The company can use the time stamps and location data associated with collected tweets to estimate where and when a person experienced a problem, making it possible to put the records on a map. This information can be compared with system logs and customer service calls. In practice, the technique picked up on issues that wouldn’t otherwise have been reported. It also detected issues earlier—around 20 minutes earlier, on average, than a customer service call came in.
“It tells us what matters to people and what affects how people feel about our network,” says Jennifer Yates, an executive director at AT&T labs who is overseeing research on new ways to manage the firm’s network. Most grumbles on Twitter are about dropped calls, followed by slow Web speeds or lack of service. Users tend to call in only with more serious problems, says Yates.
“We are working on how we can operationalize this research so that it can be used by our network managers alongside our existing monitoring tools,” says Yates. The data will accompany that from an iPhone app, Mark the Spot, that customers are encouraged to use to report problems, she says, which is used to plan network upgrades like new cellphone towers.
Yates adds that in time, users may figure out that AT&T is listening for their tweets, and become more likely to share their experiences. “I hope they would be pleased we are taking an interest,” she says.
Michelle DeHass of Attensity, a company that makes software to track and respond to tweeted complaints, says it is unusual for a company to gauge the performance of its own systems using Twitter. But she also points out that AT&T may need better tools for handling the ambiguity of language used in tweets. “Using a list of keywords is not really enough,” DeHass says. “To find everything, you need to use semantic technology able to do deep parsing and look at families of words and synonyms,” which Attensity’s software has, she adds.
Yates says AT&T has smarter forms of text analysis in the works. “It will help understand the short language in tweets and also match them up with our other sources of data, such as customer service reports,” she says.