There are new breed of tools that help you monitor the social buzz. These tools let you .listen. into the conversation about your brand. In these tools you specify a set of keywords that define your brand or are associated with your brand and then the tools do the rest.
Survey Analytics crawl the social media networks/sites and find all the mentions of the specified keywords and bring them back to you in nicely formatted reports.
The setup in most of these tools is a very manual process. Once the data is back you will needs a human to go through and analyze the data (not any different from your web analytics tool).
Survey Analytics Social Media Analytics Tools provide lots of information such as
- Brand Mentions - Conversation about your brand/competitor/industry (as specified by keywords). You get total mentions by day/week/month and also the ability to drill down to a specific conversations.
- Brand Sentiment . What is the consumer sentiment towards your brand? Are they positive, negative, neutral on your brand?
- Influencers - Who is talking about you? How influential are they and how many times have they talked about your brand.
Social Media Analytics
Survey Analytics's Social Media Analytics provides multi-language support because a bad customer review is as valid in Spanish or French as it is in English; some estimates are that less than half of all Twitter traffic is in English.
Survey Analytics's Social Media Analytics allows organizations to advance beyond ad hoc social media interactions by maintaining an archive of conversations necessary for ongoing analysis.
Understanding that your brand or business is being viewed in a negative light is useful, but understanding which elements of your business are generating that sentiment allows you to take action. Survey Analytics's has a unique approach for developing custom taxonomies and concepts that aligns to the most important business concerns and bring a level of clarity and insight other solutions can't match.
With Survey Analytics's Social Media Analytics, subject-matter expertise and statistical models are combined to create powerful, efficient sentiment extraction rules that business expertise and statistical rigor alone cannot provide.
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