While social media is vital for delivering stellar marketing and customer service, extracting industry and customer insights is often limited to brand reputation and user sentiment assessment. Until now, identifying intent from human-created data - such as social networks, blogs, news articles and discussions - has required hire a bunch of data scientists to write queries for you full time.
This manual approach to analytics is neither scalable nor cost-effective. In the smarter, simpler era of social media data analysis, brands and marketers need to move beyond the basics of positive, negative or neutral sentiment analysis.
Rather than merely reselling and licensing social data, the processing and analysis of it is where many technology companies are now shifting their focus, according to a recent 451 Research report. A programmatic approach to automating is the only viable and economic path forward to enable deeper insights for more organizations and businesses.
With DataSift's new VEDO Intent launch, access to social data machine learning can be made available to the masses. The new product allows non-technical people across the organization to easily build advanced machine learning-based classifiers for their organization without the need for a PhD. DataSift customers use the platform to access data from more than 20 social networks and news providers.
With Active Learning, VEDO Intent learns as posts are manually classified into categories such as rant, rave, purchase intent or churn. The platform dynamically builds a machine learning-based model to first suggest, and then fully automate, the real-time classification of millions of posts to surface insights that previously would have been hidden.
"Social data has evolved," explains Tim Barker, Chief Product Officer at DataSift. "Everyone from financial institutions through to the United Nations refers to it and we recognized that people need actionable, nuanced insights from social data to better understand their audience's mood and intent."
The company's new platform will automatically categorize social data based on its meaning, helping users transform it into useful application, product, or service data, and enabling businesses to better understand:
- What products people considering buying
- How satisfied or vulnerable to churn your customers are
- The potential effectiveness of marketing programs on target markets
- What generates the best reviews
Even helping determine if it's worth it to be a FIFA World Cup sponsor. Drawing from more than a trillion items of social, news and blog data, that's the power of learning machine-enabled decision making for the masses.