We're used to algorithms recommending books, movies, music and websites. Algorithms also trade stocks and predict crime, identify diabetics and monitor sleep apnea, find dates (and babysitters), calculate routes and assess your driving, and even build other algorithms. These math equations, which can reach thousands of pages of code and routinely crunch hundreds of variables, may someday run our lives. Companies increasingly use them to run the digital business and gain competitive advantage.
Unleashing an algorithm can lead to new customers and revenue, but it can also bring encounters with ethical and legal trouble. Already, consumer advocates and regulators are training their sights on the dark side of the algorithm revolution, such as creepy over-personalization and the potential for illegal price discrimination.
[ See "Examples of Algorithms in Action" on page 3 ]
As CEOs look to chief digital officers and data scientists to conquer the next frontier, CIOs have sometimes been on the sidelines, whether by choice or default. But as business leaders, CIOs may now have to elbow into meetings where Ph.D.s, corporate lawyers and other colleagues are talking about the data-driven future. CIOs need to join those conversations to help steer company strategy, certainly, but also to contribute to decisions about what data to pour into an algorithm and what to keep out, and how to monitor what the algorithm does.
That includes devising a defensible policy for handling the information produced, says Frank Pasquale, a professor of law at the University of Maryland. "Algorithmic accountability" will become part of the IT leader's job, he says.
[ Slideshow: 8 Analytics Trends to Watch in 2015 ]
That realization can hurt. Athena Capital Research, a high-frequency stock trader, used a proprietary algorithm called Gravy to slip in big buy and sell orders milliseconds before the NASDAQ exchange closed for the day in order to push stock prices higher or lower, to Athena's advantage. The Securities and Exchange Commission viewed that as illegal manipulation and last year called out Athena's CTO for helping other managers plot the most effective use of Gravy during at least six months in 2009. Athena settled the case for $1 million.
No one says CIOs must delve into Ph.D.-level math. But a working knowledge of basic concepts behind algorithms can help avoid bad results and bad press. "Algorithms allow us to get rid of biases we thought were there in human decision-making," says Michael Luca, an assistant professor at Harvard Business School. "But pitfalls are equally important to think about."
Algorithms can be used to make operations more efficient, answer "what if" questions and make new products and services possible. At United Parcel Service, the 1,000-page Orion algorithm does all of that. In 2003, UPS started building Orion (for On-Road Integrated Optimization and Navigation) to optimize delivery routes. You might have six errands to do on a given day. A UPS driver has about 120. The company wanted to save time and fuel by having drivers follow the most efficient routes possible while still making deliveries on time, says Jack Levis, director of process management. Levis oversees Orion and the team of 700 engineers, mathematicians and others who support it.
Cutting just one mile per driver per day saves $50 million per year, Levis says, and Orion has so far saved seven to eight miles per driver per day. UPS is on track to save $300 million to $400 million per year in gas and other costs by 2017.
The most important thing any manager can do when embarking on an algorithm project is to "work backwards," Levis says. That is, define carefully what business decisions the company struggles with, then identify what knowledge would help--what information you'd need to teach you the knowledge you lack. Then identify the raw data that--when combined and teased apart and interpreted--would provide that information.
UPS spent nine years working on Orion before putting it into production, adding and subtracting data, testing, then adding and subtracting again. For example, at first Orion used publicly available maps. But they weren't detailed enough. So UPS drew its own, showing features such as a customer's half-mile driveway or a back alley that shaves time getting to a receiving dock--data points that Orion needs in order to plan how to get a package delivered by 10:30 a.m.
But an algorithm created by data scientists in a laboratory can't anticipate every factor or account for every nuance. Suppose a business customer typically receives one package per day. If Orion knows the package isn't tied to a certain delivery time, the algorithm might suggest dropping it off in the morning one day but in the afternoon the next, depending on the day's tasks. That might be the most efficient approach for UPS, but customers wouldn't know what to expect if delivery times changed frequently.
People don't like that amount of uncertainty, and it might have cost UPS business. Companies often take deliveries in the morning, go about their business during the day and then call UPS back to request a late-afternoon pickup of an outgoing package. If UPS pushed deliveries to the end of the day for efficiency's sake, it might not get that later call, Levis says. "We started realizing the rules we told the algorithm weren't as good as they should have been," he says. "We've learned you need to balance optimality with consistency."
