View From The Top: SAS/Teragram
Jim Goodnight. CEO, SAS
The influx of blogs, message boards and social media sites such as Twitter offer companies a wealth of valuable information about their products, services and brand perception. But with the amount of data increasing daily, the challenge is to identify relevant information and then act quickly to improve an organization’s knowledge. Opportunity and risk hidden in text are two sides of the same coin—and both can affect your bottom line.
Suppose you are a manufacturer selling thousands of products through sites such as Overstock.com or Amazon.com, and you want to optimize profitability using customer reviews. In the past, this involved manually wading through reams of anecdotal comments to summarize results. Not only was this costly in terms of manpower, it usually took weeks to aggregate and was inconsistent across coders.
With new text analytics technologies such as sentiment analysis, companies now automatically compute the sentiment of product reviews from mainstream sites such as Amazon and Overstock, as well as social media outlets (blogs, Twitter, etc.), and capture the key issues and overall opinion of the combined assessments. Companies are also using sentiment analysis to examine the reviews in relation to the number of stars in a product rating to extract the consumer reaction to that product.
For high-level executive reports, color-coded graphs make it easy to see exactly what these online posts mean in terms of the overall tone of coverage of their brand. Equipped with this information, senior managers can act quickly to adjust and adapt products, people and processes.
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