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Afiniti featured in the Wall Street Journal

06 January 2017
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**Disclaimer.  Please note that the views and comments expressed in this article do not necessarily represent the view of Afiniti.**

The next time you dial customer service, who answers your call may be determined by what you have said on Facebook.

Companies from casino operator Caesars Entertainment Corp. to wireless carrier Sprint Corp. are increasingly checking social media and other personal data to tailor calls for each customer. The practice, however, raises concerns among privacy advocates.

For decades, call centers have answered requests based simply on the order they were received: First in, first served.

A startup called Afiniti International Holdings Ltd. is trying to change that. Its artificial intelligence software, which has been installed in more than 150 call centers by dozens of companies, examines as many as 100 databases tied to landline and cellphone numbers to determine the best agent to answer each individual caller. Such matching can result in more satisfied customers and more sales, the company says.

Afiniti’s technology not only pulls callers’ histories for a business and credit profile, but seeks insights into their behavior by scouring their public Facebook and Twitter posts as well as LinkedIn pages. In the case of a sales operation, a caller is matched with the agent who—based on the agent’s own call history—has been able to close deals with customers with similar characteristics.

“It’s a little overwhelming, sometimes scary, to know how much information can be accumulated about you,” said Larry Babbio, an Afiniti board member and former Verizon Communications Inc. executive. But, he added, the trade-off is a better consumer experience.

Afiniti declined to describe an actual case because there are so many variables at play in its matchings. However, in a simplistic example, a particular agent might show results with high-income callers from South California who post frequently about traveling in Mexico. Within Afiniti’s system, future callers of the same ilk would be routed to that agent.

After an agent has taken thousands of calls, “You have a very accurate prediction of likely behavior,” said Zia Chishti, Afiniti’s 45-year-old founder and chief executive. “The machine-learning aspect allows us to tease out patterns … in a fashion that’s more effective than chance.””

Privacy advocates worry this type of sorting could erode trust if callers suspect they are being dealt with differently based on a hidden assessment of their social standing.

“There’s a process of discrimination going on,” said Joseph Turow, a University of Pennsylvania professor who studies digital marketing. “Companies are bringing data together that we have no knowledge about, and it may discriminate against in a prejudicial sense or a positive sense, depending on who we are.”

Afiniti isn’t alone in profiling callers for clients. Chicago-based Mattersight Corp. matches agents with customers based on each caller’s personality, a determination Mattersight makes after analyzing recordings of a person’s prior customer-service calls.

Mattersight says it has a personality score for about 100 million U.S. consumers, and its system is used by companies such as UnitedHealth Group Inc. and CVS Health Inc.

There is a long and established track record of recording and analyzing phone calls, and we’re just analyzing an additional attribute to that process,” the company’s chief marketing officer, Jason Wesbecher, said, referring to the company’s personality scores.

Afiniti says it pulls only from databases that are legitimately available for purchase, such as those of credit firm Experian PLC and data clearinghouse Acxiom Corp. Social media data—such as whether a caller posted about traveling or buying certain products—is included only if the person’s account is public, Mr. Chishti said. The customer data is stored in each client’s systems, not on Afiniti’s servers.

Mr. Chishti, who studied computer science at Stanford University, came up with the idea for Afiniti around 2005 after leaving a maker of clear braces that he founded. He had experience running a private-equity firm that bought underperforming call centers and boosted profits by shifting some of the jobs overseas. But the payoff from that approach began to reach its limits, so Mr. Chishti started exploring how calls themselves could be made more efficient through the use of technology.

 

How it works

  1. When someone dials into a call center, Afiniti’s system searches as many as 100 databases linked to that person’s phone number, including Experian, Facebook and LinkedIn.
  2. Afiniti also looks at census archives for the area the person is calling from, as well as the customer’s purchase history and contact frequency with the company.
  3. Afiniti tracks how each agent performs with callers with particular characteristics, and tries to match agents with callers similar to people they’ve had success with in the past.

Afiniti’s system, which doesn’t run continuously, charges customers based on the increased sales when the system is on. Companies that use the software say it increases sales-closure rates for call centers by as much as 6%.

Mr. Chishti declined to say how much revenue the Washington, D.C.-based company had this year but he says it will be profitable by next year and is considering an initial public offering. The company has raised more than $60 million in four rounds of venture-capital funding since 2013, according to Crunchbase. A person familiar with the matter said the latest round valued Afiniti at $1.8 billion.

Next, Afiniti wants to take its technology from the call center to the retail world. It is working on facial-recognition software to identify customers when they walk into a store so the system can send the best agent over to talk with them.

Mr. Chishti hopes the artificial intelligence systems he is developing will help create, not destroy, jobs. “We are one of the examples of AI tending to increase the efficiency of humans, and therefore increase the demand for human capital.”

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