Viralheat Provides Comprehensive Social Media Monitoring and Analytics
by Bill Ives
It appears that Web analytics continues to be a growth market. At least, I see it as the important next step after the introduction of social media has dramatically increased the amount of content on the Web. Viralheat is one of the new players in this space. It is a social media metrics platform that delivers real-time data on over video and micro-blogging platforms as well as millions of blogs and websites. I recently spoke with Raj Kadam, the CEO, and Vishal Sankhla, the CTO, about their offering.
Viralheat was founded in January 2009 and launched publically in July 2009. They now have over 1500 customers on a global basis. They designed Viralheat to support each of the stages of understanding and responding to what is happening on the Web: listen, measure, analyze, and engage. The goal is to provide a single tool for each of these activities.
They made a decision to built all the tools themselves so that don’t have to rely on any third party services. They use a dynamic P2P cloud infrastructure to provide data aggregation, processing, and analysis. This gives them more control over both the data and the pricing of their service.
Viralheat identifies its target content sources and then filters out those who are non-influencers. It looks at both the overall followers to a blog or site, as well as the mentions of a specific entry. You can see this is the screen examples below. While much can be done within Viralheat, you can export data into any format to combine it with business intelligence platforms. I was impressed with the depth of their analytics.
Below is a screen shot of trends from Twitter and Google Buzz on a topic, in this case, Dell. You can see the trend over time. There are general statistics such as total messages, unique authors, total impact, and average impact (or follower) per author. There are conversational statistics such as retweet percentage, top language, and sentiment analysis. You also see the top influencers by volume and the top influencers by impact or followers. Then you can see the actual top mentions and have these sorted by positive and negative sentiment.
You can drill into a stream of related content to see details on an individual source as shown below.
And here more detail from their integration with Klout.
And here is more detail from their integration with Twitalyzer.
In setting up your queries you can use Boolean logic for further refinement. You can also add location parameters. You can create email reports to share results. Viralheat works in real time. They do sentiment analysis through NLP to look at the grammar as well as the words in a message instead of a static dictionary to enhance accuracy. They also provide a REST API to allow you to embed Viralheat in other applications. This integration works in both directions so they can provide a common interface across applications.
I think this is a smart set of features. There is a world of useful data being created by the transparency of social media. Viralheat is a useful and accessible way to tap into this data.
