The global importance of online advertising calls for a detailed understanding of consumer-specific responses to online ad repetitions. A key concern for advertisers is not only whether some consumers display degrees of “wearout” but also whether they can surpass a point at which additional exposures have a negative marginal effect: “weariness.” The authors examine a large-scale advertising campaign aimed at driving viewers to a target website, which comprises more than 12,000 users across over 400 websites. These data are analyzed using a flexible discrete mixture specification that accommodates different response shapes over ad stock and timing and parcels ad viewers into response classes based on their internet usage metrics. The resulting classes display varying degrees of wearout, with one subgroup, accounting for about 24% of the sample, evincing weariness. The model also estimates differential publisher effectiveness, with the most effective publisher being nine times more effective than the one 26 places down. The authors demonstrate that the finding of weariness is robust to all the model’s main components, with one key exception: heterogeneity in users’ ad response. Analysis further suggests that an appropriate “profiling and capping” strategy can improve ad deployment by as much as 15% overall for these data.
AnandPunamSternthalBrian (1990), “Ease of Message Processing as a Moderator of Repetition Effects in Advertising,” Journal of Marketing Research, 27 (3), 345–353.
2.
BalseiroSantiago R.FeldmanJonMirrokniVahabMuthukrishnanS. (2014), “Yield Optimization of Display Advertising with Ad Exchange,” Management Science, 60 (12), 2886–2907.
3.
BassFrank M. (1969), “A Simultaneous Equation Regression Study of Advertising and Sales of Cigarettes,” Journal of Marketing Research, 6 (3), 291–300.
4.
BassFrank M.BruceNorrisMajumdarSumitMurthiB.P.S. (2007), “Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship,” Marketing Science, 26 (2), 179–95.
5.
BassFrank M.ClarkeDarral G. (1972), “Testing Distributed Lag Models of Advertising Effect,” Journal of Marketing Research, 9 (3), 298–308.
6.
BatraRajeevRayMichael L. (1986), “Situational Effects of Advertising Repetition: The Moderating Influence of Motivation, Ability, and Opportunity to Respond,” Journal of Consumer Research, 12 (4), 432–45.
7.
BelchGeorge E. (1982), “The Effects of Television Commercial Repetition on Cognitive Response and Message Acceptance,” Journal of Consumer Research, 9 (1), 56–65.
8.
BermanRon (2018), “Beyond the Last Touch: Attribution in Online Advertising,” Marketing Science, 37 (5), 771–92.
9.
BraunMichaelMoeWendy W. (2013), “Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories,” Marketing Science, 32 (5), 753–67.
10.
BrownMillward (2012), “Do TV Ads ‘Wear Out’?”WPP, New York.
11.
BruceNorris I.FoutzNatasha Z.KolsariciCeren (2012), “Dynamic Effectiveness of Advertising and Word of Mouth in Sequential Distribution of New Products,” Journal of Marketing Research, 49 (4), 469–86.
12.
CalderBobby J.SternthalBrian (1980), “Television Commercial Wearout: An Information Processing View,” Journal of Marketing Research, 17 (2), 173–86.
13.
CampbellMargaret C.KellerKevin L. (2003), “Brand Familiarity and Advertising Repetition Effects,” Journal of Consumer Research, 30 (2), 292–304.
14.
ChakrabortySubrata (2015), “Generating Discrete Analogues of Continuous Probability Distributions: A Survey of Methods and Constructions,” Journal of Statistical Distributions and Applications, 2(1), 6, https://doi.org/10.1186/s40488-015-0028-6.
15.
ChatterjeePatraliHoffmanDonna L.NovakThomas P. (2003), “Modeling the Clickstream: Implications for Web-Based Advertising Efforts,” Marketing Science, 22 (4), 520–41.
16.
CorkindaleDavidNewallJohn (1978), “Advertising Thresholds and Wearout,” European Journal of Marketing, 12 (5), 329–78.
17.
CraigC.S.SternthalBrianLeavittClark (1976), “Advertising Wearout: An Experimental Analysis,” Journal of Marketing Research, 13 (4), 365–72.
18.
DalessandroBrianHookRodPerlichClaudiaProvostFoster (2012), “Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies,” Big Data, 3 (2), 90–102.
