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The company touts its NASA tech and endless possibilities for making customers happy — so much so that I had to try it out myself.
Trained to pick up on verbal clues that indicate different personality types, my service rep, Lila, catches that I’m not serene like a “Yoda” or emotionally focused like an “Oprah” — two of six basic profiles in eLoyalty’s arsenal. Rather, she figures out that I’m belief-focused, with a strong helping of matter-of-factness, and helps me through the rest of call with no nonsense and no profuse apologies.
You’ve probably never heard of eLoyalty, but they’ve almost certainly heard you — and quickly pegged your personality by analyzing nothing more than your voice over the phone, parsing your words, pauses and even inflections on the spot.
The company works with call centers that handle the nation’s biggest car-insurance firms, banks and health care organizations. They’re usually the ears listening in after the automated message promises you, “This call is being recorded for quality assurance.”
While we all like to think we are complex beings who defy understanding by a mere algorithm, that’s not actually the case, according to eLoyalty’s Melissa Moore, the company’s vice president for behavioral analytics.
“When we go into distress, we revert to our core way and go into very familiar patterns,” Moore said.
The company also relies on data — lots of it.
Every single call to an eLoyalty client — exasperated to routine — gets recorded. Every few hours a batch of recorded audio is uploaded to eLoyalty’s data center in Minneapolis where algorithms parse the calls, looking for anomalies and customers “going red,” industry parlance for a customer getting irate.
It now claims a database of more than 500 million recorded phone calls and a team of 150 behavioral scientists, linguists and statisticians who test new correlations.
“We can analyze 10,000 of a call center employee’s calls,” said eLoyalty vice president Jason Wesbecher. “We can say out of those 10,000, we identified distress on 600, and of those 400 were ‘emotions,’ so we need to work with how to train that person for working with emotions-based callers.”
ELoyalty exclusively licensed the NASA technology, which, in turn, is based on the personality typing theories of psychologist and author Taibi Kahler. (Some might remember Kahler as the personality guru used by Bill Clinton in his 1992 election campaign to connect better with voters.)
Kahler’s methodology divides people into six main personality types:
Spock: Thoughts-based person who approaches every issue rationally with a “just the facts, ma’am” mentality.
Princess Diana: Emotions-based person who wants warmth and congeniality.
Rush Limbaugh: Opinions-based person, a person for whom strongly held beliefs often trump facts.
Robin Williams: Reactions-based person who immediately likes or dislikes something and enjoys playing.
Donald Trump: Actions-based person, a person who prefers doing to talking.
Yoda: Reflections-based person, someone who likes to think matters through.
While each of us contains a bit of each, the company says, we all have dominant parts.
For example, eLoyalty’s algorithm might notice a call that is abnormally long and filled with heated language. The company’s software would flag the call, so that a supervisor can review it and develop a plan for contacting the customer. That’s key, eLoyalty says, for companies to make sure to deal with calls where a customer is threatening to sue or to call a congressman.
Training CSR’s is just the beginning of what personality typing can do, according to eLoyalty’s Wesbecher.
“We can feed the profile in the customer’s data warehouse, so they can tailor the website and outbound marketing communication to that person’s style,” Wesbecher said. “So when a thoughts-based person logs onto Bank of America, we know he prefers a highly organized, very clean site like Google.”
“Whereas” he continues, “when an emotions-based person logs in, she loves it if the first thing she sees is a picture of a family on a patio with a dog."
One borough of London recently released data on the first 1,000 disability claimants on which the technology was tested. Of the 1,000 subjects, 43 — or 4.3 percent — were flagged by the system and all of these were found to have filed false claims or displayed a high potential for committing fraud.
Originally posted by daddyroo45
This is great information. The next time I have one of those recorded calls about half way through I am going to recite "Mary had a little lamb" Just wonder what their data compilation machine would do about that?