A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution

References

• Anderl E, Becker I, Von Wangenheim F, Schumann JH (2016) Mapping the customer journey: Lessons learned from graph-based online attribution modeling. Internat. J. Res. Marketing 33(3):457–474.Crossref, Google Scholar
• Bacry E, Bompaire M, Gaïffas S, Muzy J-F (2020) Sparse and low-rank multivariate Hawkes processes. J. Machine Learn. Res. 21(50):1–32.Google Scholar
• Berman R (2018) Beyond the last touch: Attribution in online advertising. Marketing Sci. 37(5):685–853.Link, Google Scholar
• Bowman D, Narayandas D (2001) Managing customer-initiated contacts with manufacturers: The impact on share of category requirements and word-of-mouth behavior. J. Marketing Res. 38(3):281–297.Crossref, Google Scholar
• Breuer R, Brettel M, Engelen A (2011) Incorporating long-term effects in determining the effectiveness of different types of online advertising. Marketing Lett. 22(4):327–340.Crossref, Google Scholar
• Cai B, Zhang J, Guan Y (2022) Latent network structure learning from high-dimensional multivariate point processes. J. Amer. Statist. Assoc. 119(545):95–108.Crossref, Google Scholar
• Chen S, Shojaie A, Shea-Brown E, Witten D (2017) The multivariate Hawkes process in high dimensions: Beyond mutual excitation. Preprint, submitted July 16, https://arxiv.org/abs/1707.04928.Google Scholar
• Chen X, Jasin S, Shi C, eds. (2022) The Elements of Joint Learning and Optimization in Operations Management, Springer Series in Supply Chain Management (Springer, Cham, Switzerland).Crossref, Google Scholar
• Dalessandro B, Perlich C, Stitelman O, Provost F (2012) Causally motivated attribution for online advertising. Proc. Sixth Internat. Workshop Data Mining Online Advertising Internet Economy (Association for Computing Machinery, New York), 1–9.Google Scholar
• Daley DJ, Vere-Jones D (2003) An Introduction to the Theory of Point Processes. Volume I: Elementary Theory and Methods, Probability and Its Applications (Springer, New York).Google Scholar
• Danaher PJ, van Heerde HJ (2018) Delusion in attribution: Caveats in using attribution for multimedia budget allocation. J. Marketing Res. 55(5):667–685.Crossref, Google Scholar
• De Haan E, Wiesel T, Pauwels K (2016) The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework. Internat. J. Res. Marketing 33(3):491–507.Crossref, Google Scholar
• Didelez V (2008) Graphical models for marked point processes based on local independence. J. Roy. Statist. Soc. Ser. B Statist. Methodology 70(1):245–264.Crossref, Google Scholar
• Eichler M (2012) Graphical modelling of multivariate time series. Probab. Theory Related Fields 153(1):233–268.Crossref, Google Scholar
• Gaur J, Bharti K (2020) Attribution modelling in marketing: Literature review and research agenda. Acad. Marketing Stud. J. 24(4):1–21.Google Scholar
• Granger CW (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3):424–438.Crossref, Google Scholar
• Granger CW (1980) Testing for causality: A personal viewpoint. J. Econom. Dynam. Control 2(1):329–352.Crossref, Google Scholar
• Granger CW (1988) Some recent development in a concept of causality. J. Econometrics 39(1–2):199–211.Crossref, Google Scholar
• Gunawardana A, Meek C, Xu P (2011) A model for temporal dependencies in event streams. Shawe-Taylor J, Zemel R, Bartlett P, Pereira F, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 24 (Curran Associates, Inc., Red Hook, NY), 1962–1970.Google Scholar
• Guo X, Hu A, Xu R, Zhang J (2018) Consistency and computation of regularized MLEs for multivariate Hawkes processes. Preprint, submitted October 6, https://arxiv.org/abs/1810.02955.Google Scholar
• Hansen NR, Reynaud-Bouret P, Rivoirard V (2015) Lasso and probabilistic inequalities for multivariate point processes. Bernoulli 21(1):83–143.Crossref, Google Scholar
• Hawkes AG (1971) Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(1):83–90.Crossref, Google Scholar
• Ji W, Wang X, Zhang D (2016) A probabilistic multi-touch attribution model for online advertising. Proc. 25th ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 1373–1382.Google Scholar
• Kannan P, Reinartz W, Verhoef PC (2016) The path to purchase and attribution modeling: Introduction to special section. Internat. J. Res. Marketing 33(3):449–456.Crossref, Google Scholar
• Kireyev P, Pauwels K, Gupta S (2016) Do display ads influence search? Attribution and dynamics in online advertising. Internat. J. Res. Marketing 33(3):475–490.Crossref, Google Scholar