The Orion team is outside IT, but Levis says the IT group built the production version of Orion and CIO Dave Barnes understands what Orion can and can't do, which is critical when he helps UPS devise business strategy. UPS's My Choice service, which notifies customers of pending deliveries and lets them change delivery times or locations, wouldn't be workable without Orion, Levis says. Not only does My Choice reduce multiple delivery attempts, it also brings in new revenue: 7 million customers have signed up for the service and pay $5 per change or $40 per year for unlimited changes. Next, UPS wants to bring it to other countries.
To grow new business from algorithmic insights, companies must look for correlations that competitors haven't spotted.
Take H&R Block, for example. In December, executives at the provider of tax filing software and services talked in detail with financial analysts about the company's new algorithm, which tailors marketing email messages and in-software pop-up boxes to individual customers. The company rolled it out this tax season, after starting algorithmic tests to quantify and categorize the behavior of 8,700 tax filers in an effort to predict what customers will do.
CMO Kathy Collins discussed how, for example, H&R Block may know that, based on past behavior, you're typically a February filer who prefers to interact with the company via mobile device. If you haven't filed by Feb. 10, the algorithm will suggest that someone nudge you with an email reminder and a discount on help preparing your return. Other customers may receive an email offer the week they receive their W-2 forms.
Over time, H&R Block expects to improve its algorithm by analyzing not only the content of customer tax returns but also the very clicks a taxpayer makes while using its software, said Jason Houseworth, president of global digital and product management. "In our case," he said, "the personal data is as rich as it gets,"
The personalization made possible through the algorithm, Houseworth said, "will make each user feel that the software was not only designed for them, but is always a step ahead."
Some customers may like that, but others won't, says Pasquale, who wrote The Black Box Society: The Secret Algorithms That Control Money and Information. "There's so much pressure to know more. That's the arms race I fear."
The idea of knowing more about people is a driving force at eHarmony. The dating service matches members by their self-identified characteristics, such as hobbies and sexual orientation. But eHarmony also extrapolates what it calls unstated "deep psychological traits," such as curiosity, by putting answers to questionnaires through various formulas. A neural network also produces a "satisfaction estimator" to rate potential pairings, and the system learns over time, as members report back about their satisfaction with matches eHarmony suggests.
The company doesn't have a CIO; COO Armen Avedissian handles that role. Decisions about whether to change the algorithm are made by a team that includes Avedissian, CTO Thod Nguyen, vice president of matching Steve Carter and corporate lawyers. "It's not just hardware and software but the tactics and strategy of data manipulation," Avedissian says.
The company looks at 29 dimensions of compatibility, such as "emotional energy" and "curiosity," each of which incorporates several variables collected through detailed questionnaires. More than 125TB of data is involved. The algorithm learns by assessing what a member does with each match that eHarmony suggests (contact right away? ignore?) as well as what feedback the members provide in questionnaires and open-ended responses. That data gets poured back into the equation and the cycle starts again, more informed, Avedissian says.
The more relevant the matches, the higher the rate at which members will communicate with each other. The more they engage, the more likely they are to buy annual subscriptions. All the algorithms at eHarmony are intended to convert registrants into subscribers.
The dating service tests ideas by running slightly different algorithms for different customers, then measuring the rate at which registered members convert to annual subscribers. Risk and compliance teams run their own algorithms to see how the company's other algorithms are using sensitive data.
One recent discovery: Whether someone smokes and drinks turns out to be more important in dating in Europe compared to the United States. Once eHarmony more heavily weighted the smoking and drinking variables in its matching algorithm in the U.K., "business just took off," Avedissian says. Meaning, suggested matches were more on-target, therefore satisfaction increased--and so did conversion rates.
However, not all outcomes are expected.
Uber is upending the taxi business with an app to connect passengers with rides and a proprietary algorithm that, in part, governs "surge pricing," which raises fares at times of heavy demand. Taxi associations from New York to Paris and back have protested Uber for cutting into their business, and government regulators have challenged the company on questions of fair pricing and safety. Even so, the darling of disruption has raked in an estimated $4.9 billion in investor funding.