A single decision improved by 1% might seem trivial. But make 300 small improvements over a year, and the compounding effect becomes transformative. A/B testing allows companies to systematically test different approaches and optimize performance, but research shows that the startups that could benefit most are the least likely to use it.

In this episode, Professor Sharique Hasan of Duke University’s Fuqua School of Business discusses his paper “Experimentation and Start-up Performance: Evidence from A/B Testing,” which focuses on how startups use A/B testing to drive performance. Based on data from more than 35,000 startups, Hasan and his coauthors found those that adopt A/B testing experience significantly higher performance over time—sometimes doubling outcomes after a year.

Hasan explains that while the impact is strongest for smaller and non–Silicon Valley startups, these firms often lack the resources to implement A/B testing effectively. For them, he introduces the concept of “experimental thinking” as a more accessible alternative: a mindset of comparing options rigorously, asking the right causal questions, and framing decisions with clear counterfactuals.

Drawing from both large-scale quantitative analysis and rich qualitative insights from tech practitioners, Hasan describes how small, compound decisions can lead to transformative outcomes.

Duke Fuqua Insights features digestible conversations with our faculty about the most impactful research from their careers, including studies they teach in Fuqua classes. New episodes every other week in season.

For more from Duke Fuqua, visit us on LinkedIn, Instagram, Facebook, Bluesky, and the Duke Fuqua Insights newsletter.

Professor Bill Mayew explores whether public companies have visibility into the macroeconomy to filter errors in GDP data—and what that means for economic forecasting

Every quarter, the U.S. Bureau of Economic Analysis releases its initial GDP estimate—a flagship measure of economic health that influences corporate boardrooms, Federal Reserve policy, and investor portfolios. But there’s a catch: these early numbers are often wrong.

In this episode, Professor Bill Mayew of Duke University’s Fuqua School of Business discusses his research, published in the Journal of Accounting and Economics, on how corporations respond when government economic data contains errors. Mayew explains why concerns about a potential “macro data crisis” have gained traction and why errors in economic data are not necessarily signs of dysfunction.

Initial GDP estimates rely on incomplete survey data—less than half from actual three-month surveys—with the rest from extrapolations. The Bureau of Economic Analysis refines these estimates at the one-year and five-year marks as more data arrives. Revisions are therefore expected and necessary.

Mayew’s research examined whether large public companies with a unique pulse on the economy could see through the errors inherent in initial GDP estimates. Analyzing firm-level behavior, he and his coauthors found firms tend to take preliminary GDP figures at face value, failing to filter out the inherent noise. When GDP data signals strength in one quarter, companies increase investment, production, and inventory the next — and the same pattern occurs whether the GDP signal reflects real economic change or statistical error.

For policymakers, the findings underscore the need for caution when substituting government data with private sector sources like ADP payroll information. While private data may complement government releases in some cases, Mayew emphasizes government data from agencies like the Bureau of Economic Analysis and Bureau of Labor Statistics still has substantial value.

Instead, he concludes, “we need to think of other ways to improve government data, which may be increasingly possible as new and creative ways of measuring economic activity occur.”

Duke Fuqua Insights features digestible conversations with our faculty about the most impactful research from their careers, including studies they teach in Fuqua classes. New episodes every other week in season.

For more from Duke Fuqua, visit us on LinkedIn, Instagram, Facebook, Bluesky, and the Duke Fuqua Insights newsletter.

Professor Tong Guo explains how mandated transparency didn't reduce pharmaceutical payments to physicians—instead, it taught companies to optimize them.

When the federal government mandated that pharmaceutical companies publicly disclose every payment to physicians—from conference sponsorships to consulting fees—policymakers expected transparency to reduce potential conflicts of interest. Instead, the payments kept flowing, and companies learned to optimize them.

In this episode, Professor Tong Guo, an associate professor of Marketing at Duke University’s Fuqua School of Business, discusses her study of the Sunshine Act—a federal law requiring pharmaceutical and medical device companies to publicly disclose payments to healthcare providers. Published in the Journal of Marketing Research (2021), Guo's research using advanced machine learning methods called causal forests analyzed $100 million in payments between 16 antidiabetic brands and 50,000 physicians. Her findings reveal a nuanced reality: while total payments did not decline significantly, they shifted toward physicians who prescribe more expensive drugs and generate higher ROI for firms.

As Guo explains, most disclosed payments are legal, including sponsorships for events, conference travel, and educational presentations. "Much of these expenditures are considered legal," she notes, "so it's natural that it doesn't come with much pressure to cut it down."

"For firms, the number one rule for them to run their business is always to think about their ROI model," she explains. The transparency regulation gave firms information about which competitors were reaching out to which physicians and when, allowing them to optimize their existing relationships for maximum return. When transparency gives all competitors access to the same information, firms don't retreat—they optimize.

For MBA students and professionals, Guo's findings offer critical lessons extending beyond healthcare. Transparency doesn't always lead to restraint. Understanding who benefits from newly available information—and how—is essential across industries, from healthcare to digital marketing. As Guo points out, similar disclosure regulations now apply across industries —from TikTok influencers required to disclose brand sponsorships to financial services and beyond. "Transparency regulations would not necessarily lead to drastic changes of how people practice their business," she says. Different parties have different capabilities to leverage disclosed information, potentially creating new competitive advantages rather than leveling the playing field.

Duke Fuqua Insights features digestible conversations with our faculty about the most impactful research from their careers, including studies they teach in Fuqua classes. New episodes every other week in season.

For more from Duke Fuqua, visit us on LinkedIn, Instagram, Facebook, Bluesky, and the Duke Fuqua Insights newsletter.