It’s abundantly clear that social media provides a rich source a data on user sentiment and that companies ought to be able to exploit it. Not just to sharpen their marketing, but also to improve their products and services – potentially, the ultimate source of customer views and a crowd-sourced suggestion box.
But the challenge is how to make sense of the vast amount of information, find meaningful patterns and use it as guide for action. The digital tools for social media monitoring and analysis, analysts say, are still primitive. The current ones focus mainly on so-called sentiment analysis, giving companies a broad-brush approval rating at best.
SAS recently introduced a social media analytics program that will compete against the major metrics players such as Radian 6 and WebTrends. The new service shows once again how blogs and social networks are deeply influencing marketing, customer support and product groups within the enterprise.
“Consumers are online right now talking about your products and services, their experience, and their likes and dislikes. Smart marketers aren’t just listening to online chatter; they are analyzing it to better focus resources and build engagement and loyalty,” said Mark Chaves, Director of Media Intelligence solutions at SAS.
According to SAS, Their new Social Media Analytics platform is built on SAS’ strengths in advanced analytics and data integration. Social Media Analytics offers seven distinct advantages:
- Enterprise-level capabilities – Collect and analyze huge quantities of data, both structured and unstructured, from internal and external sources. Integrating with CRM and marketing systems, the solution aligns social media monitoring with overall business strategy and tactics.
- A long-term view – Maintain a continuous archive of online data stretching back more than two years at the start, building over time the ability to understand trends and update historical analyses based on new information.
- Predictive analytics – The software delivers the ability to quantify influence, forecast future volume of social media conversations, and then predict their impact on the business. This helps companies allocate resources, create “what-if” scenarios and correlate key marketing metrics like brand preference, Web traffic, online campaign effectiveness and media mix.
- Extensible Language Processing – Statistical models miss colloquialisms and slang. Unlike “black-box” offerings, SAS lets marketers and analysts adjust the rules that assign sentiment to topics and apply subject-matter expertise to improve statistical approaches and better classify text. This hybrid approach provides more accurate sentiment extraction rules that give a more complete picture of customer likes and dislikes and better answers to business questions.
- Multi-language support – Understand and classify conversations in 13 distinct languages including Arabic, Chinese, Dutch, English, French, German, Italian, Japanese, Korean, Polish, Portuguese, Spanish and Swedish.
- Ability to take action – Even the best data analysis is only useful if it reaches decision makers on time, in an easily understood manner. SAS Social Media Analytics delivers real-time insights through Web-based dashboards, reports and workflow-enabled alerts. This enables organizations to respond in a timely and consistent fashion across brands, business units and service groups.
Reference: Enhanced Online News
Great post Steve! During the press conference Katie Paine said that human analysis with an 88% accuracy rate is generally accepted as “good”. This automatic analysis built-into the SAS platform has the ability to track sentiment at an accuracy rate of greater than 90%. For anyone who’s spent time tagging sentiment on individual posts, this will come as a great relief. Another very cool feature is that this tool actually has the ability to learn from human input so if you manually tweak posts for sentiment, the tool will actually adapt and learn from your input.
Thanks for the post, good stuff.
Hi Steve,
Thanks for the post. Social media monitoring software is the first step for raw aggregation, however true listening involves extensive human efforts in terms of filtering, abstracting business insights and engagement after raw aggregation. Clients were initially overwhelmed by the sheer volume of social media buzz and human resources required to make a successful listening and engagement. SAS has definately made some headway. The bottom line is that software and governance structure have to work together to make social media monitoring useful for biz objectives.
Maggie, you and James are both correct IMO and thankfully things continue to improve. My research suggests that the analysts who have looked at the new sas offering – and tried it out – are impressed by the accuracy of its automated sentiment analysis. Until now, they say, software alone has not come near the caliber of “human readers”. As James pointed out in his post this “platform has the ability to track sentiment at an accuracy rate of greater than 90%”. This is just ONE LINK in a massive chain of data. In the end it will take us humans to make sense of it all.
Funny how this was published on my birthday.
There is an entirely different way to use AI in regard to Social Media. It is a matter of stepping up to rudimentary reasoning abilities that help the social customer get what they want, become alerted to what they probably want and be able to do these things via the least amount of effort.
I will be hitting potential contacts in Texas soon.