Research
Publications & Working Papers
Working Papers
Leveraging Large-Scale Granular Single-Source Data for TV Advertising
Authors: Tsung Yiou Hsieh with Rex Du and Shijie Lu
Status: Working Paper (R&R MKSC)
Abstract: This study introduces a novel instrumental variable (IV) for estimating the causal effects of linear TV advertising using large-scale panel data that link household second-by-second show viewership and ad exposure with daily purchase behavior. We exploit an institutional feature of linear TV: while advertisers choose which shows to target, networks quasi-randomly determine within-show ad airing times. This creates exogenous variation in focal brand ad exposure among partial show viewers, which we nonparametrically extract to construct a household-show-level IV. We establish the IV’s validity in the presence of endogeneity arising from advertisers’ show targeting decisions and households’ TV viewing behavior. Our IV offers a generalizable and flexible solution for household-level linear TV ad effect measurement using modern single-source data. Applying this method to data from a major food delivery platform, we estimate an ad response model in which both baseline purchase propensity and ad responsiveness vary with purchase history. Naïve estimates overstate ad elasticities by 55% compared to IV-corrected estimates. We also find that ad responsiveness is nonmonotonic with respect to purchase frequency and recency. These findings underscore the importance of addressing endogeneity in observational household TV ad exposure data and highlight the potential of behaviorally targeted TV advertising.
Measuring Heterogeneity in TV Advertising Elasticities: Evidence from 135 Retail and Restaurant Brands
Authors: Tsung Yiou Hsieh with Samsun Knight and Yakov Bart
Status: Working Paper (R&R JMR)
Abstract: We estimate the heterogeneity of TV advertising effectiveness across store characteristics and advertising levels using a large-scale panel of 135 US retail and restaurant brands, and then use these estimates to assess strategies for improving TV advertising performance. We find that only 48% to 56% of brands exhibit diminishing marginal returns at median advertising levels, suggesting that simply reducing ad expenditure across-the-board may not reliably lead to higher marginal effectiveness. Furthermore, we find significant heterogeneity in TV advertising elasticity across characteristics for over 93% of brands, but show that firms’ observed allocations generally fail to fully exploit this estimated heterogeneity and instead covary much more closely with simple heuristics. For example, we find that firms tend to advertise in areas where they already have high revenue, rather than in the areas estimated to have the highest incremental revenue from advertising. We project that brands could improve ad lift by a median 2.35 percentage points (relative to <0.5% median baseline ad lift) and earn tens of millions in additional revenue under identical-budget reallocations that better leverage this heterogeneity, and that 14-16 percentage points of brands with negative return-on-investment (ROI) from TV advertising could achieve positive ROI through such reallocations.
Published Papers
Leveraging Online Search Data as a Source of Market Intelligence
Authors: Tsung Yiou Hsieh with Rex Du
Journal: Foundations and Trends in Marketing
Year: 2023
Volume: 17(4), August 2023, 227-291
Abstract: Every year billions of users around the world submit trillions of queries through online search engines such as Google, Bing, Baidu, and Yandex. Over the years, aggregated and anonymized search volume data on keywords contained in all these queries have formed an epic database of human intentions that continues to expand every day. Thanks to platforms such as Google Trends, Google Ads Keyword Planner, Microsoft Advertising Keyword Planner, Baidu Index, and Yandex Wordstat, advertisers can readily assess search engine users’ collective interests over time and across geographic areas to optimize their search engine marketing efforts. In this monograph, we illustrate how online search volume data, indexed or otherwise, can be leveraged as a powerful source of market intelligence for purposes beyond search engine marketing. We do so by offering a brief tutorial on Google Trends and Google Ads Keyword Planner, two popular (and free) platforms for gathering online search trend and volume data, respectively. We review prior studies that have examined the use of aggregate online search data as 1) predictors for nowcasting and forecasting, 2) dependent variables in market response modeling, and 3) proxies for hard-to-measure constructs. In each of these three areas, we provide specific examples of applications to illustrate the power and versatility of online search data. We conclude by offering several ideas for future research where we see the full potential of online search data is still to be uncovered.