• Kumar S, Gupta G, Prasad R, Chatterjee A, Vig L, Shroff G (2020) Camta: Causal attention model for multi-touch attribution. 2020 Internat. Conf. Data Mining Workshops (ICDMW) (IEEE, Piscataway, NJ), 79–86.Google Scholar
• Lewis PW, Shedler GS (1979) Simulation of nonhomogeneous Poisson processes by thinning. Naval Res. Logist. Quart. 26(3):403–413.Crossref, Google Scholar
• Li B, Chun H, Zhao H (2014) On an additive semigraphoid model for statistical networks with application to pathway analysis. J. Amer. Statist. Assoc. 109(507):1188–1204.Crossref, Google Scholar
• Li H, Kannan P (2014) Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. J. Marketing Res. 51(1):40–56.Crossref, Google Scholar
• Li N, Arava SK, Dong C, Yan Z, Pani A (2018) Deep neural net with attention for multi-channel multitouch attribution. Preprint, submitted September 6, https://arxiv.org/abs/1809.02230.Google Scholar
• Lin DY, Ying Z (1994) Semiparametric analysis of the additive risk model. Biometrika 81(1):61–71.Crossref, Google Scholar
• Lin W, Lv J (2013) High-dimensional sparse additive hazards regression. J. Amer. Statist. Assoc. 108(501):247–264.Crossref, Google Scholar
• Marketing Science Institute (2016) Marketing Science Institute research priorities 2016–2018. Report, Marketing Science Institute, Cambridge, MA.Google Scholar
• Marketing Science Institute (2018) Marketing Science Institute research priorities 2018–2020. Report, Marketing Science Institute, Cambridge, MA.Google Scholar
• Marketing Science Institute (2020) Marketing Science Institute research priorities 2020–2022. Report, Marketing Science Institute, Cambridge, MA.Google Scholar
• Mei H, Eisner JM (2017) The neural Hawkes process: A neurally self-modulating multivariate point process. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., Red Hook, NY), 6757–6767.Google Scholar
• Naik PA, Raman K (2003) Understanding the impact of synergy in multimedia communications. J. Marketing Res. 40(4):375–388.Crossref, Google Scholar
• Ogata Y (1981) “On Lewis” simulation method for point processes. IEEE Trans. Inform. Theory 27(1):23–31.Crossref, Google Scholar
• Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536.Crossref, Google Scholar
• Sapp S, Vaver J, Shi M, Bathia N (2016) DASS: Digital advertising system simulation. Technical report, Google Inc., Mountain View, CA.Google Scholar
• Shao X, Li L (2011) Data-driven multi-touch attribution models. Proc. 17th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 258–264.Google Scholar
• Shapley LS (1953) A value for n-person games. Kuhn HW, Tucker AW, eds. Contributions to the Theory of Games II (Princeton University Press, Princeton, NJ), 307–317.Crossref, Google Scholar
• Sims CA (1972) Money, income, and causality. Amer. Econom. Rev. 62(4):540–552.Google Scholar
• Singal R, Besbes O, Desir A, Goyal V, Iyengar G (2022) Shapley meets uniform: An axiomatic framework for attribution in online advertising. Management Sci. 68(10):7457–7479.Link, Google Scholar
• Tao J, Li B, Xue L (2024) An additive graphical model for discrete data. J. Amer. Statist. Assoc. 119(545):368–381.Crossref, Google Scholar
• Wiesel T, Pauwels K, Arts J (2011) Practice prize paper—Marketing’s profit impact: Quantifying online and off-line funnel progression. Marketing Sci. 30(4):604–611.Link, Google Scholar
• Xu H, Farajtabar M, Zha H (2016) Learning Granger causality for Hawkes processes. Internat. Conf. Machine Learn. (PMLR, New York), 1717–1726.Google Scholar
• Xu L, Duan JA, Whinston A (2014) Path to purchase: A mutually exciting point process model for online advertising and conversion. Management Sci. 60(6):1392–1412.Link, Google Scholar
• Yang S, Ghose A (2010) Analyzing the relationship between organic and sponsored search advertising: Positive, negative, or zero interdependence? Marketing Sci. 29(4):602–623.Link, Google Scholar
• Yu X, Shanmugam K, Bhattacharjya D, Gao T, Subramanian D, Xue L (2020) Hawkesian graphical event models. Internat. Conf. Probabilistic Graphical Models (PMLR, New York), 569–580.Google Scholar
• Zhang Y, Bradlow ET, Small DS (2013) New measures of clumpiness for incidence data. J. Appl. Statist. 40(11):2533–2548.Crossref, Google Scholar
• Zhang Y, Wei Y, Ren J (2014) Multi-touch attribution in online advertising with survival theory. 2014 IEEE Internat. Conf. Data Mining (IEEE, Piscataway, NJ), 687–696.Google Scholar
• Zhao K, Mahboobi SH, Bagheri SR (2019) Revenue-based attribution modeling for online advertising. Internat. J. Marketing Res. 61(2):195–209.Google Scholar