Historically, advertising allocation decisions have operated mostly at a macro level, comprising determination of the total budget followed by apportionment among media channels. Owing to the rapid and sustained rise of digital media, an additional decision now operates at a micro level, namely, which specific customers to target with advertising. In the macro case, optimal control theory provides a powerful framework for firm profit maximization, specifically allowing for ad response, cost per medium, and discount rate, all in the presence of multiple competing brands. However, optimal control theory has never been applied to the situation of microtargeting individual customers. Consequently, in this study the author shows how optimal control theory can be adapted for application to individual customers. The author estimates a multinomial logit model with individual-specific advertising response parameters. In turn, these parameters are used to determine the optimal number of exposures each customer should receive for each advertising medium. An empirical example demonstrates that using the optimal microtargeting method improves firm profits over existing ad scheduling methods by between 150% and 183%.
AilawadiKusum L.GedenkKarenNeslinScott A. (1999), “Heterogeneity and Purchase Event Feedback in Choice Models: An Empirical Analysis with Implications for Model Building,” International Journal of Research in Marketing, 16 (3), 177–98.
2.
AsimAnsariMelaCarlNeslinScott A. (2008), “Customer Channel Migration,” Journal of Marketing Research, 45 (1), 60–76.
3.
AssaelHenryPoltrackDavid (1993), “Using Single Source Data to Select TV Programs,” Journal of Advertising Research, 33 (1), 48–56.
4.
BumbacaFederico (Rico)MisraSanjogRossiPeter E. (2020), “Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models,” Journal of Marketing Research, 57 (6), 999–1018.
5.
ChaeInyoungBrunoHernanFeinbergFred M. (2019), “Wearout or Weariness? Measuring Potential Negative Consequences of Online Ad Volume and Placement on Website Visits,” Journal of Marketing Research, 56 (1), 57–75.
6.
ChintaguntaPradeep K.VilcassimNaufel J. (1992), “An Empirical Investigation of Advertising Strategies in a Dynamic Duopoly,” Management Science, 38 (9), 1230–44.
7.
ChintaguntaPradeep K.VilcassimNaufel J (1994), “Marketing Investment Decisions in a Dynamic Duopoly: A Model and Empirical Analysis,” International Journal of Research in Marketing, 1 (3), 287–306.
8.
ChoiJae YoungShinJungwooLeeJongsu (2013), “Strategic Demand Forecast for Tablet PC Market Using Bayesian Mixed Logit Model and Market Share Simulations,” Behaviour and Information Technology, 32 (11), 1177–90.
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.
11.
DanaherPeter J.DanaherTracey S.SmithMichael S.Laoizo-MayaRuben (2020), “Advertising Effectiveness for Multiple Retailer-Brands in a Multimedia and Multichannel Environment,” Journal of Marketing Research, 57 (3), 445–67.
12.
DanaherPeter J.RustRoland T. (1994), “Determining the Optimal Level of Media Spending,” Journal of Advertising Research, 34 (1), 28–34.
13.
DanaherPeter J.van HeerdeHarald J. (2018), “Delusion in Attribution: Caveats in Using Attribution for Media Budget Allocation,” Journal of Marketing Research, 55 (5), 667–85.
14.
DinnerIsaacvan HeerdeHarald J.NeslinScott A. (2014), “Driving Online and Offline Sales: The Cross-Channel Effects of Traditional, Online Display, and Paid Search Advertising,” Journal of Marketing Research, 51 (5), 527–45.
15.
DorfmanRobertSteinerPeter O. (1954), “Optimal Advertising and Optimal Quality,” American Economic Review, 44 (5), 826–36.
16.
EbbesPeterPapiesDominikvan HeerdeHarald J. (2011), “The Sense and Nonsense of Holdout Sample Validation in the Presence of Endogeneity,” Marketing Science, 30 (6), 1115–22.
17.
EricksonGary M. (1985), “A Model of Advertising Competition,” Journal of Marketing Research, 22 (3), 297–304.
18.
EricksonGary M. (1992), “Empirical Analysis of Closed-Loop Duopoly Advertising Strategies,” Management Science, 38 (2), 1732–49.
19.
FruchterGila E. (1999), “The Many-Player Advertising Game,” Management Science, 45 (11), 1609–11.
20.
FruchterGila E.KalishShlomo (1997), “Closed-Loop Advertising Strategies in a Duopoly,” Management Science, 43 (1), 54–63.
21.