19.
DanaherPeter J.DaggerTracey S. (2013), “Comparing the Relative Effectiveness of Advertising Channels: A Case Study of a Multimedia Blitz Campaign,” Journal of Marketing Research, 50 (4), 517–34.
20.
DaytonC. MitchellMacreadyGeorge B. (1988), “Concomitant-Variable Latent-Class Models,” Journal of the American Statistical Association, 83 (401), 173–8.
21.
DeightonJohnHendersonCaroline M.NeslinScott A. (1994), “The Effects of Advertising on Brand Switching and Repeat Purchasing,” Journal of Marketing Research, 31 (1), 28–43.
22.
DempsterArthur P.LairdNan M.RubinDonald B. (1977), “Maximum Likelihood from Incomplete Data Via the EM Algorithm,” Journal of the Royal Statistical Society, Series B (Methodological), 39 (1), 1–38.
DrèzeXavierHussherrFrançois-Xavier (2003), “Internet Advertising: Is Anybody Watching?” Journal of Interactive Marketing, 17 (4), 8–23.
25.
GelmanAndrewHwangJessicaVehtariAki (2014), “Understanding Predictive Information Criteria for Bayesian Models,” Statistics and Computing, 24 (6), 997–1016.
26.
GelmanAndrewRubinDonald B. (1992), “Inference from Iterative Simulation using Multiple Sequences,” Statistical Science, 7 (4), 457–72.
27.
GermannFrankEbbesPeterGrewalRajdeep (2015), “The Chief Marketing Officer Matters!” Journal of Marketing, 79 (3), 1–22.
HaugtvedtCurtis P.SchumannDavid W.SchneierWendy L.WarrenWendy L. (1994), “Advertising Repetition and Variation Strategies: Implications for Understanding Attitude Strength,” Journal of Consumer Research, 21 (1), 176–89.
30.
HobanPaul R.BucklinRandolph E. (2014), “Effects of Internet Display Advertising in the Purchase Funnel: Model-Based Insights from a Randomized Field Experiment,” Journal of Marketing Research, 52 (3), 375–93.
IrwinJulie R.McClellandGary H. (2001), “Misleading Heuristics and Moderated Multiple Regression Models,” Journal of Marketing Research, 38 (1), 100–109.
33.
KamakuraWagner A.WedelMichelAgrawalJagadish (1994), “Concomitant Variable Latent Class Models for Conjoint Analysis,” International Journal of Research in Marketing, 11 (5), 451–64.
34.
KeeleLukeKellyNathan J. (2005), “Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables,” Political Analysis, 14 (2), 186–205.
35.
KireyevPavelPauwelsKoenGuptaSunil (2013), “Do Display Ads Influence Search? Attribution and Dynamics in Online Advertising,” International Journal of Research in Marketing, 33 (3), 475–49.
36.
LambrechtAnjaTuckerCatherine (2013), “When Does Retargeting Work? Information Specificity in Online Advertising,” Journal of Marketing Research, 50 (5), 561–76.
37.
LewisRandall (2017), “Worn-Out or Just Getting Started? The Impact of Frequency in Online Display Advertising,” working paper.
38.
LewisRandall A.RaoJustin M. (2015), “The Unfavorable Economics of Measuring the Returns to Advertising,” Quarterly Journal of Economics, 130 (4), 1941–73.
39.
LewisRandall A.ReileyDavid H. (2014), “Online Ads and Offline Sales: Measuring the Effect of Retail Advertising Via a Controlled Experiment on Yahoo!” Quantitative Marketing and Economics, 12 (3), 235–66.
40.
LiHongshuang (Alice)KannanP.K. (2014), “Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment,” Journal of Marketing Research, 51 (1), 40–56.
41.
LimpertEckhardStahelWerner A.AbbtMarkus (2001), “Log-Normal Distributions across the Sciences: Keys and Clues on the Charms of Statistics,” Bioscience, 51 (5), 341–52.
42.
LittleJohn D. (1979), “Aggregate Advertising Models: The State of the Art,” Operations Research, 27 (4), 629–67.
43.
MahajanVijayMullerEitan (1986), “Advertising Pulsing Policies for Generating Awareness for New Products,” Marketing Science, 5 (2), 89–106.
