and also pull in a more comprehensive array of consumer segments. Achieving this requires supercomputing capabilities to process the billions upon billions of daily comments across hundreds of millions of social sources, but the results are often detailed, actionable insights on the specific features and attributes of a brand or product.
The example below shows the consumer sentiment around specific attributes of a snack food, like calories, carbohydrates, fat and grains. Here, there is significant negative sentiment for the perceived calories, fat and sodium content of the product, and very high positive sentiment around the grain content of the snack.
The level of specificity of sentiment is actionable, guiding the brand team to shift their strategy on a variety of levels, including promotional messaging, packaging and education (the calorie content is not high relative to comparable snack foods).
This fundamentally shifts sentiment analysis from unactionable feedback of “consumers may like the product” to a specific, actionable “consumers dislike the perceived high calorie content and have a strong affinity for the product’s multigrains.”
This next example details the “state of mind” sentiment for patients at different stages of a specific type of cancer. This level of detail provides a deep understanding that drives strategic decisions like never before.
In this case the healthcare provider uses this insight to determine when and how to engage and educate patients, caregivers and healthcare providers to drive treatment adoption, compliance and overall care of the patient. They also have the sentiment of caregivers at each stage to understand their “state of mind” and the influential bearing it can have on the patient as well.
An Advanced Advantage
As social insights have evolved from cursory “buzz” to deeper, actionable intelligence, the aspect of sentiment has also become more valuable and strategic, largely given the level of detail it can provide on multidimensional views of products and brands through attitudes, behaviors and experiences.
It’s this type of highly specific, detailed insight which is helping to deliver actionable value to companies by understanding and strategically engaging consumer demand moments and decision points, even as the shift in real-time.