(2021).
- Describes a framework to automate bids and budgets for digital ad-campaigns bought via Real-Time Bidding (RTB). Builds resource allocation on top of modern Multi Touch Attribution (MTA) models to transparently deliver on advertiser’s campaign goals. Reports on deployment at JD.com, and evaluation of deployed system via randomized controlled trials.
- Comments are welcome.
Joonhyuk Yang, Navdeep Sahni and Harikesh Nair (2021).
- Describes a new e-commerce search algorithm that leverages information about new products’ advertising to boost their search rankings on the platform, thereby aiding the discoverability of higher quality, new products on the platform. Deployed at JD.com. A field experiment shows that the deployment benefitted both the platform and its users, and suggests that the default presumption in tech that the search and advertising markets should be separated should be re-thought and be subject to more empirical testing.
- Comments are welcome.
(2021).
- Presents a method to blend ads and content on e-commerce platforms that respects interactions and externalities and balances multiple platform objectives. Method involves a deep-learning system for click-through prediction combined with a “virtual-bid” formulation for balancing objectives that is auto-tuned from historical data. The formulation is a building block for an auction payment system such as VCG that respects externalities. Deployed on JD.com’s mobile app.
- Comments are welcome.
Caio Waisman, Harikesh Nair, Carlos Carrion and Nan Xu (2019). (Updated: March 2021).
- An experimental design for causal inference of advertising effects for an advertiser/DSP buying ads via real-time bidding (RTB). Online experimentation is induced by randomizing the bids submitted into the RTB auction. Randomization is implemented adaptively, via a contextual bandit, so the optimal bidding policy is also learned alongside inference of the ad-lift, enabling inference of the effect of advertising while concurrently minimizing the costs of experimentation.
- Comments are welcome.
Xiliang Lin, Harikesh Nair, Navdeep Sahni and Caio Waisman (2019)
- A framework for implementing parallel advertising experiments for a publisher of digital ads; plus, a characterization of treatment effects in an environment with multiple, concurrent experiments.
- Comments are welcome.
Tong Geng, Xiliang Lin, Harikesh Nair, Jun Hao, Bin Xiang, Shurui Fan (2020). IAAI, 2021 AAAI Conference.
- A bandit-based experimentation-as-a-service (EaaS) system for testing online advertising audiences and creatives, deployed to support e-commerce advertising.
Di Wu, Harikesh Nair and Tong Geng (2020).
- Reports on e-commerce field experiments developed to evaluate the efficacy of consumption vouchers as a policy tool to stimulate household spending and help mitigate the economic costs of COVID-19.
- Comments are welcome.
Julian Runge, Jonathan Levav and Harikesh Nair (2019) (Updated: March 2021).
- Assesses the efficacy of price promotions in “Freemium” using a field experiment in a free-to-play video-game.
- Comments are welcome.
Reto Hofstetter, Harikesh Nair and Sanjog Misra (2018) (Updated: Jan 2020).
- Assesses copying of designs on a crowdsourcing platform using image-comparison algorithms and describes contest-seeker’s aversion to such copying along with changes in platform participant’s behavior over time.
- Comments are welcome.
Navdeep Sahni and Harikesh Nair (2020). Review of Economic Studies, 87(3), pp. 1529-64.
Tests Nelson’s theory of advertising as a signal using a field experiment on a restaurant-search platform.
Navdeep Sahni and Harikesh Nair (2020). Marketing Science, Jan-Feb, 39(1), pp. 5-32.
Assesses whether “native” paid search advertising formats materially deceive consumers using a field experiment on a restaurant-search platform. Presents a way to assess deception using revealed preference arguments.
Tong Geng, Xiliang Lin and Harikesh Nair (2020). IAAI, 2020 AAAI Conference.
A bandit algorithm and supporting product built to assess target audiences for digital ad campaigns.
Ruihuan Du, Yu Zong, Harikesh Nair, Bo Cui and Ruyang Shou (2019). AdKDD Workshop, 2019 KDD Conference.
Develops a data driven multi touch attribution system for a publisher of digital ads.
Harikesh Nair (2019). Handbook of the Economics of Marketing, Sept, 1, pp. 359-4439.
Review of Marketing literature on pricing over the product life cycle with a focus on recent empirical work.
Dokyun Lee, Kartik Hosanagar and Harikesh Nair (2018). Management Science, 64, 11: 5105-5131.
Content codes firms’ Facebook posts using Amazon Mechanical Turk and machine learning algorithms for Natural Language Processing to investigate the effect of advertising content on customer engagement on the social network.
Anna Tuchman, Harikesh Nair and Pedro Gardete (2018). Quantitative Marketing and Economics,16(2), pp. 111-174.
Treats advertising consumed by agents as the outcome of a deliberate choice, implying a role for the demand for ads that is co-determined with the demand for products. Assesses complementarities in such joint consumption between products and advertising using TV ad consumption and product purchase data. Develops a structural model of joint demand for products and ads, and uses it to assess counterfactual targeting under “addressable TV” scenarios.
