Baohong Sun

Baohong Sun
Academic background
EducationB.A.
Ph.D.,Economics
Alma materRenmin University of China
University of Southern California
Academic work
InstitutionsCheung Kong Graduate School of Business

Baohong Sun is a Chinese economist and an academic. She is a Dean’s Distinguished Chair Professor of Marketing at Cheung Kong Graduate School of Business.[1] Her research has focused on quantitative marketing, consumer decision-making, dynamic and structural modeling, AI and recommendation systems, digital platforms, pricing, personalization, and technology-driven customer behavior.[2] Her work has received media coverage in outlets such as The Guardian,[3] China Daily,[4] Forbes[5] and The New York Times.[6]

Education

Sun completed her B.A. from Renmin University of China and a Ph.D. in Economics from the University of Southern California.[1]

Career

Sun began her academic career in 1997 as an Assistant Professor of Marketing at Carnegie Mellon University. She was promoted to Associate Professor of Marketing at Carnegie Mellon University from 2004 to 2009 and then worked as a Full Professor of Marketing from 2009 to 2011, during which time she also held the Carnegie Bosch Professorship. Since 2011, she has been a Dean’s Distinguished Chair Professor of Marketing and the Director of the Web3xAI Research Center at Cheung Kong Graduate School of Business.[1]

Sun also holds professional experience, including contributing to the World Economic Forum.[7] Additionally, she was also on the Board of Directors at the Chinese Economic Society.[1]

Works

In 2016, Sun authored the book Customer-Centric Marketing: A Pragmatic Framework wherein she explained how to use advanced analytics and optimization to make dynamic, data-driven, customer-centric marketing decisions that adapt over time to maximize long-term profitability.[8] Her recent book, titled Brand Intelligence: Navigating the Transformation in the AI and Web3 Era has explained how brands transform into intelligent, AI-driven digital ecosystems that grow through data, communities, and decentralized value creation.[9]

Research

Sun's research has explored economics and quantitative marketing. Her work has focused on developing dynamic structural models.[10] A major theme in her research is analyzing rational and strategic consumer behavior, especially in environments where decisions unfold sequentially and are influenced by prices, promotions, and information.[11]

A large portion of Sun's research focuses on data-driven and model-based marketing, including customer relationship management, pricing strategy, loyalty programs, and cross-selling. She has studied how firms can optimally allocate marketing resources and design incentives by modeling how consumers respond dynamically to marketing actions such as promotions,[12] subscription pricing,[13] and recommendation systems.[14] Her work often uses rigorous econometric and structural approaches to quantify how marketing interventions affect both short-term purchases and long-term customer value.[15]

More recently, Sun's research has expanded into digital platforms and emerging technologies, including e-commerce,[16] social commerce,[17] machine learning,[18] and AI-driven marketing systems.[19] She has examined how consumer behavior evolves in online environments,[20] how recommendation systems influence exploration and purchase decisions,[21] and how firms can use big data to improve targeting and personalization.[22]

Selected articles

  • Erdem, T., & Sun, B. (2002). An empirical investigation of the spillover effects of advertising and sales promotions in umbrella branding. Journal of Marketing Research, 39(4), 408-420.
  • Li, S., Sun, B., & Wilcox, R. T. (2005). Cross-selling sequentially ordered products: An application to consumer banking services. Journal of Marketing Research, 42(2), 233-239.
  • Erdem, T., Keane, M. P., & Sun, B. (2008). A dynamic model of brand choice when price and advertising signal product quality. Marketing Science, 27(6), 1111-1125.
  • Ma, L., Sun, B., & Kekre, S. (2015). The squeaky wheel gets the grease—An empirical analysis of customer voice and firm intervention on Twitter. Marketing Science, 34(5), 627-645.
  • Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International journal of research in marketing, 37(3), 481-504.

