Aravind Eye Care, a nonprofit organization in India, is one of the largest providers of eye care services in the world. Aravind’s business model is a combination of free or highly subsidized eye care for lower-income patients and market-price services for patients with higher incomes. In simple terms, the profit that Aravind makes on the 40–50 percent of its patients who pay full price is enough to cover the cost of the lower-income patients’ care, according to Sachin Gupta, Marketing and Management Communication.
“I’m fascinated by Aravind,” Gupta says. “I grew up in the business world, working in for-profit settings, thinking about efficiency and things like that. And here is a nonprofit organization that does a lot of good in the world and is highly efficient and successful and able to compete with top health-care organizations. Aravind balances the books by cross-subsidizing across patient categories, and it’s been doing this for close to 50 years now at this large scale.”
Uncovering Revenue-Mission Synergy
Gupta is an expert in marketing and health care, and he wanted to understand how Aravind succeeds in attracting paying patients despite never advertising to them. About six years ago, he approached the organization with a marketing research project.
“Aravind runs eye camps for poor people,” Gupta explains. “On a weekend, a team of ophthalmologists and technicians go to a rural area to treat low-income patients. Two to four days beforehand, they run local advertising aimed at those who live within a five- to ten-kilometer radius of the camp’s location. They put up posters. They ask village leaders, such as local politicians, teachers, and religious institutions, to spread the word. They hire advertisers to make announcements to the community through loudspeakers mounted on auto rickshaws and taxis.”
All that advertising is designed to inform the community about the upcoming camp and to raise awareness of common, treatable eye conditions—especially cataracts. As a result, the camps are well-attended by low-income people who otherwise would not have access to that type of care. But Gupta and his collaborators found that a spillover effect occurs. When Aravind runs an eye camp for low-income patients, paying patients will choose to go to Aravind’s nearby urban hospitals to undergo cataract surgery or other kinds of eye services at around the same time. Clearly, the messaging about eye conditions and treatments is also drawing the attention of those with enough money to pay for health care.
“This was an interesting insight, because you’d think that the revenue side of the operation subsidizes the mission side,” Gupta says. “You generate money through selling something, then use that money to support the free service, which does happen here. But in addition, there’s an opposite effect. Aravind is spending money on the free side, and that’s generating revenues.”
How Many Surgeries to Achieve Competence?
Gupta and his collaborators have expanded the scope of their research to encompass other aspects of Aravind’s business model and to write award-winning papers about their findings. In one project, they explored the clinical impact of the high volume of surgeries performed by Aravind surgeons. “The average surgeon at Aravind will do many times more surgeries than the average surgeon in a private practice in India,” Gupta says. “They are doing such high volumes of surgeries, they become competent much more quickly.”
The researchers found that, on average, a resident needs to complete 300 surgeries to reach competence as measured by complication rates. “These findings have led to a better understanding of the process by which resident surgeons learn and become more skilled,” Gupta says. “Knowing that it takes 300 surgeries to become a competent surgeon is an important piece of information. It’s not easy for health-care organizations to get a resident to 300 surgeries, but we put it out there that this should be a goal for residents to achieve competence.”
Gupta especially likes working with Aravind because he’s confident his research findings will be seriously considered by the organization. “The people who run Aravind are all very interested in data and insights and research,” he says. “It’s really satisfying knowing they are inclined to act upon my research.”
In Pursuit of Anonymity
Unrelated to his work with Aravind, Gupta recently embarked on a distinct line of research investigating the supposed anonymity of marketing research data.
Assurances of anonymity are often key to persuading consumers to participate in the kind of purchasing information panels that marketing research firms organize, Gupta explains. Consumers agree to allow a company to track their purchases over a specified period of time, with the understanding that any data the company gathers will be stripped of any personally identifying information before it is shared with a third party.
“But an outside intruder—for instance, someone who works for a retailer—may seek to discover a marketing participant’s identity by matching information that a retailer possesses about store purchases with marketing data that is supposed to be anonymous,” Gupta explains. “People have purchasing patterns: they buy certain things with periodicity, and that information is one way an intruder could identify them.”
“Our algorithm modifies unique data patterns slightly so that people with identifiable purchasing patterns start to look like other people.”
Gupta teamed up with Matthew Schneider (Drexel University), Yan Yu (University of Cincinnati), and Shaobo Li (University of Kansas) to determine whether anonymized market research data can be cross-referenced and matched up with individual consumers.
“We used a data set that is very commonly used in the market research business,” Gupta says. “And we found that the probability of identifying individuals based on their patterns of purchasing behaviors was very high.”
Once Gupta and his collaborators understood the nature and extent of the problem, they undertook to eliminate it. They developed statistical algorithms that distort the data, blurring individual purchasing patterns. “Our algorithm modifies unique data patterns slightly so that people with identifiable purchasing patterns start to look like other people,” Gupta explains. At the same time, the distortion does not meaningfully affect the metrics that marketing researchers are seeking, such as brand market shares or customer sensitivity to promotions.
Gupta’s findings add a detailed picture to broad public concerns about privacy, marketing, and consumer information. Gupta hopes that the algorithms generated by his research will stop the breach, becoming one of the many innovative strategies that are needed to protect consumer data across domains.
“This question of anonymity and vulnerability of data is relevant in many settings, including physicians writing prescriptions for patients and retail customers purchasing sensitive products such as birth control or pregnancy tests,” Gupta says. “These are the kinds of information you don’t want a third party to be able to match up with your actual identity.”
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