Applying Large Language Models to Sponsored Search Advertising
References
- (2012) Return on quality improvements in search engine marketing. J. Interactive Marketing 26(3):141–154.Crossref, Google Scholar
- (2011) Location, location, location: An analysis of profitability of position in online advertising markets. J. Marketing Res. 48(6):1057–1073.Crossref, Google Scholar
- (2015) Do organic results help or hurt sponsored search performance? Inform. Systems Res. 26(4):695–713.Link, Google Scholar
- (2015) Keyword search advertising and first-page bid estimates: A strategic analysis. Management Sci. 61(3):507–519.Link, Google Scholar
- (2025) AI-human hybrids for marketing research: Leveraging LLMs as collaborators. J. Marketing 89(2):43–70.Crossref, Google Scholar
- (2019) Learning in repeated auctions with budgets: Regret minimization and equilibrium. Management Sci. 65(9):3952–3968.Link, Google Scholar
- (2013) The role of search engine optimization in search marketing. Marketing Sci. 32(4):644–651.Link, Google Scholar
- (2015) Consumer heterogeneity and paid search effectiveness: A large‐scale field experiment. Econometrica 83(1):155–174.Crossref, Google Scholar
- (2020) Online display advertising markets: A literature review and future directions. Inform. Systems Res. 31(2):556–575.Link, Google Scholar
- (2022) Generating ad creatives using deep learning for search advertising. Turkish J. Electrical Engrg. Comput. Sci. 30(5):1881–1896.Google Scholar
- (2020) Plug and play language models: A simple approach to controlled text generation. Preprint, submitted March 3, https://arxiv.org/abs/1912.02164.Google Scholar
- (2022) How generative AI is changing creative work. Harvard Business Rev. (November 14), https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work.Google Scholar
- (2018) Ad click prediction in sequence with Long Short-Term Memory Networks: An externality-aware model. SIGIR’18: Proc. 41st Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 1065–1068.Google Scholar
- (2022) Letting logos speak: Leveraging multiview representation learning for data-driven branding and logo design. Marketing Sci. 41(2):401–425.Link, Google Scholar
- (2019) MOBIUS: Towards the next generation of query-ad matching in Baidu’s sponsored search. KDD’19: Proc. 25th Internat. Conf. Knowledge Discovery & Data Mining (Association for Computing Machinery, New York), 2509–2517.Google Scholar
- (2010) Automatic generation of listing ads by reusing promotional texts. Proc. 12th Internat. Conf. Electronic Commerce (Association for Computing Machinery, New York), 191–200.Google Scholar
- (2009) An empirical analysis of search engine advertising: Sponsored search in electronic markets. Management Sci. 55(10):1605–1622.Link, Google Scholar
- (2011) Online display advertising: Targeting and obtrusiveness. Marketing Sci. 30(3):389–404.Link, Google Scholar
- Google (2023) About Ad Rank. Accessed October 26, 2023, https://support.google.com/google-ads/answer/1722122.Google Scholar
- (2016) Scalable semantic matching of queries to ads in sponsored search advertising. SIGIR’16 Proc. 39th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 375–384.Google Scholar
- (2009) Website morphing. Marketing Sci. 28(2):202–223.Link, Google Scholar
- (2016) Deal-seeking versus brand-seeking: Search behaviors and purchase propensities in sponsored search platforms. MIS Quart. 40(1):187–204.Crossref, Google Scholar
- (2024) Generative AI for scalable feedback to multimodal exercises. Internat. J. Res. Marketing 41(3):468–488.Crossref, Google Scholar
- (2021) An empirical study of generating texts for search engine advertising. Proc. 2021 Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Language Tech. Indust. Papers (Association for Computational Linguistics, Pennsylvania), 255–262.Google Scholar
- (2024) Frontiers: Determining the validity of large language models for automated perceptual analysis. Marketing Sci. 43(2):254–266.Link, Google Scholar
- (2024) Predicting purchase intent: Deciphering customer interactions with AI assistants. Preprint, submitted August 28, https://dx.doi.org/10.2139/ssrn.4939706.Google Scholar
- (2018) A semantic approach for estimating consumer content preferences from online search queries. Marketing Sci. 37(6):930–952.Link, Google Scholar
- (2019) Large scale cross category analysis of consumer review content on sales conversion. Leveraging deep learning. J. Marketing Res. 56(6):918–943.Crossref, Google Scholar
