It uses a sophisticated blend of technologically advanced linguistic processing and understanding of psychological drivers to determine exactly what is steering conversations on social media and to extract immediate contextual insights for immediate action and results.
"Traditional social listening and sentiment analysis tools have dealt with big data by essentially avoiding analysing it as a whole. These tools extract small slices of data, which may or may not be representative and pass it over to the crowd to explain what the sentiment (positive, negative or neutral) behind the data is. This only scratches the surface," says head of social insights at Echo-Social, Robin Meisel.
While proponents of crowd-sourced sentiment analysis have long touted the capabilities of humans to understand the nuances of language better than computers, Meisel says research has proven that this is not necessarily the case.
"Humans only agree on sentiment 70% of the time. We are fallible, inconsistent and limited in our capacity to crunch data. As a result crowdsourcing is costly, ineffective, unreliable and does not replicate results well."
According to Meisel, we are entering what he calls the Industrial Revolution of the social age. "By developing a tool that harnesses natural language processing and linguistic dictionaries, the depth and breadth of analysis can be expanded exponentially to keep up with the explosion of social data. The resources that were tied to sentiment analysis can now specialise in providing market insight."
Its online dashboard allows for the simultaneous monitoring of the online conversation across social platforms including Facebook, Twitter, LinkedIn, blogging platforms and forums. "If it has a data stream, we can track it."
The unique metrics include market segmentation, competitor activity, language usage, the psychological drivers behind conversations, linguistic insight, topics of relevance, advanced word clouds (drawing correlations between words allowing for early identification of trends) and geo-location analysis.
"What sets it apart, is the ability to customise the service and metrics based on a client's specific needs and interests." It can be tailored for any business sector or industry. Through the broader offering, various multi-channel feedback mechanisms can be developed for efficient and specialised responses to complaints and queries, including call centres, SMS lines, emails and social media.
This provides brands with a central platform to measure the voice of the customer - essentially integrating traditional and social customer service methodologies.
Linguistic analysis clusters data based on similarity rather than predetermined classifications such as the standard positive negative and neutral. "This system allows the data to essentially speak for itself - allowing trends, patterns and insights to emerge naturally, creating structure out of the unstructured social data.".
While the number of social mentions increases daily, it is becoming increasingly important for brands and businesses to become proactive. As much as 42% of Twitter users expect a company to respond to their enquiries in less than an hour.
"Along with these expectations come great opportunities for customer service. Seventy percent of users who receive help via social customer service avenues will return as customers in the future. There is tremendous business-value to be derived from social media - but only when it is leveraged effectively," he concludes.
