原型
广告
频道(广播)
业务
计算机科学
电信
艺术
文学类
作者
J. Jason Bell,Felipe Thomaz,Andrew T. Stephen
标识
DOI:10.1177/00222429241302808
摘要
Prior research on advertising media mixes has mostly focused on single channels (e.g., television), pairwise cross-elasticities, or budget optimization within single campaigns. This is detached from practice where (1) marketers decide between an increasingly large number of media channels, (2) media plans involve complex combinations of channels, and (3) marketers manage complementarities among many channels. The authors use latent class analysis to uncover tendencies in media allocations. Latent classes account for nonrandom selection of channels into campaigns, capture pairwise and higher-order interactions between channels, and allow for meaningful interpretation. The authors describe the most common media channel archetypes and estimate their relationship to the effectiveness of a set advertising campaigns on common brand-related performance metrics. They use a dataset of 1,083 advertising campaigns from around the world run between 2008 and 2019. While they find no single media mix that consistently correlates with high performance across all metrics, clear high-performing patterns emerge for specific metrics. The authors find that traditional channels (TV, outdoor) often appear together with digital channels (Facebook, YouTube) in high-performing campaigns. Additionally, current marketing practices appear suboptimal, with simple strategies predicted to improve lifts by 50% or more.
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