- (2021) Content-based model of web search behavior: An application to TV show search. Management Sci. 67(10):6378–6398.Link, Google Scholar
- (2019) A decade of online advertising research: What we learned and what we need to know. J. Advertising 48(1):1–13.Crossref, Google Scholar
- (2017) Single-paper meta-analysis: Benefits for study summary, theory testing, and replicability. J. Consumer Res. 43(6):1048–1063.Crossref, Google Scholar
- (2022) US search ad spending 2022. A tried and true lower-funnel tactic thrives amid uncertainty. eMarketer (September 12), https://www.insiderintelligence.com/content/us-search-ad-spending-2022.Google Scholar
- (2015) Position effects in search advertising and their moderators: A regression discontinuity approach. Marketing Sci. 34(3):388–407.Link, Google Scholar
- (2018) The order effect of advertisers on consumer search behavior in sponsored search markets. J. Bus. Res. 84:24–33.Crossref, Google Scholar
- (2021) Carbon emissions and large neural network training. Preprint, submitted April 23, https://arxiv.org/abs/2104.10350.Google Scholar
- (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8):1–9.Google Scholar
- (2018) Keyword length and matching options as indicators of search intent in sponsored search. Inform. Processing Management 54:175–183.Crossref, Google Scholar
- (2022) Frontiers: Supporting content marketing with natural language generation. Marketing Sci. 41(3):441–452.Link, Google Scholar
- (2011) Zooming in on paid search ads—A consumer-level model calibrated on aggregated data. Marketing Sci. 30(5):789–800.Link, Google Scholar
- (2017) A new method to aid copy testing of paid search text advertisements. J. Marketing Res. 54(6):885–900.Crossref, Google Scholar
- (2018) Exclusive placement in online advertising. Marketing Sci. 37(6):970–986.Link, Google Scholar
- (2020) It’s not just size that matters: Small language models are also few-shot learners. Preprint, submitted September 15, https://arxiv.org/abs/2009.07118v1.Google Scholar
- (2016) Read this paper! A field experiment on the role of a call-to-action in paid search. Eur. Conf. Inform. Systems ECIS Proc. 63:1–15.Google Scholar
- (2023) Leveraging AI for content generation: A customer equity perspective. Sudhir K, Toubia O, eds. Artificial Intelligence in Marketing, Review of Marketing Research, vol. 20 (Emerald Publishing, Leeds, UK), 125–145.Crossref, Google Scholar
- (2021) The path to click: Are you on it? Marketing Sci. 40(2):344–365.Link, Google Scholar
- (2020) Targeting prospective customers: Robustness of machine-learning methods to typical data challenges. Management Sci. 66(6):2495–2522.Link, Google Scholar
- (2018) Competition and crowd-out for brand keywords in sponsored search. Marketing Sci. 37(2):200–215.Link, Google Scholar
- (2013) Practice Prize Paper—PROSAD: A bidding decision support system for profit optimizing search engine advertising. Marketing Sci. 32(2):213–220.Link, Google Scholar
- (2019) Identifying customer needs from user-generated content. Marketing Sci. 38(1):1–20.Link, Google Scholar
- (2019) Extracting features of entertainment products: A guided Latent Dirichlet Allocation approach informed by the psychology of media consumption. J. Marketing Res. 56(1):18–36.Crossref, Google Scholar
- (2021) A near-optimal bidding strategy for real-time display advertising auctions. J. Marketing Res. 58(1):1–21.Crossref, Google Scholar
- (2014) Morphing banner advertising. Marketing Sci. 33(1):27–46.Link, Google Scholar
- (2017) Attention is all you need. 31st Conf. Neural Inform. Processing Systems NIPS 2017 (Curran Associates Inc., Red Hook, NY), 1–15.Google Scholar
- (2024) A bottle of water per email: The hidden environmental costs of using AI chatbots. Washington Post (September 18), https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/.Google Scholar
- (2011) Price competition and endogenous valuation in search advertising. J. Marketing Res. 48(3):566–586.Crossref, Google Scholar
- (2010) Analyzing the relationship between organic and sponsored search advertising: Positive, negative, or zero interdependence? Marketing Sci. 29(4):602–623.Link, Google Scholar
- (2018) Search engine advertising for organic food: The effectiveness of information concreteness on advertising performance. J. Consumer Behav. 17(1):47–56.Crossref, Google Scholar
- (2020) Effects of paid search advertising on product sales: A Chinese semantic perspective. J. Marketing Management 36(15–16):1481–1504.Crossref, Google Scholar