Harikesh Nair, Sanjog Misra, William J. Hornbuckle IV, Ranjan Mishra and Anand Acharya (2017). Marketing Science, 36(5), Sept-Oct, pp. 699-725.
Reports on a marketing analytics model development and implementation effort, and its evaluation via a field-experiment at MGM Resorts International.
Oystein Daljord, Sanjog Misra and Harikesh Nair (2016). Journal of Marketing Research, April, 55(2). pp. 161-182. (Sept 2015 version).
Main point: When contracts cannot be fully heterogenous, firms gain from choosing both agents and incentives jointly. Empirically, the ability to choose agents seems to mitigate significantly the loss in incentives from the restriction to uniform contracts. This may explain the continued prevalence of uniform contracts in real-world settings.
Scott K. Shriver, Harikesh Nair and Reto Hofstetter (2013). Management Science, 59(6), June, 1425-43. (June 2012 version).
- Measures feedback effects between user generated content and social tie formation in an online social network, using a bounds-based strategy to account for endogenous network formation.
- Technical Appendix: A survey to understand users’ motivations to post content and to form ties online
Sridhar Narayanan and Harikesh Nair (2013). Journal of Marketing Research, 50(1), pp. 70-94. (August 2012 version).
Presents an approach to obtain consistent estimates of causal installed base effects in a linear model. Estimates neighborhood effects in the adoption of the Toyota Prius in California using the model.
Paul Ellickson, Sanjog Misra and Harikesh Nair (2012). Journal of Marketing Research, (Lead Article), 49(6), Dec, pp. 750-772.
- Investigates dynamic effects of Wal-Mart entry on Supermarket pricing strategies in the US in the 1990-s. Quantifies costs and benefits to Supermarket chains of repositioning their pricing into HiLo or EDLP policies.
- Appendix: Wal-Mart, Supermarkets and Price Format Repositioning in the Tradepress
Pradeep Chintagunta and Harikesh Nair (2011). Marketing Science, 30(6), Nov-Dec, pp. 977-996.
A selective review of discrete-choice models in Marketing focusing on recent applications and emphasizing the link to utility maximization.
Sanjog Misra and Harikesh Nair (2011). Quantitative Marketing and Economics, 9(3), September, pp. 211-25.
- Uses a dynamic agency theoretic model to analyze and design salesforce contracts. Validates the model using a field intervention in a US Fortune 500 firm.
- Discussion by John Rust and Rick Staelin
- Response to Discussions by Profs. Rust and Staelin, Quantitative Marketing and Economics, 9(3), September, pp. 267-73.
Wes Hartmann, Harikesh Nair and Sridhar Narayanan (2011). Marketing Science, 30(6), Nov-Dec, pg. 1079-97.
Exploits kinks in the targeting rules used by firms as a regression discontinuity design to identify causal effects of targeted marketing. Applies approach to measuring direct-mail and casino marketing response.
Harikesh Nair, Puneet Manchanda, and Tulikaa Bhatia (2010). Journal of Marketing Research, Vol. XLVII (Oct), pp. 883-895.
Exploits a natural experiment to measure causal effects of specialist physician behavior on primary care physician’s drug prescription decisions. Explores implications for pharmaceutical detailing allocation.
Wes Hartmann and Harikesh Nair (2010). Marketing Science, March/April, 29(2), pp. 366-386.
Develops an estimable dynamic demand system for storable, tied goods. Measures interstore substitution effects using the model to investigate channel-pricing issues that arise in the marketing of tied goods.
Pradeep Chintagunta, Harikesh Nair and R. Sukumar. (2009). Journal of Applied Econometrics, April/May, 24(3).
Analyzes life-cycle effects of marketing in the 32-bit video-game industry in the US.
Wes Hartmann, Puneet Manchanda, Harikesh Nair, Matt Bothner, Peter Dodds, Dave Godes, Karthik Hosanagar and Catherine Tucker (2008). Seventh Triennial Choice Symposium Session paper, Marketing Letters, 19(4), pg. 287-304.
A selective review of recent literature on empirical analysis of social interactions, focusing on identification and policy analysis.
Harikesh Nair (2007). Quantitative Marketing and Economics, 5(3), 239-292.
Develops a structural model to capture demand dynamics for durable technology products. Computes life-cycle pricing policies for firms when selling to forward-looking consumers using demand estimates. Applies model to the video-game industry to quantify impact of forward-looking behavior.
Harikesh Nair, Jean-Pierre Dubé and Pradeep Chintagunta. (2005). Marketing Science 24(3), 444-463.
Develops an aggregate demand system for use in situations where consumers buy many units of one chosen brand in each purchase occasion (discrete/continuous choice).
Harikesh Nair, Pradeep Chintagunta and Jean-Pierre Dubé. (2004). Quantitative Marketing and Economics 2(1), 23-58.
Analyzes indirect network effects in the adoption of PDA-s in the US focusing on complementarities in demand between PDA hardware and software.
Ramarao Desiraju, Harikesh Nair and Pradeep Chintagunta. (2004). International Journal of Research in Marketing 21(4), 341-357.
Analyzes heterogeneity in the speed of adoption of pharmaceutical drugs across several developing countries.