TY - GEN
T1 - TV Ad Events and Digital Search
T2 - 2019 IEEE International Conference on Big Data, Big Data 2019
AU - Hill, Shawndra
AU - Colas, Anthony
AU - Schwartz, H. Andrew
AU - Burtch, Gordon
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Prior research has shown that TV content affects what people do on the web, particularly in the minutes after a TV ad airs, when online searches for the advertised product spike. To study this, researchers have typically focused on the total volume of search queries that include any and all keywords associated with the brands and products in TV ads. We argue that a granular consideration of search queries would be beneficial for two reasons. First, focusing on relevant keywords reduces measurement error, which can hinder identification of a significant effect from TV ads. Second, by grouping queries into themes based on semantic similarity, marketers can, for example, explore consumer intent in searches, or whether response manifests in queries with objective 'value'. Our nuanced proposed approach considers query activity more broadly. Leveraging data from Bing and iSpotTV for 12 product campaigns, we first demonstrate that the outcome measure (i.e., response variable) can significantly influence conclusions about whether an ad has influenced search behavior. Second, we present a theme-based difference-in-differences method to determine the associations between TV ads and individual queries and query groups. We do this by first exploring the effects of TV ads on distinct, individual queries, and then aggregating queries into themes reflecting customer intentions, instead of aggregating all queries under a product or brand name umbrella. Our approach has implications for researchers who can improve measurement in search response; for marketers who can evaluate whether and how marketing messages resonate with consumers; and for sponsored search advertisers who can determine which keywords and queries they should bid on in the moments after their TV ads air. In addition, our method can be applied outside of the advertising context to understand how user generated content is impacted by events in general. Finally, we have developed a standalone tool that has been deployed to by Marketers internal to our company to generate reports to assess the success of their TV ad campaigns and has been deployed to our advertising customers by the Bing Ads Sales team to provide business intelligence1.1The dashboards in production can be demonstrated at the conference.
AB - Prior research has shown that TV content affects what people do on the web, particularly in the minutes after a TV ad airs, when online searches for the advertised product spike. To study this, researchers have typically focused on the total volume of search queries that include any and all keywords associated with the brands and products in TV ads. We argue that a granular consideration of search queries would be beneficial for two reasons. First, focusing on relevant keywords reduces measurement error, which can hinder identification of a significant effect from TV ads. Second, by grouping queries into themes based on semantic similarity, marketers can, for example, explore consumer intent in searches, or whether response manifests in queries with objective 'value'. Our nuanced proposed approach considers query activity more broadly. Leveraging data from Bing and iSpotTV for 12 product campaigns, we first demonstrate that the outcome measure (i.e., response variable) can significantly influence conclusions about whether an ad has influenced search behavior. Second, we present a theme-based difference-in-differences method to determine the associations between TV ads and individual queries and query groups. We do this by first exploring the effects of TV ads on distinct, individual queries, and then aggregating queries into themes reflecting customer intentions, instead of aggregating all queries under a product or brand name umbrella. Our approach has implications for researchers who can improve measurement in search response; for marketers who can evaluate whether and how marketing messages resonate with consumers; and for sponsored search advertisers who can determine which keywords and queries they should bid on in the moments after their TV ads air. In addition, our method can be applied outside of the advertising context to understand how user generated content is impacted by events in general. Finally, we have developed a standalone tool that has been deployed to by Marketers internal to our company to generate reports to assess the success of their TV ad campaigns and has been deployed to our advertising customers by the Bing Ads Sales team to provide business intelligence1.1The dashboards in production can be demonstrated at the conference.
KW - Causal Inference
KW - Clustering
KW - Diff in Diff
UR - https://www.scopus.com/pages/publications/85081407030
U2 - 10.1109/BigData47090.2019.9006405
DO - 10.1109/BigData47090.2019.9006405
M3 - Conference contribution
AN - SCOPUS:85081407030
T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
SP - 3518
EP - 3528
BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
A2 - Baru, Chaitanya
A2 - Huan, Jun
A2 - Khan, Latifur
A2 - Hu, Xiaohua Tony
A2 - Ak, Ronay
A2 - Tian, Yuanyuan
A2 - Barga, Roger
A2 - Zaniolo, Carlo
A2 - Lee, Kisung
A2 - Ye, Yanfang Fanny
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2019 through 12 December 2019
ER -