References

  1. 1 2 3 4 "SUN Baohong - CKGSB". Cheung Kong Graduate School of Business. Retrieved May 6, 2026.
  2. ↑ "Baohong Sun - Google Scholar". Google Scholar. Retrieved May 6, 2026.
  3. ↑ "Cash is king — for now: China signals it will slow transition to cashless society". The Guardian. Retrieved May 6, 2026.
  4. ↑ "Net gains for doing business in China". China Daily. Retrieved May 6, 2026.
  5. ↑ "Amway's China Redux".
  6. ↑ "Don't Worry, Be Happy: The Warranty Psychology". The Newyork Times. Retrieved May 6, 2026.
  7. ↑ "Network of Global Future Councils 2016-2018" (PDF). World Economic Forum. Retrieved May 6, 2026.
  8. ↑ "Customer-Centric Marketing: A Pragmatic Framework". Worldcat. Retrieved May 6, 2026.
  9. ↑ "Brand Intelligence: Navigating the Transformation in the AI and Web3 Era". Worldcat. Retrieved May 6, 2026.
  10. ↑ Gong, Ke; Peng, Yi; Wang, Yong; Xu, Maozeng (December 2018). "Time series analysis for C2C conversion rate". Electronic Commerce Research. 18 (4): 768. doi:10.1007/s10660-017-9283-6.
  11. ↑ Guo, Liang; Villas‐Boas, J. Miguel (December 2007). "Consumer Stockpiling and Price Competition in Differentiated Markets". Journal of Economics & Management Strategy. 16 (4): 851. doi:10.1111/j.1530-9134.2007.00159.x.
  12. ↑ Phumchusri, Naragain; Kosawanitchakarn, Warot; Chawanapranee, Sirawich; Srimook, Sirawish (1 October 2023). "Evaluating promotional pricing effectiveness using convenience store daily sales data". Journal of Revenue and Pricing Management. 22 (5): 363. doi:10.1057/s41272-022-00415-5. ISSN 1477-657X. PMC 9684835.
  13. ↑ "Comparison of Airline Co-Branded Credit Card Programs via Frequent Flyer Money Saver Analysis for Full-Service U.S. Carriers".
  14. ↑ Luo, Anita; Kumar, V. (February 2013). "Recovering Hidden Buyer–Seller Relationship States to Measure the Return on Marketing Investment in Business-to-Business Markets". Journal of Marketing Research. 50 (1): 146. doi:10.1509/jmr.11.0295.
  15. ↑ Boustani, Noureddine; Emrouznejad, Ali; Gholami, Roya; Despic, Ozren; Ioannou, Athina (August 2024). "Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks". Annals of Operations Research. 339 (1–2): 615. doi:10.1007/s10479-023-05209-5.
  16. ↑ "Substitutive First-party Content as a Strategic Decision for Platform Growth – Evidence from a B2B Platform".
  17. ↑ Liu, Xia; Burns, Alvin C.; Hou, Yingjian (3 April 2017). "An Investigation of Brand-Related User-Generated Content on Twitter". Journal of Advertising. 46 (2): 237. doi:10.1080/00913367.2017.1297273.
  18. ↑ Thakur, Jyoti; Kushwaha, Bijay Prasad (March 2024). "Artificial intelligence in marketing research and future research directions: Science mapping and research clustering using bibliometric analysis". Global Business and Organizational Excellence. 43 (3): 153. doi:10.1002/joe.22233.
  19. ↑ Ameen, Nisreen; Sharma, Gagan Deep; Tarba, Shlomo; Rao, Amar; Chopra, Ritika (September 2022). "Toward advancing theory on creativity in marketing and artificial intelligence". Psychology & Marketing. 39 (9): 1813. doi:10.1002/mar.21699.
  20. ↑ Wang, Yang; Chaudhry, Alexander (April 2018). "When and how Managers' Responses to Online Reviews Affect Subsequent Reviews". Journal of Marketing Research. 55 (2): 165. doi:10.1509/jmr.15.0511.
  21. ↑ Chenavaz, Régis Y.; Dimitrov, Stanko (31 December 2025). "Artificial intelligence and dynamic pricing: a systematic literature review". Journal of Applied Economics. 28 (1): 19. doi:10.1080/15140326.2025.2466140.
  22. ↑ Li, Hongshuang (Alice); Kannan, P.K. (February 2014). "Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment". Journal of Marketing Research. 51 (1): 40–56. doi:10.1509/jmr.13.0050.
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