As BBVA Compass was tinkering with its retail banking pricing strategies last fall, robots were scraping the web behind the scenes.
Like its peers, the Birmingham, Ala. bank needed to adapt to caps on interchange fees. It had to decide which benefits its retail customers would keep and which would get the ax.
On a hunch, the bank decided to drop its $25 checking anniversary bonus, while keeping its debit rewards and other features.
The computers confirmed that people were upset, but prodded executives to tell customer service reps and tellers to explain that BBVA Compass had decided to keep its other perks.
Those robots, really snippets of code dipping and diving into paragraphs and sentences across the internet, gave the bank what focus groups and pollsters would have taken months to confirm.
The reports the bank receives from the machines give BBVA daily insights into consumer reaction to the bank and its competitors.
"We look at our own [bank]. We look at our major competitors," says John Wessman, BBVA Compass' executive vice president and chief marketing officer. "You tend to get a little bit different perspective from consumers. They will be much more open and sharing on their Facebook accounts than they would be if they called a call center."
The technology BBVA used is an outgrowth of sentiment analysis — the same type of study researchers conduct when polling an audience. A computer program, instead of asking people questions, surveys the Web on a large scale.
Technology giants such as IBM and SAS offer clients software packages that comb the Web for clues to consumer sentiment. Those programs parse language and attempt to understand the meaning of words, pairs of words or phrases, in context.
This has become ever more important as banks move their customers out of branches and toward the Web. Those customers are less likely to walk into a brick-and-mortar building or chat directly with a teller.
And regulators such as the newly created Consumer Financial Protection Bureau have already said they plan on crowdsourcing complaints. That means a bank needs to monitor its presence on the Web even more closely.
Still, the technology is more of an ongoing study than a science.
Many vendors, and even more computer scientists and academics, are working to perfect methods of gauging feelings and emotions across the Internet without hiring a pollster.
The practice has only recently filtered into the financial services industry, and the largest players in the past several years have just grasped the benefits it could have for business.
In the end, the technology could be as valuable to banks as face-to-face interactions.
The Study of Sentiment
Linguists and programmers have been developing their brand of sentiment analysis since the beginning of the last decade.
The science incorporates natural language processing, which examines sentences and paragraphs, mimicking the way a researcher might speak to a member of a focus group. It uses filters similar to, but more sophisticated than, programs that spot spam emails.
It's artificial intelligence. Not the kind you've seen in sci-fi movies. It's more about machines learning from data and experience sifting through large quantities of data to inform business management and problem-solving.
The simplest method of sentiment analysis measures good words against bad.
A more complex model has a computer working to figure out phrases and groups of words associated with positive or negative feelings in sentences that researchers choose.
"If I have 100 tweets, and 50 of them are positive and 50 of them are negative, what is the difference?" says Eugene Wu, a database graduate student in MIT's Computer Science and Artificial Intelligence Laboratory. "The computer will distinguish them and look for a word or pair of tweets that are more positive. [The computer] can start saying these words are closely associated with positive feelings."
Methods computer scientists use to gauge sentiment differ according to the geographies they're polling, as well as the cultures and businesses those algorithms are trying to cover.
The technology will be someday be able to tell with certainty whether a person is a buyer or seller, for instance.
Researchers also try to figure out the subjects of individual conversations that happen on Twitter or in email messages.
It's called topic modeling.
"The question is not if people are tweeting positively or negatively, but can we discover automatically the nature of the underlying topic that is under discussion," says Philip Resnik, a professor of linguistics at the University of Maryland, who also works at the university's Institute for Advanced Computer Studies and is lead scientist at Converseon, a social media agency.
One early application for sentiment analysis was academics trying to gauge the positions of movie reviews, says Apoorv Agarwal, a fourth-year doctoral student at Columbia University. Agarwal last year co-wrote an academic paper on sentiment analysis of Twitter data.
The reviews, because of their rating systems, provide a guide for computers to measure against.
Academics later set out to analyze different views taken on a subject in longer online articles. Further analysis would identify the different moods found within a story.