FruchterGila E.KalishShlomo (1998), “Dynamic Promotional Budgeting and Media Allocation,” European Journal of Operations Research, 111 (1), 15–27.
22.
GordonBrett R.ZettelmeyerFlorianBhargavaNehaChapskyDan (2019), “A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook,” Marketing Science, 38 (2), 193–225.
23.
HughesArthur (2010), “The Lifetime Value of a Catalog Customer,” (accessed April 8, 2020), http://www.dbmarketing.com/2010/03/the-lifetime-value-of-a-catalog-customer.
24.
JohnsonJustin P. (2013), “Targeted Advertising and Advertising Avoidance,” RAND Journal of Economics, 44 (1), 128–44.
25.
KamienMorton I.SchwartzNancy L. (1991), Dynamic Optimization: The Calculus of Variations and Optimal Control in Economics and Management. Elsevier Science Publishing Company.
26.
KolsariciCerenVakratsasDemetriosNaikPrasad A. (2020), “The Anatomy of the Advertising Budget Decision: How Analytics and Heuristics Drive Sales Performance,” Journal of Marketing Research, 57 (3), 468–88.
27.
KumarAshishBezawadaRamRishikaRishikaJanakiramanRamkumarKannanP.K. (2016) “From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior,” Journal of Marketing, 80 (1), 7–25.
28.
LambrechtAnjaTuckerCatherine (2013), “When Does Retargeting Work? Information Specificity in Online Advertising,” Journal of Marketing Research, 50 (4), 561–76.
29.
LeoneRobert P. (1995), “Generalizing What Is Known About Temporal Aggregation and Advertising Carryover,” Marketing Science, 14 (3, Part 2), G141−50.
30.
LodishLeonard M.AbrahamMagidKalmensonStuartLivelsbergerJeanneLubetkinBethRichardsonBruce, et al. (1995), “How T.V. Advertising Works: A Meta-Analysis of 389 Real World Split Cable TV Advertising Experiments,” Journal of Marketing Research, 37 (2), 125–39.
31.
MarkTanyaBullaJanNirajRakeshBullaIngoSchwarzwallerWolfgang (2019), “Catalogue as a Tool for Reinforcing Habits: Empirical Evidence from a Multichannel Retailer,”International Journal of Research in Marketing, 36 (4), 528–41.
MorganNeil A.RegoLopo L. (2009), “Brand Portfolio Strategy and Firm Performance,” Journal of Marketing, 73 (1), 59–74.
34.
NaikPrasad A.RamanKalyan (2003), “Understanding the Impact of Synergy in Multimedia Communications,” Journal of Marketing Research, 40 (4), 375–88.
35.
NaikPrasad A.RamanKalyanWinerRussell S. (2005), “Planning Marketing-Mix Strategies in the Presence of Interaction Effects,” Marketing Science, 24 (1), 25–34.
36.
ParkSunghoGuptaSachin (2012), “Handling Endogenous Regressors by Joint Estimation Using Copulas,” Marketing Science, 31 (4), 567–86.
37.
PetrinAmilTrainKenneth (2010), “A Control Function Approach to Endogeneity in Consumer Choice Models,” Journal of Marketing Research, 47 (1), 3–13.
38.
RossiPeter E.AllenbyGreg M.McCullochRobert (1996), “The Value of Purchase History Data in Target Marketing,” Marketing Science, 15 (4), 321–40.
39.
RossiPeter E.AllenbyGreg M.McCullochRobert (2005), Bayesian Statistics and Marketing. John Wiley & Sons.
40.
RoyRishinChintaguntaPradeep K.HaldarSudeep (1996), “A Framework for Investigating Habits, ‘The Hand of the Past,’ and Heterogeneity in Dynamic Brand Choice,” Marketing Science, 15 (3), 280–99.
41.
SchweidelDavid A.KnoxGeorge (2013), “Incorporating Direct Marketing Activity into Latent Attrition Models,” Marketing Science, 32 (3), 471–87.
TerzaJoseph V.BasuAnirbanRathouzPaul J. (2008), “Two-Stage Residual Inclusion Estimation: Addressing Endogeneity in Health Econometric Modeling,” Journal of Health Economics, 27 (3), 531–43.
45.
TrainKenneth E. (2009), Discrete Choice Models with Simulation, 2nd ed.Cambridge University Press.
46.
ZhangXi (Alan)KumarV.CosgunerKoray (2017), “Dynamically Managing a Profitable Email Marketing Program,” Journal of Marketing Research, 54 (6), 851–66.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