44.
ManchandaPuneetDubéJean-PierreGohKhim YongChintaguntaPradeep K. (2006), “The Effect of Banner Advertising on Internet Purchasing,” Journal of Marketing Research, 43 (1), 98–108.
45.
MoeWendy W.FaderPeter S. (2004), “Dynamic Conversion Behavior at E-Commerce Sites,” Management Science, 50 (3), 326–35.
NaikPrasad A.MantralaMurali K.SawyerAlan G. (1998), “Planning Media Schedules in the Presence of Dynamic Advertising Quality,” Marketing Science, 17 (3), 214–35.
48.
NerloveMarcArrowKenneth J. (1962), “Optimal Advertising Policy Under Dynamic Conditions,” Economica, 29 (114), 129–42.
PechmannCorneliaStewartDavid W. (1988), “Advertising Repetition: A Critical Review of Wearin and Wearout,” Current Issues and Research in Advertising, 11 (1/2), 285–329.
51.
RaoAmbar G.MillerPeter B. (1975), “Advertising-Sales Response Functions,” Journal of Advertising Research, 15, 7–15.
52.
RaoVithala R. (1972), “Alternative Econometric Models of Sales-Advertising Relationships,” Journal of Marketing Research, 9 (2), 177–81.
53.
RutzOliver J.BucklinRandolph E. (2012), “Does Banner Advertising Affect Browsing for Brands? Clickstream Choice Model Says Yes, for Some,” Quantitative Marketing and Economics, 10 (2), 231–57.
54.
SahniNavdeep S. (2016), “Advertising Spillovers: Evidence from Online Field Experiments and Implications for Returns on Advertising,” Journal of Marketing Research, 53 (4), 459–78.
55.
SchwartzEric M.BradlowEric T.FaderPeter S. (2017), “Customer Acquisition Via Display Advertising Using Multi-Armed Bandit Experiments,” Marketing Science, 36 (4), 500–522.
56.
ShaoXuhuiLiLexin (2011), “Data-Driven Multi-Touch Attribution Models,” in proceedings for KDD ‘11. New York: Association for Computing Machinery, 258–64.
57.
SiddarthSivaramakrishnanChattopadhyayAmitava (1998), “To Zap or Not to Zap: A Study of the Determinants of Channel Switching During Commercials,” Marketing Science, 17 (2), 124–38.
58.
SpiegelhalterDavid J.BestNicola G.CarlinBradley P.LindeAngelika Van Der (2002), “Bayesian Measures of Model Complexity and Fit,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64 (4), 583–639.
59.
SteenkampJan-Benedict E.M.NijsVincent R.HanssensDominique M.DekimpeMarnik G. (2005), “Competitive Reactions to Advertising and Promotion Attacks,” Marketing Science, 24 (1), 35–54.
60.
TellisGerard J. (1988), “Advertising Exposure, Loyalty, and Brand Purchase: A Two-Stage Model of Choice,” Journal of Marketing Research, 25 (2), 134–44.
61.
TeruiNobuhikoBanMasataka (2008), “Modeling Heterogeneous Effective Advertising Stock Using Single-Source Data,” Quantitative Marketing and Economics, 6, 415–38.
VakratsasDemetriosFeinbergFred M.BassFrank M.KalyanaramGurumurthy (2004), “The Shape of Advertising Response Functions Revisited: A Model of Dynamic Probabilistic Thresholds,” Marketing Science, 23 (1), 109–19.
64.
Van DiepenMerelDonkersBasFransesPhilip H. (2009), “Does Irritation Induced by Charitable Direct Mailings Reduce Donations?” International Journal of Research in Marketing, 26, 180–88.
65.
Van HeerdeHarald J.GijsbrechtsElsPauwelsKoen (2015), “Fanning the Flames? How Media Coverage of a Price War Affects Retailers, Consumers, and Investors,” Journal of Marketing Research, 52 (5), 674–93.
WatanabeSumio (2010), “Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory,” Journal of Machine Learning Research, 11, 3571–94.
68.
YaverogluIdilDonthuNaveen (2008), “Advertising Repetition and Placement Issues in Online Environments,” Journal of Advertising, 37 (2), 31–44.
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